From 6355500ac849f9efe1393ae4d98e35561d4673c8 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Fri, 17 Apr 2026 02:35:00 +0530 Subject: [PATCH 01/36] added python file translated from ENIGMA_PNInspiral.c in lalsuite --- esigmapy/python_codes/ESIGMA_PNInspiral.py | 2778 ++++++++++++++++++++ 1 file changed, 2778 insertions(+) create mode 100644 esigmapy/python_codes/ESIGMA_PNInspiral.py diff --git a/esigmapy/python_codes/ESIGMA_PNInspiral.py b/esigmapy/python_codes/ESIGMA_PNInspiral.py new file mode 100644 index 0000000..ee7056c --- /dev/null +++ b/esigmapy/python_codes/ESIGMA_PNInspiral.py @@ -0,0 +1,2778 @@ +""" +ESIGMA_PNInspiral.py +==================== +Python translation of ENIGMA_PNInspiral.c + +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 -> math.pi +* M_PI2 -> math.pi**2 (macro used in original) +* M_PI3 -> math.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 math +from math import pi, sqrt, log, cos, sin + +# ------ Constants -------------------------------------------------- +EULER_GAMMA = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant +LOG2 = 0.693147180559945309417232121458 +LOG3 = 1.09861228866810969139524523692 +LOG4 = 1.38629436111989061883446424292 +LOG5 = 1.60943791243410037460075933323 +REAL8_FAIL_NAN = float('nan') + +# ------ 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 * 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 * 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 * 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 + e2 = e * e + pre = 192.0 * sqrt(ef) / (985.0 * e2) + return pre * (sqrt(ef) * phi_e(e) - phi_e_tilde(e)) + + +def psi_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 18816.0 / (55691.0 * e2 * sqrt(ef)) + pf2 = 16382.0 * sqrt(ef) / (55691.0 * e2) + b1 = sqrt(ef) * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) + b2 = sqrt(ef) * psi_e(e) - psi_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def zed_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 924.0 / (19067.0 * e2 * sqrt(ef)) + pf2 = 12243.0 * sqrt(ef) / (76268.0 * e2) + b1 = -ef * sqrt(ef) * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) + b2 = sqrt(ef) * zed_e(e) - zed_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def kappa_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + den = 769.0 / 96.0 - 3059665.0 * LOG2 / 700566.0 + 8190315.0 * LOG3 / 1868176.0 + pf = sqrt(ef) / e2 + b1 = sqrt(ef) * 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 = 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 = 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)""" + 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 * sqrt(ef) + e0 = 2.0 * eta * 96.0 / 15.0 + return (num / den) if e else e0 + +## --------- 1PN terms ------------ + +def x_dot_1pn(e: float, eta: float) -> float: + """Eq. (A27)""" + 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 * sqrt(ef) + num = -eta * (t0 + t2 + t4 + t6) + e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) + return (num / den) if e else e0 + +## --------- 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + M = m1 + m2 + if e: + 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 * ef**5 * M**4) + else: + 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) + + +def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: + """Eq. (A28)""" + pre = eta * x**6 * sqrt(x) + if e: + return pre * 256.0 * pi * phi_e(e) / 5.0 + else: + return pre * 256.0 * pi / 5.0 + +## --------- 2PN terms ------------ + +def x_dot_2pn(e: float, eta: float) -> float: + """Eq. (A29)""" + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + + ef = 1.0 - e2 + eta2 = eta * eta + + # Small-e limit + e0_lim = eta * (68206.0 / 2835.0 + + 27322.0 * eta / 315.0 + + 1888.0 * eta2 / 45.0) + + if e: return e0_lim + else: + sqrt_ef = 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 + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + ef_sqrt = sqrt(ef) + ef_55 = ef**4 * ef * ef_sqrt + M2 = (m1 + m2)**2 + M4 = M2**2 + pre = 64.0 * eta / 5.0 + + if e: + 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) + else: + return pre * ( + ((1 + 80*kappa1)*m1*m1*S1z*S1z + + 158*m1*m2*S1z*S2z + + (1 + 80*kappa2)*m2*m2*S2z*S2z) + ) / (16.0 * M2) + +## --------- 2.5PN terms ------------ + +def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: + """See Huerta et al.""" + ef = 1.0 - e*e + ef2 = ef * ef + e2 = e * e + pre = eta * x**7 * sqrt(x) + + if e: + b1 = 256.0 * pi * phi_e(e) / ef + b2 = (-17599.0 * pi * psi_n(e) / 35.0 + - 2268.0 * eta * pi * zed_n(e) / 5.0 + - 788.0 * e2 * pi * phi_e_rad(e) / ef2) + return pre * (b1 + 2.0*b2/3.0) + else: + b1e0 = 256.0 * pi + b2e0 = -17599.0 * pi / 35.0 - 2268.0 * eta * 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + ef = 1.0 - e2 + ef_sqrt = sqrt(ef) + M2 = (m1 + m2)**2 + M4 = M2 * M2 + M6 = M4 * M2 + pre = 64.0 * eta / 5.0 + + if e: + 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 + ) + +## --------- 3PN terms ------------ + +def x_dot_hereditary_3(e: float, eta: float, x: float) -> float: + """See Huerta et al.""" + pi2 = pi * pi + pre = eta * x**8 + + if e: + x_3_term = (64.0 * + (-116761.0 * kappa_e(e) + + (19600.0*pi2 - 59920.0*EULER_GAMMA - 59920.0*LOG4 - 89880.0*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*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.)""" + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + e10 = e8 * e2 + + ef = 1.0 - e2 + eta2 = eta * eta + pi2 = pi * 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 * log(x) / 525.0 + ) + + if abs(e)<1e-12: return e0_lim + + sqrt_ef = 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 - log((1.0 / ef) + (1.0 / sqrt_ef)) - 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + ef = 1.0 - e2 + ef_sqrt = sqrt(ef) + ef_65 = ef**6 * ef_sqrt + M2 = (m1 + m2)**2 + M4 = M2 * M2 + pre = 64.0 * eta / 5.0 + + if e: + return ( + -9.645061728395062e-6 * + m1 * m2 * 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 * 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 + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + + e_fact = 1.0 - e2 + e_fact_sqrt = sqrt(e_fact) + + e_fact_65 = (e_fact**6) * e_fact_sqrt + + M = m1 + m2 + M2 = M * M + M4 = M2 * M2 + M6 = M4 * 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) + ) + + # --- 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 e 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 * ( + 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 e 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 e else val_e0 + + +def x_dot_3_5_pn(e: float, eta: float) -> float: + """3.5PN non-spinning""" + val_e0 = (64.0 * eta * 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 e else val_e0 + +## --------- 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*pi**2/126.0 - + eta*(1472377 + 928158*eta)*pi**2/16128.0 + + 127751*LOG2/1470.0 - 47385*LOG3/1568.0 + + eta*(-850042*LOG2/2205.0 + 47385*LOG3/392.0) + + (124741 - 582500*eta)*log(x)/8820.0 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if e 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 * + 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 e else val_e0 + + +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 e 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*pi**3/12.0 - + pi * (700*eta*(3098001198 + eta*(525268513 + 289286988*eta)) + + 145786798080*euler + + 3*(-343801320119 + 97191198720*LOG2)) / 2.2353408e9 - + 3424*pi*log(x)/105.0 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if e 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 = pi * 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 * 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 * log(x)) / 8820.0 + ) + + + 0.071630658436214 * ( + 12774.514362657092 + - 39782.929590804066 * eta + + 346.61235705608044 * eta2 + + 2.068222621184919 * eta3 + + eta4 + - 4169.536804308797 * eta * 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 not e: + return 0.0 + num = -e * eta * (121.0*e2 + 304.0) + den = 15.0 * ef**2 * sqrt(ef) + return num / den + +## ---------- 1PN ------------- + +def e_dot_1pn(e: float, eta: float) -> float: + """Eq. (A32)""" + if not e: + 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 * 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 not e: + return 0.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + M = m1 + m2 + pre = e / (ef**4) * (m1*m2) / (90.0 * M**4) + 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 not e: + return 0.0 + pre = 32.0 * eta * e * x**7 / 5.0 + return pre * (-985.0 * pi * phi_e_rad(e) / 48.0) + +## ---------- 2PN ------------- + +def e_dot_2pn(e: float, eta: float) -> float: + e_2_pn = 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) + + if e != 0.0: + pre_factor = -e * eta / (e_fact**4 * math.sqrt(e_fact)) + e_2_pn = pre_factor * ( + zero_term + + e_2_term + + e_4_term + + e_6_term + + math.sqrt(e_fact) * (zero_rt + e_2_rt + e_4_rt) + ) + else: + e_2_pn = 0.0 + + 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 not e: + return 0.0 + kappa1 = 1.0; kappa2 = 1.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + ef_45 = ef**4 * 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 e == 0.0: + 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 = math.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 not e: + return 0.0 + pre = 32.0 * eta * e * x**4 * x**4 * sqrt(x) / 5.0 + a2 = (55691.0 * psi_e_rad(e) / 1344.0 + 19067.0 * eta * zed_e_rad(e) / 126.0) * pi + return pre * a2 + +## --------- 3PN ------------- + +def e_rad_hereditary_3(e: float, eta: float, x: float) -> float: + if not e: + return 0.0 + pre = 32.0 * eta * e * x**7 / 5.0 + x32 = x * 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*pi**2/3.0 - 1712.0*EULER_GAMMA/105.0 - 1712.0*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 e == 0.0: + 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 = math.sqrt(e_fact) + + pi_pow_2 = math.pi * math.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 + ) + * math.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 not e: + return 0.0 + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + ef_sqrt = sqrt(ef) + ef_55 = ef**4 * ef * ef_sqrt + M2 = (m1+m2)**2 + M4 = M2*M2 + return ( + e * m1 * m2 * 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) + + +import math + +def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: + if e == 0.0: + 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 = math.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 * 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 = 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 * sqrt(ef) * ef3) + eta2 = eta*eta + pi2 = pi*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 + 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 * cos(u) - 1.0 + +## --------- 0PN ------------- + +def phi_dot_0pn(e: float, eta: float, u: float) -> float: + """Eq. (A11)""" + cf = cosu_factor(e, u) + return 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 - cos(u))) / (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 not e: + return 0.0 + cf = -1.0 + e * cos(u) + return (2*e*(m1*S1z + m2*S2z)*(e - 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 = 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 * sqrt(ef) * ef * cf5) + return pre * (t0+t2+t4+t6+c1+c2+c3 + 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 not e: + 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 - cos(u)) + ) / ((1-e**2)**1.5 * (m1+m2)**2 * (-1+e*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 = math.sqrt(e_fact) + + M = m1 + m2 + M_fact_2 = M * M + M_fact_4 = M_fact_2 * M_fact_2 + + cos_u = math.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 = 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 = 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 * 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 + + 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 = math.sqrt(e_fact) + M_fact_2 = (m1 + m2) ** 2 + M_fact_4 = M_fact_2 * M_fact_2 + cos_u = math.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 * 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*cos(u)) / ef + b2 = (-18.0 + 2.0*eta - e*(6.0 - 7.0*eta)*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))*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*cos(u)) + d1 = 6.0*ef**2 + n2 = (72.0*(-4.0+7.0*eta) + + 36.0*sqrt(ef)*(-5.0+2.0*eta)*(2.0+e*cos(u)) + + ef*(72.0+30.0*eta+8.0*eta2 + e*(-72.0+7*(33.0-5.0*eta)*eta)*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*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=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*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))*cos(u))) + ) / (6.0 * (-1+e2)**3 * M4) + +## ---------- 3PN -------------------- + +def rel_sep_3pn(e: float, u: float, eta: float) -> float: + pi_pow_2 = pi * 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 + ) * cos(u) + + term4_sqrt = 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 + ) * math.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 = 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 = 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 + + +# ============ 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 - sqrt(1.0 - 4.0*eta)) / (2.0*eta) + + +# ============ Kepler equation solvers ========================================= + +def mikkola_finder(eccentricity: float, mean_anomaly: float) -> float: + """Mikkola's method for the eccentric anomaly.""" + while mean_anomaly > pi: + mean_anomaly -= 2*pi + while mean_anomaly < -pi: + mean_anomaly += 2*pi + + sgn = 1.0 if mean_anomaly >= 0.0 else -1.0 + mean_anomaly *= sgn + + a = (1.0 - eccentricity) / (4.0*eccentricity + 0.5) + b = 0.5 * mean_anomaly / (4.0*eccentricity + 0.5) + + sgn_b = 1.0 if b >= 0.0 else -1.0 + z = (b + sgn_b * sqrt(b*b + a**3))**(1.0/3.0) + s = z - a / z + s = s - 0.078 * s**5 / (1.0 + eccentricity) + + ecc_anomaly = mean_anomaly + eccentricity * (3.0*s - 4.0*s**3) + return sgn * ecc_anomaly + + +def pn_kepler_equation(eta: float, x: float, e: float, l: float) -> float: + """3PN-accurate Kepler equation solver via Newton's method.""" + tol = 1.0e-12 + + if l == 0.0: + return 0.0 + + while l > pi: + l -= 2*pi + while l < -pi: + l += 2*pi + + neg = False + if l < 0.0: + l = -l + neg = True + + newt_thresh = tol * abs(1.0 - e) + u = mikkola_finder(e, l) + + if e > 0.8 and l < pi/3.0: + trial = l / abs(1.0 - e) + if trial*trial > 6.0*abs(1.0 - e): + if l < pi: + trial = (6.0*l)**(1.0/3.0) + u = trial + + if e < 1.0: + err = u - e*sin(u) - l + while abs(err) > newt_thresh: + u -= err / (1.0 - e*cos(u)) + err = u - e*sin(u) - l + + return -u if neg else u + + +# ─── 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: + """dx/dt dispatcher.""" + x5 = x**5 + xsqx = x * sqrt(x) + + if radiation_pn_order == 0 or radiation_pn_order == 1: + return x_dot_0pn(e, eta) * x5 + + elif radiation_pn_order == 2: + return (x_dot_0pn(e, eta) + x_dot_1pn(e, eta)*x) * x5 + + elif radiation_pn_order == 3: + return (x_dot_0pn(e, eta) + + x_dot_1pn(e, eta)*x + + x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx) * x5 + \ + x_dot_hereditary_1_5(e, eta, x) + + elif radiation_pn_order == 4: + return (x_dot_0pn(e, eta) + + x_dot_1pn(e, eta)*x + + x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + + x_dot_2pn(e, eta, x)*x*x + + x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x) * x5 + \ + x_dot_hereditary_1_5(e, eta, x) + + elif radiation_pn_order == 5: + return (x_dot_0pn(e, eta) + + x_dot_1pn(e, eta)*x + + x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + + x_dot_2pn(e, eta, x)*x*x + + x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x + + x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z)*x*x*sqrt(x)) * x5 + \ + x_dot_hereditary_1_5(e, eta, x) + x_dot_hereditary_2_5(e, eta, x) + + elif radiation_pn_order == 6: + return (x_dot_0pn(e, eta) + + x_dot_1pn(e, eta)*x + + x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + + x_dot_2pn(e, eta, x)*x*x + + x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x + + x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z)*x*x*sqrt(x) + + x_dot_3pn(e, eta, x)*x**3 + + x_dot_3pn_SO(e, eta, m1, m2, S1z, S2z)*x**3 + + x_dot_3pn_SS(e, eta, m1, m2, S1z, S2z)*x**3) * x5 + \ + x_dot_hereditary_1_5(e, eta, x) + \ + x_dot_hereditary_2_5(e, eta, x) + \ + x_dot_hereditary_3(e, eta, x) + + else: # >= 7: full 3.5PN + optional 4PN/4.5PN + xdot = (x_dot_0pn(e, eta) + + x_dot_1pn(e, eta)*x + + x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + + x_dot_2pn(e, eta, x)*x*x + + x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x + + x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z)*x*x*sqrt(x) + + x_dot_3pn(e, eta, x)*x**3 + + x_dot_3pn_SO(e, eta, m1, m2, S1z, S2z)*x**3 + + x_dot_3pn_SS(e, eta, m1, m2, S1z, S2z)*x**3 + + x_dot_3_5pnSO(e, eta, m1, m2, S1z, S2z)*x**3*sqrt(x) + + x_dot_3_5_pn(e, eta)*x**3*sqrt(x) + + x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z)*x**3*sqrt(x) + + x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z)*x**3*sqrt(x) + + (x_dot_4pn(e, eta, x) + + x_dot_4pnSO(e, eta, m1, m2, S1z, S2z) + + x_dot_4pnSS(e, eta, m1, m2, S1z, S2z))*x**4 + + x_dot_4_5_pn(e, eta, x)*x**4*sqrt(x)) * x5 + \ + x_dot_hereditary_1_5(e, eta, x) + \ + x_dot_hereditary_2_5(e, eta, x) + \ + x_dot_hereditary_3(e, eta, x) + return xdot + + +def de_dt(radiation_pn_order: int, + eta: float, m1: float, m2: float, S1z: float, S2z: float, + x: float, e: float) -> float: + """de/dt dispatcher.""" + x4 = x**4 + xsqx = x * sqrt(x) + + if radiation_pn_order == 0 or radiation_pn_order == 1: + return e_dot_0pn(e, eta) * x4 + + elif radiation_pn_order == 2: + return (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x) * x4 + + elif radiation_pn_order == 3: + return (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x + + e_dot_1_5pn_SO(e, m1, m2, S1z, S2z)*xsqx) * x4 + \ + e_rad_hereditary_1_5(e, eta, x) + + elif radiation_pn_order == 4: + return (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x + + e_dot_2pn(e, eta)*x*x + + e_dot_1_5pn_SO(e, m1, m2, S1z, S2z)*xsqx + + e_dot_2pn_SS(e, m1, m2, S1z, S2z)*x*x) * x4 + \ + e_rad_hereditary_1_5(e, eta, x) + + else: # >= 5: full 3.5PN + edot = (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x + + e_dot_2pn(e, eta)*x*x + + e_dot_3pn(e, eta, x)*x**3 + + e_dot_3_5pn(e, eta)*x**3*sqrt(x) + + e_dot_1_5pn_SO(e, m1, m2, S1z, S2z)*xsqx + + e_dot_2pn_SS(e, m1, m2, S1z, S2z)*x*x + + e_dot_2_5pn_SO(e, m1, m2, S1z, S2z)*x*x*sqrt(x) + + (e_dot_3pn_SO(e, m1, m2, S1z, S2z) + + e_dot_3pn_SS(e, m1, m2, S1z, S2z))*x**3) * x4 + \ + e_rad_hereditary_1_5(e, eta, x) + \ + e_rad_hereditary_2_5(e, eta, x) + \ + e_rad_hereditary_3(e, eta, x) + return edot + + +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 = 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 = 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 + ) + + +# ─── Main ODE function (replaces eccentric_x_model_odes) ───────────────────── + +def eccentric_x_model_odes(t: float, + y: list, + eta: float, m1: float, m2: float, + S1z: float, S2z: float, + radiation_pn_order: int) -> list: + """ + ODE right-hand side for the ENIGMA PN inspiral model. + + Parameters + ---------- + t : float + Current time (unused, but required by ODE solvers). + y : list or array [x, e, l, phi] + State vector: + x — PN parameter (v/c)^2 + e — eccentricity + l — mean anomaly + phi — orbital phase + eta, m1, m2, S1z, S2z : float + Binary parameters. + radiation_pn_order : int + PN order selector (0-12). + + Returns + ------- + dydt : list [dx/dt, de/dt, dl/dt, dphi/dt] + + Notes + ----- + Use with scipy.integrate.solve_ivp or odeint: + from scipy.integrate import solve_ivp + import functools + rhs = functools.partial(eccentric_x_model_odes, + eta=eta, m1=m1, m2=m2, + S1z=S1z, S2z=S2z, + radiation_pn_order=radiation_pn_order) + sol = solve_ivp(rhs, [t0, t_end], y0, ...) + """ + x, e, l, phi = y + + u = pn_kepler_equation(eta, x, e, l) + + dxdt_val = dx_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) + dedt_val = de_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) + dldt_val = dl_dt(eta, m1, m2, S1z, S2z, x, e) + + if e: + dphidt_val = dphi_dt(u, eta, m1, m2, S1z, S2z, x, e) + else: + dphidt_val = x * sqrt(x) # zero-eccentricity limit + + return [dxdt_val, dedt_val, dldt_val, dphidt_val] \ No newline at end of file From fd6acb31ff73c219fad8f045f9a0dc83a67f3524 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Fri, 17 Apr 2026 04:16:35 +0530 Subject: [PATCH 02/36] Updated the inspiral code --- esigmapy/python_codes/ESIGMA_PNInspiral.py | 446 ++++++++++++--------- 1 file changed, 248 insertions(+), 198 deletions(-) diff --git a/esigmapy/python_codes/ESIGMA_PNInspiral.py b/esigmapy/python_codes/ESIGMA_PNInspiral.py index ee7056c..781e933 100644 --- a/esigmapy/python_codes/ESIGMA_PNInspiral.py +++ b/esigmapy/python_codes/ESIGMA_PNInspiral.py @@ -2511,181 +2511,150 @@ def SmallMassRatio(eta: float) -> float: return (1.0 - 2.0*eta - sqrt(1.0 - 4.0*eta)) / (2.0*eta) -# ============ Kepler equation solvers ========================================= +# ================== ODE right-hand-side dispatchers ========================== -def mikkola_finder(eccentricity: float, mean_anomaly: float) -> float: - """Mikkola's method for the eccentric anomaly.""" - while mean_anomaly > pi: - mean_anomaly -= 2*pi - while mean_anomaly < -pi: - mean_anomaly += 2*pi +def dx_dt(radiation_pn_order: int, + eta: float, m1: float, m2: float, S1z: float, S2z: float, + x: float, e: float) -> float: - sgn = 1.0 if mean_anomaly >= 0.0 else -1.0 - mean_anomaly *= sgn + x2 = x * x + x3 = x2 * x + xsqx = x * math.sqrt(x) + x5 = x**5 - a = (1.0 - eccentricity) / (4.0*eccentricity + 0.5) - b = 0.5 * mean_anomaly / (4.0*eccentricity + 0.5) + # ------------------------- + # Instantaneous PN part + # ------------------------- + inst = x_dot_0pn(e, eta) - sgn_b = 1.0 if b >= 0.0 else -1.0 - z = (b + sgn_b * sqrt(b*b + a**3))**(1.0/3.0) - s = z - a / z - s = s - 0.078 * s**5 / (1.0 + eccentricity) + if radiation_pn_order >= 2: + inst += x_dot_1pn(e, eta) * x - ecc_anomaly = mean_anomaly + eccentricity * (3.0*s - 4.0*s**3) - return sgn * ecc_anomaly + 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 -def pn_kepler_equation(eta: float, x: float, e: float, l: float) -> float: - """3PN-accurate Kepler equation solver via Newton's method.""" - tol = 1.0e-12 + if radiation_pn_order >= 5: + inst += x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z) * x2 * math.sqrt(x) - if l == 0.0: - return 0.0 + 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 - while l > pi: - l -= 2*pi - while l < -pi: - l += 2*pi + if radiation_pn_order >= 7: + inst += x_dot_3_5pnSO(e, eta, m1, m2, S1z, S2z) * x3 * math.sqrt(x) + inst += x_dot_3_5_pn(e, eta) * x3 * math.sqrt(x) + inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * math.sqrt(x) + inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * math.sqrt(x) + + 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) - neg = False - if l < 0.0: - l = -l - neg = True + if radiation_pn_order >= 9: + inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * math.sqrt(x) - newt_thresh = tol * abs(1.0 - e) - u = mikkola_finder(e, l) + if radiation_pn_order >= 10: + inst += 0.0 #dxdt_5pn(x, eta), not implemented - if e > 0.8 and l < pi/3.0: - trial = l / abs(1.0 - e) - if trial*trial > 6.0*abs(1.0 - e): - if l < pi: - trial = (6.0*l)**(1.0/3.0) - u = trial + if radiation_pn_order >= 11: + inst += 0.0 #dxdt_5_5pn(x, eta), not implemented - if e < 1.0: - err = u - e*sin(u) - l - while abs(err) > newt_thresh: - u -= err / (1.0 - e*cos(u)) - err = u - e*sin(u) - l + if radiation_pn_order >= 12: + inst += 0.0 #dxdt_6pn(x, eta), not implemented - return -u if neg else u + # 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) -# ─── ODE right-hand-side dispatchers ───────────────────────────────────────── + if radiation_pn_order >= 5: + result += x_dot_hereditary_2_5(e, eta, x) -def dx_dt(radiation_pn_order: int, - eta: float, m1: float, m2: float, S1z: float, S2z: float, - x: float, e: float) -> float: - """dx/dt dispatcher.""" - x5 = x**5 - xsqx = x * sqrt(x) - - if radiation_pn_order == 0 or radiation_pn_order == 1: - return x_dot_0pn(e, eta) * x5 - - elif radiation_pn_order == 2: - return (x_dot_0pn(e, eta) + x_dot_1pn(e, eta)*x) * x5 - - elif radiation_pn_order == 3: - return (x_dot_0pn(e, eta) + - x_dot_1pn(e, eta)*x + - x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx) * x5 + \ - x_dot_hereditary_1_5(e, eta, x) - - elif radiation_pn_order == 4: - return (x_dot_0pn(e, eta) + - x_dot_1pn(e, eta)*x + - x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + - x_dot_2pn(e, eta, x)*x*x + - x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x) * x5 + \ - x_dot_hereditary_1_5(e, eta, x) - - elif radiation_pn_order == 5: - return (x_dot_0pn(e, eta) + - x_dot_1pn(e, eta)*x + - x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + - x_dot_2pn(e, eta, x)*x*x + - x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x + - x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z)*x*x*sqrt(x)) * x5 + \ - x_dot_hereditary_1_5(e, eta, x) + x_dot_hereditary_2_5(e, eta, x) - - elif radiation_pn_order == 6: - return (x_dot_0pn(e, eta) + - x_dot_1pn(e, eta)*x + - x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + - x_dot_2pn(e, eta, x)*x*x + - x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x + - x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z)*x*x*sqrt(x) + - x_dot_3pn(e, eta, x)*x**3 + - x_dot_3pn_SO(e, eta, m1, m2, S1z, S2z)*x**3 + - x_dot_3pn_SS(e, eta, m1, m2, S1z, S2z)*x**3) * x5 + \ - x_dot_hereditary_1_5(e, eta, x) + \ - x_dot_hereditary_2_5(e, eta, x) + \ - x_dot_hereditary_3(e, eta, x) - - else: # >= 7: full 3.5PN + optional 4PN/4.5PN - xdot = (x_dot_0pn(e, eta) + - x_dot_1pn(e, eta)*x + - x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z)*xsqx + - x_dot_2pn(e, eta, x)*x*x + - x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z)*x*x + - x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z)*x*x*sqrt(x) + - x_dot_3pn(e, eta, x)*x**3 + - x_dot_3pn_SO(e, eta, m1, m2, S1z, S2z)*x**3 + - x_dot_3pn_SS(e, eta, m1, m2, S1z, S2z)*x**3 + - x_dot_3_5pnSO(e, eta, m1, m2, S1z, S2z)*x**3*sqrt(x) + - x_dot_3_5_pn(e, eta)*x**3*sqrt(x) + - x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z)*x**3*sqrt(x) + - x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z)*x**3*sqrt(x) + - (x_dot_4pn(e, eta, x) + - x_dot_4pnSO(e, eta, m1, m2, S1z, S2z) + - x_dot_4pnSS(e, eta, m1, m2, S1z, S2z))*x**4 + - x_dot_4_5_pn(e, eta, x)*x**4*sqrt(x)) * x5 + \ - x_dot_hereditary_1_5(e, eta, x) + \ - x_dot_hereditary_2_5(e, eta, x) + \ - x_dot_hereditary_3(e, eta, x) - return xdot + 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: - """de/dt dispatcher.""" - x4 = x**4 - xsqx = x * sqrt(x) - - if radiation_pn_order == 0 or radiation_pn_order == 1: - return e_dot_0pn(e, eta) * x4 - - elif radiation_pn_order == 2: - return (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x) * x4 - - elif radiation_pn_order == 3: - return (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x + - e_dot_1_5pn_SO(e, m1, m2, S1z, S2z)*xsqx) * x4 + \ - e_rad_hereditary_1_5(e, eta, x) - - elif radiation_pn_order == 4: - return (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x + - e_dot_2pn(e, eta)*x*x + - e_dot_1_5pn_SO(e, m1, m2, S1z, S2z)*xsqx + - e_dot_2pn_SS(e, m1, m2, S1z, S2z)*x*x) * x4 + \ - e_rad_hereditary_1_5(e, eta, x) - - else: # >= 5: full 3.5PN - edot = (e_dot_0pn(e, eta) + e_dot_1pn(e, eta)*x + - e_dot_2pn(e, eta)*x*x + - e_dot_3pn(e, eta, x)*x**3 + - e_dot_3_5pn(e, eta)*x**3*sqrt(x) + - e_dot_1_5pn_SO(e, m1, m2, S1z, S2z)*xsqx + - e_dot_2pn_SS(e, m1, m2, S1z, S2z)*x*x + - e_dot_2_5pn_SO(e, m1, m2, S1z, S2z)*x*x*sqrt(x) + - (e_dot_3pn_SO(e, m1, m2, S1z, S2z) + - e_dot_3pn_SS(e, m1, m2, S1z, S2z))*x**3) * x4 + \ - e_rad_hereditary_1_5(e, eta, x) + \ - e_rad_hereditary_2_5(e, eta, x) + \ - e_rad_hereditary_3(e, eta, x) - return edot + + x2 = x * x + x3 = x2 * x + x4 = x2 * x2 + xsqx = x * sqrt(x) + + # ------------------------- + # 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) * x2 * sqrt(x) + + if radiation_pn_order >= 6: + inst += e_dot_3pn(e, eta, x) * x3 + inst += e_dot_3_5pn(e, eta) * x3 * sqrt(x) + 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 += 0.0 # placeholder for higher order terms, not implemented + + 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, @@ -2722,57 +2691,138 @@ def dphi_dt(u: float, eta: float, m1: float, m2: float, S1z: float, S2z: float, ) -# ─── Main ODE function (replaces eccentric_x_model_odes) ───────────────────── +# ================== 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 > math.pi: + mean_anomaly -= 2 * math.pi + while mean_anomaly < -math.pi: + mean_anomaly += 2 * math.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) + # Using your existing pow1_3 function + z = pow1_3(b + sgn_b * math.sqrt(b * b + a * a * a)) + + # Mikkola Eq. (9c) + s = z - a / z + + # add the correction given in Mikkola Eq. (7) + # Using your existing pow5 function + s = s - 0.078 * pow5(s) / (1.0 + eccentricity) + + # finally Mikkola Eq. (8) gives u + # Using your existing pow3 function + ecc_anomaly = mean_anomaly + eccentricity * (3.0 * s - 4.0 * pow3(s)) + + # correct the sign of u + return ecc_anomaly * sgn_mean_anomaly + -def eccentric_x_model_odes(t: float, - y: list, - eta: float, m1: float, m2: float, - S1z: float, S2z: float, - radiation_pn_order: int) -> list: +def pn_kepler_equation(eta, x, e, l): """ - ODE right-hand side for the ENIGMA PN inspiral model. - - Parameters - ---------- - t : float - Current time (unused, but required by ODE solvers). - y : list or array [x, e, l, phi] - State vector: - x — PN parameter (v/c)^2 - e — eccentricity - l — mean anomaly - phi — orbital phase - eta, m1, m2, S1z, S2z : float - Binary parameters. - radiation_pn_order : int - PN order selector (0-12). - - Returns - ------- - dydt : list [dx/dt, de/dt, dl/dt, dphi/dt] - - Notes - ----- - Use with scipy.integrate.solve_ivp or odeint: - from scipy.integrate import solve_ivp - import functools - rhs = functools.partial(eccentric_x_model_odes, - eta=eta, m1=m1, m2=m2, - S1z=S1z, S2z=S2z, - radiation_pn_order=radiation_pn_order) - sol = solve_ivp(rhs, [t0, t_end], y0, ...) + 3PN accurate Kepler equation solver using Newton's method. """ - x, e, l, phi = y + # (void)eta, (void)x equivalents are unnecessary in Python + mean_anom_negative = False + u = 0.0 + tol = 1.0e-12 + + if l == 0: + return u + # range reduction of the l + while l > math.pi: + l -= 2 * math.pi + while l < -math.pi: + l += 2 * math.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 < math.pi / 3.0): + trial = l / abs(1.0 - e) + if trial * trial > 6.0 * abs(1.0 - e): + # cubic term is dominant + if l < math.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 * math.sin(u) - l + while abs(newt_err) > newt_thresh: + u -= newt_err / (1.0 - e * math.cos(u)) + newt_err = u - e * math.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 + # In Python, we usually access attributes directly + 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) - dxdt_val = dx_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) - dedt_val = de_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) - dldt_val = dl_dt(eta, m1, m2, S1z, S2z, x, e) + dydt = [0.0, 0.0, 0.0, 0.0] - if e: - dphidt_val = dphi_dt(u, eta, m1, m2, S1z, S2z, x, e) + if e != 0: + 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: - dphidt_val = x * sqrt(x) # zero-eccentricity limit + # 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 * math.sqrt(x) + + # Check for NaN (equivalent to XLAL_REAL8_FAIL_NAN) + if math.isnan(dydt[0]) or math.isnan(dydt[1]): + # You can raise an exception or return a specific error code + raise ValueError("ODE derivative calculated as NaN") + + return dydt - return [dxdt_val, dedt_val, dldt_val, dphidt_val] \ No newline at end of file From 91feb1ce4bfefebbdcfa2ac4555d0a13f2748839 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Fri, 17 Apr 2026 11:12:48 +0530 Subject: [PATCH 03/36] Clean up --- esigmapy/python_codes/ESIGMA_PNInspiral.py | 44 +++++++++++----------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/esigmapy/python_codes/ESIGMA_PNInspiral.py b/esigmapy/python_codes/ESIGMA_PNInspiral.py index 781e933..9283b87 100644 --- a/esigmapy/python_codes/ESIGMA_PNInspiral.py +++ b/esigmapy/python_codes/ESIGMA_PNInspiral.py @@ -1089,13 +1089,13 @@ def e_dot_2pn(e: float, eta: float) -> float: e_4_rt = e_pow_4 * (565.0 / 6.0 - 113.0 * eta / 3.0) if e != 0.0: - pre_factor = -e * eta / (e_fact**4 * math.sqrt(e_fact)) + pre_factor = -e * eta / (e_fact**4 * sqrt(e_fact)) e_2_pn = pre_factor * ( zero_term + e_2_term + e_4_term + e_6_term - + math.sqrt(e_fact) * (zero_rt + e_2_rt + e_4_rt) + + sqrt(e_fact) * (zero_rt + e_2_rt + e_4_rt) ) else: e_2_pn = 0.0 @@ -1136,7 +1136,7 @@ def e_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float) -> fl e_fact = 1.0 - e_2 e_fact_2 = e_fact * e_fact - e_fact_sqrt = math.sqrt(e_fact) + e_fact_sqrt = sqrt(e_fact) e_fact_55 = e_fact_2 * e_fact_2 * e_fact * e_fact_sqrt M = m1 + m2 @@ -1240,7 +1240,7 @@ def e_dot_3pn(e: float, eta: float, x: float) -> float: e_pow_8 = e_pow_6 * e_pow_2 e_fact = 1.0 - e_pow_2 - sqrt_e_fact = math.sqrt(e_fact) + sqrt_e_fact = sqrt(e_fact) pi_pow_2 = math.pi * math.pi @@ -1366,7 +1366,7 @@ def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> floa e_fact = 1.0 - e_2 e_fact_2 = e_fact * e_fact - e_fact_sqrt = math.sqrt(e_fact) + e_fact_sqrt = sqrt(e_fact) e_fact_55 = e_fact_2 * e_fact_2 * e_fact * e_fact_sqrt M = m1 + m2 @@ -1683,13 +1683,13 @@ def phi_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float, u: e_6 = e_4 * e_2 e_fact = 1.0 - e_2 - e_fact_sqrt = math.sqrt(e_fact) + e_fact_sqrt = sqrt(e_fact) M = m1 + m2 M_fact_2 = M * M M_fact_4 = M_fact_2 * M_fact_2 - cos_u = math.cos(u) + cos_u = cos(u) numerator = ( # (m1 - m2)(m1 + m2)(...) @@ -1932,10 +1932,10 @@ def phi_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float, u: fl e_4 = e_2 * e_2 e_6 = e_4 * e_2 e_fact = 1.0 - e_2 - e_fact_sqrt = math.sqrt(e_fact) + e_fact_sqrt = sqrt(e_fact) M_fact_2 = (m1 + m2) ** 2 M_fact_4 = M_fact_2 * M_fact_2 - cos_u = math.cos(u) + cos_u = cos(u) cos_u_2 = cos_u * cos_u cos_u_3 = cos_u_2 * cos_u @@ -2359,7 +2359,7 @@ def pow7_2(x): + 464940.0 * pi_pow_2 + 17875620.0) * eta - 417440.0 ) * e - ) * math.cos(u) + ) * cos(u) + 116235.0 * pi_pow_2 * eta + 1814400.0 ) @@ -2519,7 +2519,7 @@ def dx_dt(radiation_pn_order: int, x2 = x * x x3 = x2 * x - xsqx = x * math.sqrt(x) + xsqx = x * sqrt(x) x5 = x**5 # ------------------------- @@ -2538,7 +2538,7 @@ def dx_dt(radiation_pn_order: int, 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) * x2 * math.sqrt(x) + inst += x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z) * x2 * sqrt(x) if radiation_pn_order >= 6: inst += x_dot_3pn(e, eta, x) * x3 @@ -2546,10 +2546,10 @@ def dx_dt(radiation_pn_order: int, 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) * x3 * math.sqrt(x) - inst += x_dot_3_5_pn(e, eta) * x3 * math.sqrt(x) - inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * math.sqrt(x) - inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * math.sqrt(x) + inst += x_dot_3_5pnSO(e, eta, m1, m2, S1z, S2z) * x3 * sqrt(x) + inst += x_dot_3_5_pn(e, eta) * x3 * sqrt(x) + inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * sqrt(x) + inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * sqrt(x) if radiation_pn_order >= 8: inst += ( @@ -2559,7 +2559,7 @@ def dx_dt(radiation_pn_order: int, ) * (x2 * x2) if radiation_pn_order >= 9: - inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * math.sqrt(x) + inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * sqrt(x) if radiation_pn_order >= 10: inst += 0.0 #dxdt_5pn(x, eta), not implemented @@ -2720,7 +2720,7 @@ def mikkola_finder(eccentricity, mean_anomaly): # Mikkola Eq. (9b) # Using your existing pow1_3 function - z = pow1_3(b + sgn_b * math.sqrt(b * b + a * a * a)) + z = pow1_3(b + sgn_b * sqrt(b * b + a * a * a)) # Mikkola Eq. (9c) s = z - a / z @@ -2776,10 +2776,10 @@ def pn_kepler_equation(eta, x, e, l): # iterate using Newton's method to get solution if e < 1.0: - newt_err = u - e * math.sin(u) - l + newt_err = u - e * sin(u) - l while abs(newt_err) > newt_thresh: - u -= newt_err / (1.0 - e * math.cos(u)) - newt_err = u - e * math.sin(u) - l + u -= newt_err / (1.0 - e * cos(u)) + newt_err = u - e * sin(u) - l return -u if mean_anom_negative else u @@ -2817,7 +2817,7 @@ def eccentric_x_model_odes(t, y, params): 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 * math.sqrt(x) + dydt[3] = x * sqrt(x) # Check for NaN (equivalent to XLAL_REAL8_FAIL_NAN) if math.isnan(dydt[0]) or math.isnan(dydt[1]): From f9e7dcad5a68062b1110e9d8e39ecfeace573691 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Fri, 17 Apr 2026 19:22:28 +0530 Subject: [PATCH 04/36] Added ESIGMA_GOterms python version, incomplete --- esigmapy/python_codes/ESIGMA_GOterms.py | 628 +++++++++++++++++++++ esigmapy/python_codes/ESIGMA_PNInspiral.py | 7 +- 2 files changed, 629 insertions(+), 6 deletions(-) create mode 100644 esigmapy/python_codes/ESIGMA_GOterms.py diff --git a/esigmapy/python_codes/ESIGMA_GOterms.py b/esigmapy/python_codes/ESIGMA_GOterms.py new file mode 100644 index 0000000..ea4638b --- /dev/null +++ b/esigmapy/python_codes/ESIGMA_GOterms.py @@ -0,0 +1,628 @@ +import math +import cmath +from typing import Any + +def Complex(real: float, imag: float) -> complex: + """Helper function to mimic the C-style Complex constructor.""" + return complex(real, imag) + +# All the hGO_l_m functions contain the 3PN non-spinning general orbit (GO) terms +# from Mishra et al. arXiv:1501.07096 and 3.5PN quasi-circular spinning +# corrections as given in Henry et al, arXiv:2209.00374v2 + +# The (2,2) mode has newly computed 4PN non-spinning quasi-circular piece from +# arXiv:2304.11185 + +# H22 + +def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, + PhiDOT: float, vpnorder: int, S1z: float, S2z: float, + x: float, params: Any) -> complex: + + # Note: In Python, `params` should be an object (like a dataclass or SimpleNamespace) + # so that attributes can be accessed via dot notation (e.g., params.PhiDOT2). + + # For black holes kappa and lambda is 1 + kappa1 = 1.0 + kappa2 = 1.0 + lambda1 = 1.0 + lambda2 = 1.0 + + delta = math.sqrt(1 - 4 * Nu) + + 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 + + # Mapping M_PI2 to pi^2 (standard in PN GW theory for this coefficient) + M_PI = math.pi + M_PI2 = math.pi ** 2 + + if vpnorder == 0: + return (mass / r + params.PhiDOT2 * params.r2 + + Complex(0, 2) * PhiDOT * r * rDOT - params.rDOT2) + + elif vpnorder == 2: + return ((21 * params.Mtot2 * (-10 + Nu) - + 27 * (-1 + 3 * Nu) * params.r2 * combination_b * combination_a3 + + mass * r * + ((11 + 156 * Nu) * params.PhiDOT2 * params.r2 + + Complex(0, 10) * (5 + 27 * Nu) * PhiDOT * r * rDOT - + 3 * (15 + 32 * Nu) * params.rDOT2)) / + (42. * params.r2)) + + elif vpnorder == 3: + return ((params.Mtot2 * (Complex(0, -1) * rDOT * + ((3 + 3 * delta - 8 * Nu) * S1z + + (3 - 3 * delta - 8 * Nu) * S2z) + + PhiDOT * r * + ((-3 - 3 * delta + 5 * Nu) * S1z + + (-3 + 3 * delta + 5 * Nu) * S2z))) / + (3. * params.r2)) + # (<--This is the general orbit term) + # (This is the quasi-circular limit of the general orbit term-->) + # - ((-4 * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x2p5) / 3.) + + # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - Nu * (S1z + S2z)) * params.x2p5) / 3.) + # (<--This is Quentins quasi-circular term) + + elif vpnorder == 4: + return ((6 * params.Mtot3 * (3028 + 1267 * Nu + 158 * params.eta2) + + 9 * (83 - 589 * Nu + 1111 * params.eta2) * params.r3 * + combination_b2 * combination_a4 + + params.Mtot2 * r * + ((-11891 - 36575 * Nu + 13133 * params.eta2) * params.PhiDOT2 * + params.r2 + + Complex(0, 8) * (-773 - 3767 * Nu + 2852 * params.eta2) * + PhiDOT * r * rDOT - + 6 * (-619 + 2789 * Nu + 934 * params.eta2) * params.rDOT2) - + 3 * mass * params.r2 * + (2 * (-835 - 19 * Nu + 2995 * params.eta2) * params.PhiDOT4 * + params.r4 + + Complex(0, 6) * (-433 - 721 * Nu + 1703 * params.eta2) * + params.PhiDOT3 * params.r3 * rDOT + + 6 * (-33 + 1014 * Nu + 232 * params.eta2) * params.PhiDOT2 * + params.r2 * params.rDOT2 + + Complex(0, 4) * (-863 + 1462 * Nu + 2954 * params.eta2) * + PhiDOT * r * params.rDOT3 - + 3 * (-557 + 664 * Nu + 1712 * params.eta2) * params.rDOT4)) / + (1512. * params.r3) + + (3 * params.Mtot3 * + (S1z * (4 * Nu * S2z + (1 + delta - 2 * Nu) * S1z * kappa1) - + (-1 + delta + 2 * Nu) * params.S2z2 * kappa2)) / + (4. * params.r3)) + # This is where circular limit terms added and subtracted + # - ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + + # ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + + elif vpnorder == 5: + return ((params.Mtot2 * Nu * + (2 * mass * (Complex(0, -702) * PhiDOT * r + rDOT) + + 3 * r * + (Complex(0, -316) * params.PhiDOT3 * params.r3 - + 847 * params.PhiDOT2 * params.r2 * rDOT + + Complex(0, 184) * PhiDOT * r * params.rDOT2 - + 122 * params.rDOT3))) / + (105. * params.r3) + # Henry et al. QC spin terms + # + ((2*(56*delta*Nu*(-S1z + S2z) + 101*Nu*(S1z + S2z) + 132*params.eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) + + + # Henry et al. ecc spin terms + ((params.Mtot2 * + (mass * + ((238 + delta * (238 - 141 * Nu) + Nu * (-181 + 474 * Nu)) * + PhiDOT * r * S1z + + Complex(0, 8) * + (55 + delta * (55 - 19 * Nu) + + 2 * Nu * (-50 + 43 * Nu)) * + rDOT * S1z + + (238 + delta * (-238 + 141 * Nu) + Nu * (-181 + 474 * Nu)) * + PhiDOT * r * S2z + + Complex(0, 8) * + (55 + delta * (-55 + 19 * Nu) + + 2 * Nu * (-50 + 43 * Nu)) * + rDOT * S2z) - + r * (PhiDOT * r * params.rDOT2 * + (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * Nu + + 188 * params.eta2) * + S1z) + + (18 * (-1 + delta) + 5 * (63 + 55 * delta) * Nu - + 188 * params.eta2) * + S2z) - + Complex(0, 2) * params.rDOT3 * + ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * Nu - + 4 * params.eta2) * + S1z + + (-27 + 27 * delta + 30 * Nu - 42 * delta * Nu - + 4 * params.eta2) * + S2z) + + Complex(0, 2) * params.PhiDOT2 * params.r2 * rDOT * + ((51 + 88 * Nu * (-3 + 5 * Nu) + + delta * (51 + 62 * Nu)) * + S1z + + (51 + 88 * Nu * (-3 + 5 * Nu) - + delta * (51 + 62 * Nu)) * + S2z) + + params.PhiDOT3 * params.r3 * + ((120 * (1 + delta) + (-483 + 83 * delta) * Nu + + 234 * params.eta2) * + S1z + + (120 + 3 * Nu * (-161 + 78 * Nu) - + delta * (120 + 83 * Nu)) * + S2z)))) / + (84. * params.r3))) + + elif vpnorder == 6: + return ((4 * params.Mtot4 * + (-8203424 + 2180250 * params.eta2 + 592600 * params.eta3 + + 15 * Nu * (-5503804 + 142065 * M_PI2)) - + 2700 * (-507 + 6101 * Nu - 25050 * params.eta2 + 34525 * params.eta3) * + params.r4 * combination_b3 * combination_a5 + + params.Mtot3 * r * + (params.PhiDOT2 * + (337510808 - 198882000 * params.eta2 + + 56294600 * params.eta3 + + Nu * (183074880 - 6392925 * M_PI2)) * + params.r2 + + Complex(0, 110) * PhiDOT * + (-5498800 - 785120 * params.eta2 + 909200 * params.eta3 + + 3 * Nu * (-1849216 + 38745 * M_PI2)) * + r * rDOT + + 2 * + (51172744 - 94929000 * params.eta2 - 5092400 * params.eta3 + + 45 * Nu * (2794864 + 142065 * M_PI2)) * + params.rDOT2) - + 20 * params.Mtot2 * params.r2 * + ((-986439 + 1873255 * Nu - 9961400 * params.eta2 + + 6704345 * params.eta3) * + params.PhiDOT4 * params.r4 + + Complex(0, 4) * + (-273687 - 978610 * Nu - 4599055 * params.eta2 + + 2783005 * params.eta3) * + params.PhiDOT3 * params.r3 * rDOT + + (-181719 + 19395325 * Nu + 8237980 * params.eta2 + + 2612735 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT2 + + Complex(0, 8) * + (-234312 + 1541140 * Nu + 1230325 * params.eta2 + + 1828625 * params.eta3) * + PhiDOT * r * params.rDOT3 - + 3 * + (-370268 + 1085140 * Nu + 2004715 * params.eta2 + + 1810425 * params.eta3) * + params.rDOT4) + + 300 * mass * params.r3 * + (4 * + (12203 - 36427 * Nu - 27334 * params.eta2 + + 149187 * params.eta3) * + params.PhiDOT6 * params.r6 + + Complex(0, 2) * + (44093 - 68279 * Nu - 295346 * params.eta2 + + 541693 * params.eta3) * + params.PhiDOT5 * params.r5 * rDOT + + 2 * + (27432 - 202474 * Nu + 247505 * params.eta2 + + 394771 * params.eta3) * + params.PhiDOT4 * params.r4 * params.rDOT2 + + Complex(0, 2) * + (97069 - 383990 * Nu - 8741 * params.eta2 + + 1264800 * params.eta3) * + params.PhiDOT3 * params.r3 * params.rDOT3 + + (-42811 + 53992 * Nu + 309136 * params.eta2 - + 470840 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT4 + + Complex(0, 2) * + (51699 - 252256 * Nu + 131150 * params.eta2 + + 681160 * params.eta3) * + PhiDOT * r * params.rDOT5 - + 3 * + (16743 - 75104 * Nu + 26920 * params.eta2 + + 207200 * params.eta3) * + params.rDOT6)) / + (3.3264e6 * params.r4) + # Henry et al. QC spin terms + # + (((4*(1 + delta)*(-7 + 9*kappa1) - 7*(9 + 17*delta)*Nu - 9*(15 + 7*delta)*kappa1*Nu + 12*(7 - 17*kappa1)*params.eta2)*params.S1z2 + 2*S1z*(Complex(0,-42)*(1 + delta - 2*Nu) - 84*(1 + delta - Nu)*M_PI + Nu*(-271 + 288*Nu)*S2z) + S2z*(12*(7 - 17*kappa2)*params.eta2*S2z + 4*(-1 + delta)*(Complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + Nu*(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 * + (params.Mtot3 * + (2 * mass * + (S1z * (Complex(0, 14) * (1 + delta - 2 * Nu) + + 42 * (1 + delta - 2 * Nu) * Nu * S1z + + kappa1 * + (438 + delta * (438 + 103 * Nu) + + Nu * (-773 + 108 * Nu)) * + S1z) + + 2 * + (Complex(0, -7) * (-1 + delta + 2 * Nu) + + (995 - 192 * Nu) * Nu * S1z) * + S2z - + (42 * Nu * (-1 + delta + 2 * Nu) + + kappa2 * (-438 + (773 - 108 * Nu) * Nu + + delta * (438 + 103 * Nu))) * + params.S2z2) + + r * (params.rDOT2 * + (Complex(0, 56) * (1 + delta - 2 * Nu) * S1z + + (-56 * (1 + delta - 2 * Nu) * Nu + + kappa1 * (291 * (1 + delta) - + 2 * (445 + 154 * delta) * Nu + + 24 * params.eta2)) * + params.S1z2 + + 4 * Nu * (-3 + 44 * Nu) * S1z * S2z + + S2z * (Complex(0, -56) * (-1 + delta + 2 * Nu) + + 56 * Nu * (-1 + delta + 2 * Nu) * S2z + + kappa2 * + (291 - 890 * Nu + 24 * params.eta2 + + delta * (-291 + 308 * Nu)) * + S2z)) + + params.PhiDOT2 * params.r2 * + (Complex(0, 196) * (1 + delta - 2 * Nu) * S1z + + (56 * Nu * (7 + 7 * delta + Nu) + + kappa1 * (-153 * (1 + delta) - + 2 * (62 + 215 * delta) * Nu + + 804 * params.eta2)) * + params.S1z2 + + 8 * (60 - 187 * Nu) * Nu * S1z * S2z + + S2z * (Complex(0, -196) * (-1 + delta + 2 * Nu) + + 56 * Nu * (7 - 7 * delta + Nu) * S2z + + kappa2 * + (-153 + 4 * Nu * (-31 + 201 * Nu) + + delta * (153 + 430 * Nu)) * + S2z)) + + 2 * PhiDOT * r * rDOT * + (Complex(0, -1) * + (117 * (1 + delta) * kappa1 - + 434 * (1 + delta) * Nu + + (-23 + 211 * delta) * kappa1 * Nu + + 2 * (14 - 195 * kappa1) * params.eta2) * + params.S1z2 + + 4 * S1z * + (35 * (1 + delta - 2 * Nu) + + Complex(0, 1) * (9 - 209 * Nu) * Nu * S2z) + + S2z * (-140 * (-1 + delta + 2 * Nu) + + Complex(0, 1) * + (-14 * Nu * (-31 + 31 * delta + 2 * Nu) + + kappa2 * (117 * (-1 + delta) + + (23 + 211 * delta) * Nu + + 390 * params.eta2)) * + S2z))))) / + params.r4)) + # + Henry et al. QC spinning hereditary terms + # (((-8 * M_PI * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x4) / 3.)) + + elif vpnorder == 7: + return ( + # Henry et al QC spin terms + # ((3318*params.eta3*(S1z + S2z) + Nu*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*params.S1z3 - 1008*params.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*params.S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*params.S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*params.eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*params.S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. + # + + # Henry et al. ecc+spin terms + ((params.Mtot2 * + (-3 * mass * r * + (Complex(0, -16) * params.rDOT3 * + (Complex(0, 12) * Nu * (-16703 + 4427 * Nu) + + 35 * Nu * + (4578 + Nu * (-4288 + 765 * Nu) + + delta * (3748 + 802 * Nu)) * + S1z + + 35 * + (delta * (942 - 2 * Nu * (1874 + 401 * Nu)) + + Nu * (4578 + Nu * (-4288 + 765 * Nu))) * + S2z - + 32970 * (S1z + delta * S1z + S2z)) + + 4 * PhiDOT * r * params.rDOT2 * + (-338520 * params.eta3 * (S1z + S2z) + + 48930 * (S1z + delta * S1z + S2z - delta * S2z) + + Nu * (Complex(0, 3420696) - + 35 * (14154 + 21167 * delta) * S1z - + 495390 * S2z + 740845 * delta * S2z) + + params.eta2 * (Complex(0, -612336) + + 245 * (3566 - 1565 * delta) * S1z + + 245 * (3566 + 1565 * delta) * S2z)) + + params.PhiDOT3 * params.r3 * + (2515380 * params.eta3 * (S1z + S2z) - + 5 * Nu * + (Complex(0, 1859936) + + 7 * (82329 + 37061 * delta) * S1z + + 7 * (82329 - 37061 * delta) * S2z) - + 128100 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * params.eta2 * + (Complex(0, 381348) + + 35 * (-18505 + 1777 * delta) * S1z - + 35 * (18505 + 1777 * delta) * S2z)) + + Complex(0, 8) * params.PhiDOT2 * params.r2 * rDOT * + (779100 * params.eta3 * (S1z + S2z) + + 5 * Nu * + (Complex(0, 828806) + + 7 * (4839 + 5971 * delta) * S1z + + 7 * (4839 - 5971 * delta) * S2z) - + 62475 * (S1z + delta * S1z + S2z - delta * S2z) + + params.eta2 * (Complex(0, -976002) + + 35 * (-29599 + 3109 * delta) * S1z - + 35 * (29599 + 3109 * delta) * S2z))) + + 3 * params.r2 * + (Complex(0, 4) * params.PhiDOT2 * params.r2 * params.rDOT3 * + (Complex(0, 2) * (65451 - 350563 * Nu) * Nu + + 105 * Nu * + (6020 + delta * (3114 + 411 * Nu) + + Nu * (-4513 + 9136 * Nu)) * + S1z + + 105 * + (-3 * delta * (-408 + Nu * (1038 + 137 * Nu)) + + Nu * (6020 + Nu * (-4513 + 9136 * Nu))) * + S2z - + 128520 * (S1z + delta * S1z + S2z)) + + PhiDOT * r * params.rDOT4 * + (Complex(0, -128) * Nu * (2487 + 18334 * Nu) - + 105 * Nu * + (8689 + 8 * Nu * (-687 + 1402 * Nu) + + delta * (2143 + 8212 * Nu)) * + S1z + + 105 * + (Nu * (-8689 + 8 * (687 - 1402 * Nu) * Nu) + + delta * (-1470 + Nu * (2143 + 8212 * Nu))) * + S2z + + 154350 * (S1z + delta * S1z + S2z)) - + Complex(0, 6) * params.rDOT5 * + (-11760 * params.eta3 * (S1z + S2z) + + 4 * params.eta2 * + (Complex(0, 57338) + + 35 * (569 + 301 * delta) * S1z + + 35 * (569 - 301 * delta) * S2z) - + 16 * Nu * + (Complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + + 35 * (72 - 71 * delta) * S2z) + + 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + + params.PhiDOT5 * params.r5 * + (2263380 * params.eta3 * (S1z + S2z) + + 3 * Nu * + (Complex(0, 653432) + + 35 * (13283 + 7839 * delta) * S1z + + 35 * (13283 - 7839 * delta) * S2z) - + 219240 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * params.eta2 * + (Complex(0, -291268) + + 105 * (-7669 + 165 * delta) * S1z - + 105 * (7669 + 165 * delta) * S2z)) + + Complex(0, 14) * params.PhiDOT4 * params.r4 * rDOT * + (385170 * params.eta3 * (S1z + S2z) + + 15 * Nu * + (Complex(0, 16914) + (8633 + 2267 * delta) * S1z + + 8633 * S2z - 2267 * delta * S2z) - + 18630 * (S1z + delta * S1z + S2z - delta * S2z) + + 2 * params.eta2 * + (Complex(0, -67904) + + 15 * (-13932 + 679 * delta) * S1z - + 15 * (13932 + 679 * delta) * S2z)) + + 6 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (-6720 * params.eta3 * (S1z + S2z) + + 5 * Nu * + (Complex(0, -241704) + + 7 * (4377 + 3083 * delta) * S1z + + 7 * (4377 - 3083 * delta) * S2z) - + 36960 * (S1z + delta * S1z + S2z - delta * S2z) + + params.eta2 * (Complex(0, -364384) + + 35 * (5059 - 4753 * delta) * S1z + + 35 * (5059 + 4753 * delta) * S2z))) + + 14 * params.Mtot2 * + (Complex(0, 30) * rDOT * + (15280 * params.eta3 * (S1z + S2z) - + 4 * params.eta2 * + (Complex(0, -111) + 3562 * S1z + 682 * delta * S1z + + 945 * kappa1 * params.S1z3 + + (3562 - 682 * delta + + 945 * (-2 + kappa1) * params.S1z2) * + S2z + + 945 * (-2 + kappa2) * S1z * params.S2z2 + + 945 * kappa2 * params.S2z3) + + Nu * (Complex(0, -27520) + + 378 * + (5 * (1 + delta) * kappa1 + + 3 * (3 + delta) * lambda1) * + params.S1z3 + + (29749 - 13605 * delta) * S2z - + 1512 * (1 + delta) * kappa1 * params.S1z2 * S2z - + 378 * + (5 * (-1 + delta) * kappa2 + + 3 * (-3 + delta) * lambda2) * + params.S2z3 + + S1z * (29749 + 13605 * delta + + 1512 * (-1 + delta) * kappa2 * + params.S2z2)) + + 2 * (-8009 * (1 + delta) * S1z - + 567 * (1 + delta) * lambda1 * params.S1z3 + + (-1 + delta) * S2z * + (8009 + 567 * lambda2 * params.S2z2))) + + PhiDOT * r * + (285840 * params.eta3 * (S1z + S2z) - + 30 * params.eta2 * + (Complex(0, 11504) + 1890 * kappa1 * params.S1z3 + + 23090 * S2z - 1823 * delta * S2z + + 1890 * (-2 + kappa1) * params.S1z2 * S2z + + 1890 * kappa2 * params.S2z3 + + S1z * (23090 + 1823 * delta + + 1890 * (-2 + kappa2) * params.S2z2)) + + 30 * (689 * (1 + delta) * S1z - + 1134 * (1 + delta) * lambda1 * params.S1z3 + + (-1 + delta) * S2z * + (-689 + 1134 * lambda2 * params.S2z2)) + + 2 * Nu * + (Complex(0, 415432) - 66840 * S2z + + 15 * (8 * (-557 + 1342 * delta) * S1z + + 189 * + (5 * (1 + delta) * kappa1 + + 6 * (3 + delta) * lambda1) * + params.S1z3 - + 10736 * delta * S2z - + 2457 * (1 + delta) * kappa1 * params.S1z2 * + S2z + + 2457 * (-1 + delta) * kappa2 * S1z * + params.S2z2 - + 189 * + (5 * (-1 + delta) * kappa2 + + 6 * (-3 + delta) * lambda2) * + params.S2z3)))))) / + (317520. * params.r4)) + # + Henry et al. QC spinning hereditary terms + # (2 * M_PI * (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x4p5) + ) + + else: + return complex(0, 0) + + + +# hQC_l_m() functions contains only the non-spinning hereditary terms at +# particular PN order. + +def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, + params: Any) -> complex: + + EulerGamma = 0.5772156649015329 + x0 = 0.4375079683656479 + b0 = 2 * mass / math.exp(0.5) + r0 = b0 + kappa1 = 1.0 # for black holes kappa and lambda is 1 + kappa2 = 1.0 + delta = math.sqrt(1 - 4 * Nu) + + M_PI = math.pi + M_PI2 = math.pi ** 2 + + # keeping only the hereditary terms + # if vpnorder == 0: + # return (2 * x) + + # elif vpnorder == 2: + # return ((-5.095238095238095 + (55 * Nu) / 21.) * params.x2) + + if vpnorder == 3: + # return (4 * M_PI * params.x2p5) + return (complex(0, 0.6666666666666666) * params.x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * math.log(2) + 12 * math.log(b0) + 18 * math.log(x))) + + # elif vpnorder == 4: + # return ((-2.874338624338624 - (1069 * Nu) / 108. + (2047 * params.eta2) / 756.) * params.x3) + + elif vpnorder == 5: + # return ((complex(0,-48)*Nu - (214*M_PI)/21. + (68*Nu*M_PI)/21.)*params.x3p5) + # return ((2 * (-107 + 34 * Nu) * M_PI * params.x3p5) / 21.) # This is old implementation + return (complex(0, 0.015873015873015872) * (-107 + 34 * Nu) * params.x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * math.log(2) + 12 * math.log(b0) + 18 * math.log(x))) + + elif vpnorder == 6: + # return ((params.x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * Nu) * M_PI) - 13696 * math.log(16 * x))) / 1680.) # This is the old implementation. + + return (params.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) * Nu * S1z + complex(0, 5.333333333333333) * EulerGamma * Nu * S1z - + (8 * M_PI * S1z) / 3. + (8 * Nu * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - + complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * Nu * S2z + + complex(0, 5.333333333333333) * EulerGamma * Nu * S2z - (8 * M_PI * S2z) / 3. + (8 * Nu * M_PI * S2z) / 3. + + (912 * math.log(2)) / 35. - 64 * EulerGamma * math.log(2) + complex(0, 32) * M_PI * math.log(2) - + complex(0, 10.666666666666666) * S1z * math.log(2) + complex(0, 10.666666666666666) * Nu * S1z * math.log(2) - + complex(0, 10.666666666666666) * S2z * math.log(2) + complex(0, 10.666666666666666) * Nu * S2z * math.log(2) - + 64 * math.log(2) * math.log(2) + (88 * math.log(b0)) / 3. - 32 * EulerGamma * math.log(b0) + + complex(0, 16) * M_PI * math.log(b0) - complex(0, 5.333333333333333) * S1z * math.log(b0) + + complex(0, 5.333333333333333) * Nu * S1z * math.log(b0) - complex(0, 5.333333333333333) * S2z * math.log(b0) + + complex(0, 5.333333333333333) * Nu * S2z * math.log(b0) - 64 * math.log(2) * math.log(b0) - + 16 * math.log(b0) * math.log(b0) - (1712 * math.log(r0)) / 105. + (684 * math.log(x)) / 35. - + 48 * EulerGamma * math.log(x) + complex(0, 24) * M_PI * math.log(x) - complex(0, 8) * S1z * math.log(x) + + complex(0, 8) * Nu * S1z * math.log(x) - complex(0, 8) * S2z * math.log(x) + complex(0, 8) * Nu * S2z * math.log(x) - + 96 * math.log(2) * math.log(x) - 48 * math.log(b0) * math.log(x) - 36 * math.log(x) * math.log(x) - + complex(0, 0.4444444444444444) * delta * (S1z - S2z) * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + + 24 * math.log(2) + 12 * math.log(b0) + 18 * math.log(x)))) + + elif vpnorder == 7: + # return (((-2173 - 4990 * Nu + 1120 * params.eta2) * M_PI * params.x4p5) / 378.) # This is the old implementation. + return (complex(0, 0.0004409171075837742) * params.x4p5 * (23903 - 26076 * EulerGamma + 57452 * Nu - + 59880 * EulerGamma * Nu - 18728 * params.eta2 + 13440 * EulerGamma * params.eta2 + + complex(0, 13038) * M_PI + complex(0, 29940) * Nu * M_PI - complex(0, 6720) * params.eta2 * M_PI - + 8316 * kappa1 * params.S1z2 - 8316 * delta * kappa1 * params.S1z2 + 9072 * EulerGamma * kappa1 * params.S1z2 + + 9072 * delta * EulerGamma * kappa1 * params.S1z2 + 16632 * kappa1 * Nu * params.S1z2 - + 18144 * EulerGamma * kappa1 * Nu * params.S1z2 - complex(0, 4536) * kappa1 * M_PI * params.S1z2 - + complex(0, 4536) * delta * kappa1 * M_PI * params.S1z2 + complex(0, 9072) * kappa1 * Nu * M_PI * params.S1z2 - + 33264 * Nu * S1z * S2z + 36288 * EulerGamma * Nu * S1z * S2z - complex(0, 18144) * Nu * M_PI * S1z * S2z - + 8316 * kappa2 * params.S2z2 + 8316 * delta * kappa2 * params.S2z2 + 9072 * EulerGamma * kappa2 * params.S2z2 - + 9072 * delta * EulerGamma * kappa2 * params.S2z2 + 16632 * kappa2 * Nu * params.S2z2 - + 18144 * EulerGamma * kappa2 * Nu * params.S2z2 - complex(0, 4536) * kappa2 * M_PI * params.S2z2 + + complex(0, 4536) * delta * kappa2 * M_PI * params.S2z2 + complex(0, 9072) * kappa2 * Nu * M_PI * params.S2z2 - + 52152 * math.log(2) - 119760 * Nu * math.log(2) + 26880 * params.eta2 * math.log(2) + + 18144 * kappa1 * params.S1z2 * math.log(2) + 18144 * delta * kappa1 * params.S1z2 * math.log(2) - + 36288 * kappa1 * Nu * params.S1z2 * math.log(2) + 72576 * Nu * S1z * S2z * math.log(2) + + 18144 * kappa2 * params.S2z2 * math.log(2) - 18144 * delta * kappa2 * params.S2z2 * math.log(2) - + 36288 * kappa2 * Nu * params.S2z2 * math.log(2) + 12 * (-2173 - 4990 * Nu + 1120 * params.eta2 + + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * math.log(b0) + 18 * (-2173 - 4990 * Nu + + 1120 * params.eta2 + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * math.log(x))) + + # 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 * (params.x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * Nu) + + 12 * (628830397253 + 1854914893791 * Nu + 421984442880 * math.log(2)) + 415134720 * EulerGamma * (6099 - 40277 * Nu + complex(0, 70) * (107 - 13 * Nu) * M_PI + 280 * (-107 + 13 * Nu) * math.log(2)) + + 35 * (-28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) + + 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2 - 955450349568 * Nu * math.log(2) + + 3321077760 * (-107 + 13 * Nu) * math.log(2) * math.log(2) - complex(0, 5930496) * M_PI * (6099 - 74773 * Nu + 280 * (-107 + 13 * Nu) * math.log(2))) - 5700491596800 * math.log(mass) + + 6966444925440 * math.log(x) + 69189120 * (420 * (-107 + 13 * Nu) * math.log(b0) * math.log(b0) + + 420 * (-107 + 13 * Nu) * math.log(mass) * math.log(mass) - 14 * math.log(mass) * (-11803 * Nu + + 60 * EulerGamma * (-107 + 13 * Nu) + complex(0, 30) * (107 - 13 * Nu) * M_PI + + 120 * (-107 + 13 * Nu) * math.log(2) + 90 * (-107 + 13 * Nu) * math.log(x)) + 14 * math.log(b0) * (5885 - 6420 * EulerGamma - 11803 * Nu + 780 * EulerGamma * Nu + complex(0, 3210) * M_PI - + complex(0, 390) * Nu * M_PI + 120 * (-107 + 13 * Nu) * math.log(2) + (6420 - 780 * Nu) * math.log(mass) + + 90 * (-107 + 13 * Nu) * math.log(x)) + 5 * math.log(x) * (-74149 * Nu + 252 * EulerGamma * (-107 + 13 * Nu) - + complex(0, 126) * (-107 + 13 * Nu) * M_PI + 504 * (-107 + 13 * Nu) * math.log(2) + + 189 * (-107 + 13 * Nu) * math.log(x)) + 84672 * Nu * math.log(x0))))) + + # return ((params.x5 * (276756480 * EulerGamma * (11449 + 19105 * Nu) - 12 * (846557506853 + 1008017482431 * Nu) + 35 * (28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) - complex(0, 3953664) * (11449 + 109657 * Nu) * M_PI - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2) + 138378240 * (11449 + 19105 * Nu) * math.log(16 * x))) / 7.62810048e10) + + else: + return complex(0, 0) + + +def hl_2_m_2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: Any) -> complex: + + if vpnorder < 0 or vpnorder > 8: + raise ValueError("Error in hl_2_m_2: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * mass * Nu * math.sqrt(math.pi / 5.0)) / R + + # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms + waveform_modes = ( + hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) + ) + + # cpolar(r, theta) in C is equivalent to cmath.rect(r, theta) in Python + phase_factor = cmath.rect(1.0, -2 * Phi) + + return amplitude * waveform_modes * phase_factor + +def hl_2_m_min2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: Any) -> complex: + + if vpnorder < 0 or vpnorder > 8: + raise ValueError("Error in hl_2_m_min2: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * mass * Nu * math.sqrt(math.pi / 5.0)) / R + + # Sum the GO and QC terms, then apply the complex conjugate for the m = -2 mode + waveform_modes = ( + hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) + ).conjugate() + + # Calculate the phase factor with positive 2 * Phi + phase_factor = cmath.rect(1.0, 2 * Phi) + + return amplitude * waveform_modes * phase_factor \ No newline at end of file diff --git a/esigmapy/python_codes/ESIGMA_PNInspiral.py b/esigmapy/python_codes/ESIGMA_PNInspiral.py index 9283b87..caba6f5 100644 --- a/esigmapy/python_codes/ESIGMA_PNInspiral.py +++ b/esigmapy/python_codes/ESIGMA_PNInspiral.py @@ -2719,18 +2719,15 @@ def mikkola_finder(eccentricity, mean_anomaly): sgn_b = 1.0 if b >= 0.0 else -1.0 # Mikkola Eq. (9b) - # Using your existing pow1_3 function z = pow1_3(b + sgn_b * sqrt(b * b + a * a * a)) # Mikkola Eq. (9c) s = z - a / z # add the correction given in Mikkola Eq. (7) - # Using your existing pow5 function s = s - 0.078 * pow5(s) / (1.0 + eccentricity) # finally Mikkola Eq. (8) gives u - # Using your existing pow3 function ecc_anomaly = mean_anomaly + eccentricity * (3.0 * s - 4.0 * pow3(s)) # correct the sign of u @@ -2741,7 +2738,6 @@ def pn_kepler_equation(eta, x, e, l): """ 3PN accurate Kepler equation solver using Newton's method. """ - # (void)eta, (void)x equivalents are unnecessary in Python mean_anom_negative = False u = 0.0 tol = 1.0e-12 @@ -2791,7 +2787,6 @@ def eccentric_x_model_odes(t, y, params): y = [x, e, l, phi] """ # Assuming params is an object or dictionary - # In Python, we usually access attributes directly eta = params.eta m1 = params.m1 m2 = params.m2 @@ -2821,7 +2816,7 @@ def eccentric_x_model_odes(t, y, params): # Check for NaN (equivalent to XLAL_REAL8_FAIL_NAN) if math.isnan(dydt[0]) or math.isnan(dydt[1]): - # You can raise an exception or return a specific error code + # Raise an exception or return a specific error code raise ValueError("ODE derivative calculated as NaN") return dydt From f32aa427cbaeb0b606e689af91b99a0a984e10b6 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Wed, 22 Apr 2026 22:20:43 +0530 Subject: [PATCH 05/36] added __init__.py --- esigmapy/python_codes/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 esigmapy/python_codes/__init__.py 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 From 8981acd0f2a547d2346007da7623fb1ed5c2ce08 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Wed, 22 Apr 2026 22:21:30 +0530 Subject: [PATCH 06/36] Added new functions --- esigmapy/python_codes/esigma_go_terms.py | 2545 ++++++++++++++++++++++ 1 file changed, 2545 insertions(+) create mode 100644 esigmapy/python_codes/esigma_go_terms.py diff --git a/esigmapy/python_codes/esigma_go_terms.py b/esigmapy/python_codes/esigma_go_terms.py new file mode 100644 index 0000000..25cd460 --- /dev/null +++ b/esigmapy/python_codes/esigma_go_terms.py @@ -0,0 +1,2545 @@ +""" +esigma_go_terms.py +==================== +Python translation of ESIGMA_GOTerms.c +""" + +import numpy as np +from dataclasses import dataclass + +# Mapping M_PI2 to pi^2 (standard in PN GW theory for this coefficient) +M_PI = np.pi +M_PI2 = M_PI ** 2 + +def Complex(real: float, imag: float) -> complex: + """Helper function to mimic the C-style Complex constructor.""" + return complex(real, imag) +def rect(r: float, phi: float) -> complex: + """Convert polar coordinates to rectangular form.""" + return r * np.exp(1j * phi) + +@dataclass +class KeplerVars: + # Basic orbital elements + pn_order: int + eta: float + Mtot: float + x: float + e: float + l: float + r: float + rDOT: float + PhiDOT: float + S1z: float + S2z: float + + # Powers of x + x2: float = 0.0; x2p5: float = 0.0; x3: float = 0.0; x3p5: float = 0.0 + x4: float = 0.0; x4p5: float = 0.0; x5: float = 0.0; x6: float = 0.0 + x7: float = 0.0; x8: float = 0.0; x9: float = 0.0 + + # Powers of e + e2: float = 0.0; e3: float = 0.0; e4: float = 0.0; e5: float = 0.0 + e6: float = 0.0; e7: float = 0.0; e8: float = 0.0; e9: float = 0.0 + + # Powers of r + r2: float = 0.0; r3: float = 0.0; r4: float = 0.0; r5: float = 0.0 + r6: float = 0.0; r7: float = 0.0; r8: float = 0.0; r9: float = 0.0 + + # Powers of rDOT + rDOT2: float = 0.0; rDOT3: float = 0.0; rDOT4: float = 0.0; rDOT5: float = 0.0 + rDOT6: float = 0.0; rDOT7: float = 0.0; rDOT8: float = 0.0; rDOT9: float = 0.0 + + # Powers of PhiDOT + PhiDOT2: float = 0.0; PhiDOT3: float = 0.0; PhiDOT4: float = 0.0; PhiDOT5: float = 0.0 + PhiDOT6: float = 0.0; PhiDOT7: float = 0.0; PhiDOT8: float = 0.0; PhiDOT9: float = 0.0 + + # Powers of S1z + S1z2: float = 0.0; S1z3: float = 0.0; S1z4: float = 0.0; S1z5: float = 0.0 + S1z6: float = 0.0; S1z7: float = 0.0; S1z8: float = 0.0; S1z9: float = 0.0 + S1z10: float = 0.0; S1z11: float = 0.0; S1z12: float = 0.0; S1z13: float = 0.0 + S1z14: float = 0.0; S1z15: float = 0.0; S1z16: float = 0.0 + + # Powers of S2z + S2z2: float = 0.0; S2z3: float = 0.0; S2z4: float = 0.0 + S2z5: float = 0.0; S2z6: float = 0.0 + + # Powers of eta + eta2: float = 0.0; eta3: float = 0.0; eta4: float = 0.0 + eta5: float = 0.0; eta6: float = 0.0 + + # Powers of Mtot + Mtot2: float = 0.0; Mtot3: float = 0.0; Mtot4: float = 0.0 + Mtot5: float = 0.0; Mtot6: float = 0.0 + + def compute_powers(self): + """Compute all power fields from the base values.""" + self.x2 = self.x**2; self.x2p5 = self.x**2.5; self.x3 = self.x**3 + self.x3p5 = self.x**3.5; self.x4 = self.x**4; self.x4p5 = self.x**4.5 + self.x5 = self.x**5; self.x6 = self.x**6; self.x7 = self.x**7 + self.x8 = self.x**8; self.x9 = self.x**9 + + self.e2 = self.e**2; self.e3 = self.e**3; self.e4 = self.e**4; self.e5 = self.e**5 + self.e6 = self.e**6; self.e7 = self.e**7; self.e8 = self.e**8; self.e9 = self.e**9 + + self.r2 = self.r**2; self.r3 = self.r**3; self.r4 = self.r**4; self.r5 = self.r**5 + self.r6 = self.r**6; self.r7 = self.r**7; self.r8 = self.r**8; self.r9 = self.r**9 + + self.rDOT2 = self.rDOT**2; self.rDOT3 = self.rDOT**3; self.rDOT4 = self.rDOT**4 + self.rDOT5 = self.rDOT**5; self.rDOT6 = self.rDOT**6; self.rDOT7 = self.rDOT**7 + self.rDOT8 = self.rDOT**8; self.rDOT9 = self.rDOT**9 + + self.PhiDOT2 = self.PhiDOT**2; self.PhiDOT3 = self.PhiDOT**3; self.PhiDOT4 = self.PhiDOT**4 + self.PhiDOT5 = self.PhiDOT**5; self.PhiDOT6 = self.PhiDOT**6; self.PhiDOT7 = self.PhiDOT**7 + self.PhiDOT8 = self.PhiDOT**8; self.PhiDOT9 = self.PhiDOT**9 + + self.S1z2 = self.S1z**2; self.S1z3 = self.S1z**3; self.S1z4 = self.S1z**4 + self.S1z5 = self.S1z**5; self.S1z6 = self.S1z**6; self.S1z7 = self.S1z**7 + self.S1z8 = self.S1z**8; self.S1z9 = self.S1z**9; self.S1z10 = self.S1z**10 + self.S1z11 = self.S1z**11; self.S1z12 = self.S1z**12; self.S1z13 = self.S1z**13 + self.S1z14 = self.S1z**14; self.S1z15 = self.S1z**15; self.S1z16 = self.S1z**16 + + self.S2z2 = self.S2z**2; self.S2z3 = self.S2z**3; self.S2z4 = self.S2z**4 + self.S2z5 = self.S2z**5; self.S2z6 = self.S2z**6 + + self.eta2 = self.eta**2; self.eta3 = self.eta**3; self.eta4 = self.eta**4 + self.eta5 = self.eta**5; self.eta6 = self.eta**6 + + self.Mtot2 = self.Mtot**2; self.Mtot3 = self.Mtot**3; self.Mtot4 = self.Mtot**4 + self.Mtot5 = self.Mtot**5; self.Mtot6 = self.Mtot**6 + +# All the hGO_l_m functions contain the 3PN non-spinning general orbit (GO) terms +# from Mishra et al. arXiv:1501.07096 and 3.5PN quasi-circular spinning +# corrections as given in Henry et al, arXiv:2209.00374v2 + +# The (2,2) mode has newly computed 4PN non-spinning quasi-circular piece from +# arXiv:2304.11185 + +# H22 + +def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, + PhiDOT: float, vpnorder: int, S1z: float, S2z: float, + x: float, params: KeplerVars) -> complex: + + # Note: In Python, `params` should be an object (like a dataclass or SimpleNamespace) + # so that attributes can be accessed via dot notation (e.g., params.PhiDOT2). + + # 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 * Nu) + + 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 + + + + if vpnorder == 0: + return (mass / r + params.PhiDOT2 * params.r2 + + Complex(0, 2) * PhiDOT * r * rDOT - params.rDOT2) + + elif vpnorder == 2: + return ((21 * params.Mtot2 * (-10 + Nu) - + 27 * (-1 + 3 * Nu) * params.r2 * combination_b * combination_a3 + + mass * r * + ((11 + 156 * Nu) * params.PhiDOT2 * params.r2 + + Complex(0, 10) * (5 + 27 * Nu) * PhiDOT * r * rDOT - + 3 * (15 + 32 * Nu) * params.rDOT2)) / + (42. * params.r2)) + + elif vpnorder == 3: + return ((params.Mtot2 * (Complex(0, -1) * rDOT * + ((3 + 3 * delta - 8 * Nu) * S1z + + (3 - 3 * delta - 8 * Nu) * S2z) + + PhiDOT * r * + ((-3 - 3 * delta + 5 * Nu) * S1z + + (-3 + 3 * delta + 5 * Nu) * S2z))) / + (3. * params.r2)) + # (<--This is the general orbit term) + # (This is the quasi-circular limit of the general orbit term-->) + # - ((-4 * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x2p5) / 3.) + + # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - Nu * (S1z + S2z)) * params.x2p5) / 3.) + # (<--This is Quentins quasi-circular term) + + elif vpnorder == 4: + return ((6 * params.Mtot3 * (3028 + 1267 * Nu + 158 * params.eta2) + + 9 * (83 - 589 * Nu + 1111 * params.eta2) * params.r3 * + combination_b2 * combination_a4 + + params.Mtot2 * r * + ((-11891 - 36575 * Nu + 13133 * params.eta2) * params.PhiDOT2 * + params.r2 + + Complex(0, 8) * (-773 - 3767 * Nu + 2852 * params.eta2) * + PhiDOT * r * rDOT - + 6 * (-619 + 2789 * Nu + 934 * params.eta2) * params.rDOT2) - + 3 * mass * params.r2 * + (2 * (-835 - 19 * Nu + 2995 * params.eta2) * params.PhiDOT4 * + params.r4 + + Complex(0, 6) * (-433 - 721 * Nu + 1703 * params.eta2) * + params.PhiDOT3 * params.r3 * rDOT + + 6 * (-33 + 1014 * Nu + 232 * params.eta2) * params.PhiDOT2 * + params.r2 * params.rDOT2 + + Complex(0, 4) * (-863 + 1462 * Nu + 2954 * params.eta2) * + PhiDOT * r * params.rDOT3 - + 3 * (-557 + 664 * Nu + 1712 * params.eta2) * params.rDOT4)) / + (1512. * params.r3) + + (3 * params.Mtot3 * + (S1z * (4 * Nu * S2z + (1 + delta - 2 * Nu) * S1z * kappa1) - + (-1 + delta + 2 * Nu) * params.S2z2 * kappa2)) / + (4. * params.r3)) + # This is where circular limit terms added and subtracted + # - ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + + # ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + + elif vpnorder == 5: + return ((params.Mtot2 * Nu * + (2 * mass * (Complex(0, -702) * PhiDOT * r + rDOT) + + 3 * r * + (Complex(0, -316) * params.PhiDOT3 * params.r3 - + 847 * params.PhiDOT2 * params.r2 * rDOT + + Complex(0, 184) * PhiDOT * r * params.rDOT2 - + 122 * params.rDOT3))) / + (105. * params.r3) + # Henry et al. QC spin terms + # + ((2*(56*delta*Nu*(-S1z + S2z) + 101*Nu*(S1z + S2z) + 132*params.eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) + + + # Henry et al. ecc spin terms + ((params.Mtot2 * + (mass * + ((238 + delta * (238 - 141 * Nu) + Nu * (-181 + 474 * Nu)) * + PhiDOT * r * S1z + + Complex(0, 8) * + (55 + delta * (55 - 19 * Nu) + + 2 * Nu * (-50 + 43 * Nu)) * + rDOT * S1z + + (238 + delta * (-238 + 141 * Nu) + Nu * (-181 + 474 * Nu)) * + PhiDOT * r * S2z + + Complex(0, 8) * + (55 + delta * (-55 + 19 * Nu) + + 2 * Nu * (-50 + 43 * Nu)) * + rDOT * S2z) - + r * (PhiDOT * r * params.rDOT2 * + (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * Nu + + 188 * params.eta2) * + S1z) + + (18 * (-1 + delta) + 5 * (63 + 55 * delta) * Nu - + 188 * params.eta2) * + S2z) - + Complex(0, 2) * params.rDOT3 * + ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * Nu - + 4 * params.eta2) * + S1z + + (-27 + 27 * delta + 30 * Nu - 42 * delta * Nu - + 4 * params.eta2) * + S2z) + + Complex(0, 2) * params.PhiDOT2 * params.r2 * rDOT * + ((51 + 88 * Nu * (-3 + 5 * Nu) + + delta * (51 + 62 * Nu)) * + S1z + + (51 + 88 * Nu * (-3 + 5 * Nu) - + delta * (51 + 62 * Nu)) * + S2z) + + params.PhiDOT3 * params.r3 * + ((120 * (1 + delta) + (-483 + 83 * delta) * Nu + + 234 * params.eta2) * + S1z + + (120 + 3 * Nu * (-161 + 78 * Nu) - + delta * (120 + 83 * Nu)) * + S2z)))) / + (84. * params.r3))) + + elif vpnorder == 6: + return ((4 * params.Mtot4 * + (-8203424 + 2180250 * params.eta2 + 592600 * params.eta3 + + 15 * Nu * (-5503804 + 142065 * M_PI2)) - + 2700 * (-507 + 6101 * Nu - 25050 * params.eta2 + 34525 * params.eta3) * + params.r4 * combination_b3 * combination_a5 + + params.Mtot3 * r * + (params.PhiDOT2 * + (337510808 - 198882000 * params.eta2 + + 56294600 * params.eta3 + + Nu * (183074880 - 6392925 * M_PI2)) * + params.r2 + + Complex(0, 110) * PhiDOT * + (-5498800 - 785120 * params.eta2 + 909200 * params.eta3 + + 3 * Nu * (-1849216 + 38745 * M_PI2)) * + r * rDOT + + 2 * + (51172744 - 94929000 * params.eta2 - 5092400 * params.eta3 + + 45 * Nu * (2794864 + 142065 * M_PI2)) * + params.rDOT2) - + 20 * params.Mtot2 * params.r2 * + ((-986439 + 1873255 * Nu - 9961400 * params.eta2 + + 6704345 * params.eta3) * + params.PhiDOT4 * params.r4 + + Complex(0, 4) * + (-273687 - 978610 * Nu - 4599055 * params.eta2 + + 2783005 * params.eta3) * + params.PhiDOT3 * params.r3 * rDOT + + (-181719 + 19395325 * Nu + 8237980 * params.eta2 + + 2612735 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT2 + + Complex(0, 8) * + (-234312 + 1541140 * Nu + 1230325 * params.eta2 + + 1828625 * params.eta3) * + PhiDOT * r * params.rDOT3 - + 3 * + (-370268 + 1085140 * Nu + 2004715 * params.eta2 + + 1810425 * params.eta3) * + params.rDOT4) + + 300 * mass * params.r3 * + (4 * + (12203 - 36427 * Nu - 27334 * params.eta2 + + 149187 * params.eta3) * + params.PhiDOT6 * params.r6 + + Complex(0, 2) * + (44093 - 68279 * Nu - 295346 * params.eta2 + + 541693 * params.eta3) * + params.PhiDOT5 * params.r5 * rDOT + + 2 * + (27432 - 202474 * Nu + 247505 * params.eta2 + + 394771 * params.eta3) * + params.PhiDOT4 * params.r4 * params.rDOT2 + + Complex(0, 2) * + (97069 - 383990 * Nu - 8741 * params.eta2 + + 1264800 * params.eta3) * + params.PhiDOT3 * params.r3 * params.rDOT3 + + (-42811 + 53992 * Nu + 309136 * params.eta2 - + 470840 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT4 + + Complex(0, 2) * + (51699 - 252256 * Nu + 131150 * params.eta2 + + 681160 * params.eta3) * + PhiDOT * r * params.rDOT5 - + 3 * + (16743 - 75104 * Nu + 26920 * params.eta2 + + 207200 * params.eta3) * + params.rDOT6)) / + (3.3264e6 * params.r4) + # Henry et al. QC spin terms + # + (((4*(1 + delta)*(-7 + 9*kappa1) - 7*(9 + 17*delta)*Nu - 9*(15 + 7*delta)*kappa1*Nu + 12*(7 - 17*kappa1)*params.eta2)*params.S1z2 + 2*S1z*(Complex(0,-42)*(1 + delta - 2*Nu) - 84*(1 + delta - Nu)*M_PI + Nu*(-271 + 288*Nu)*S2z) + S2z*(12*(7 - 17*kappa2)*params.eta2*S2z + 4*(-1 + delta)*(Complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + Nu*(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 * + (params.Mtot3 * + (2 * mass * + (S1z * (Complex(0, 14) * (1 + delta - 2 * Nu) + + 42 * (1 + delta - 2 * Nu) * Nu * S1z + + kappa1 * + (438 + delta * (438 + 103 * Nu) + + Nu * (-773 + 108 * Nu)) * + S1z) + + 2 * + (Complex(0, -7) * (-1 + delta + 2 * Nu) + + (995 - 192 * Nu) * Nu * S1z) * + S2z - + (42 * Nu * (-1 + delta + 2 * Nu) + + kappa2 * (-438 + (773 - 108 * Nu) * Nu + + delta * (438 + 103 * Nu))) * + params.S2z2) + + r * (params.rDOT2 * + (Complex(0, 56) * (1 + delta - 2 * Nu) * S1z + + (-56 * (1 + delta - 2 * Nu) * Nu + + kappa1 * (291 * (1 + delta) - + 2 * (445 + 154 * delta) * Nu + + 24 * params.eta2)) * + params.S1z2 + + 4 * Nu * (-3 + 44 * Nu) * S1z * S2z + + S2z * (Complex(0, -56) * (-1 + delta + 2 * Nu) + + 56 * Nu * (-1 + delta + 2 * Nu) * S2z + + kappa2 * + (291 - 890 * Nu + 24 * params.eta2 + + delta * (-291 + 308 * Nu)) * + S2z)) + + params.PhiDOT2 * params.r2 * + (Complex(0, 196) * (1 + delta - 2 * Nu) * S1z + + (56 * Nu * (7 + 7 * delta + Nu) + + kappa1 * (-153 * (1 + delta) - + 2 * (62 + 215 * delta) * Nu + + 804 * params.eta2)) * + params.S1z2 + + 8 * (60 - 187 * Nu) * Nu * S1z * S2z + + S2z * (Complex(0, -196) * (-1 + delta + 2 * Nu) + + 56 * Nu * (7 - 7 * delta + Nu) * S2z + + kappa2 * + (-153 + 4 * Nu * (-31 + 201 * Nu) + + delta * (153 + 430 * Nu)) * + S2z)) + + 2 * PhiDOT * r * rDOT * + (Complex(0, -1) * + (117 * (1 + delta) * kappa1 - + 434 * (1 + delta) * Nu + + (-23 + 211 * delta) * kappa1 * Nu + + 2 * (14 - 195 * kappa1) * params.eta2) * + params.S1z2 + + 4 * S1z * + (35 * (1 + delta - 2 * Nu) + + Complex(0, 1) * (9 - 209 * Nu) * Nu * S2z) + + S2z * (-140 * (-1 + delta + 2 * Nu) + + Complex(0, 1) * + (-14 * Nu * (-31 + 31 * delta + 2 * Nu) + + kappa2 * (117 * (-1 + delta) + + (23 + 211 * delta) * Nu + + 390 * params.eta2)) * + S2z))))) / + params.r4)) + # + Henry et al. QC spinning hereditary terms + # (((-8 * M_PI * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x4) / 3.)) + + elif vpnorder == 7: + return ( + # Henry et al QC spin terms + # ((3318*params.eta3*(S1z + S2z) + Nu*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*params.S1z3 - 1008*params.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*params.S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*params.S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*params.eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*params.S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. + # + + # Henry et al. ecc+spin terms + ((params.Mtot2 * + (-3 * mass * r * + (Complex(0, -16) * params.rDOT3 * + (Complex(0, 12) * Nu * (-16703 + 4427 * Nu) + + 35 * Nu * + (4578 + Nu * (-4288 + 765 * Nu) + + delta * (3748 + 802 * Nu)) * + S1z + + 35 * + (delta * (942 - 2 * Nu * (1874 + 401 * Nu)) + + Nu * (4578 + Nu * (-4288 + 765 * Nu))) * + S2z - + 32970 * (S1z + delta * S1z + S2z)) + + 4 * PhiDOT * r * params.rDOT2 * + (-338520 * params.eta3 * (S1z + S2z) + + 48930 * (S1z + delta * S1z + S2z - delta * S2z) + + Nu * (Complex(0, 3420696) - + 35 * (14154 + 21167 * delta) * S1z - + 495390 * S2z + 740845 * delta * S2z) + + params.eta2 * (Complex(0, -612336) + + 245 * (3566 - 1565 * delta) * S1z + + 245 * (3566 + 1565 * delta) * S2z)) + + params.PhiDOT3 * params.r3 * + (2515380 * params.eta3 * (S1z + S2z) - + 5 * Nu * + (Complex(0, 1859936) + + 7 * (82329 + 37061 * delta) * S1z + + 7 * (82329 - 37061 * delta) * S2z) - + 128100 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * params.eta2 * + (Complex(0, 381348) + + 35 * (-18505 + 1777 * delta) * S1z - + 35 * (18505 + 1777 * delta) * S2z)) + + Complex(0, 8) * params.PhiDOT2 * params.r2 * rDOT * + (779100 * params.eta3 * (S1z + S2z) + + 5 * Nu * + (Complex(0, 828806) + + 7 * (4839 + 5971 * delta) * S1z + + 7 * (4839 - 5971 * delta) * S2z) - + 62475 * (S1z + delta * S1z + S2z - delta * S2z) + + params.eta2 * (Complex(0, -976002) + + 35 * (-29599 + 3109 * delta) * S1z - + 35 * (29599 + 3109 * delta) * S2z))) + + 3 * params.r2 * + (Complex(0, 4) * params.PhiDOT2 * params.r2 * params.rDOT3 * + (Complex(0, 2) * (65451 - 350563 * Nu) * Nu + + 105 * Nu * + (6020 + delta * (3114 + 411 * Nu) + + Nu * (-4513 + 9136 * Nu)) * + S1z + + 105 * + (-3 * delta * (-408 + Nu * (1038 + 137 * Nu)) + + Nu * (6020 + Nu * (-4513 + 9136 * Nu))) * + S2z - + 128520 * (S1z + delta * S1z + S2z)) + + PhiDOT * r * params.rDOT4 * + (Complex(0, -128) * Nu * (2487 + 18334 * Nu) - + 105 * Nu * + (8689 + 8 * Nu * (-687 + 1402 * Nu) + + delta * (2143 + 8212 * Nu)) * + S1z + + 105 * + (Nu * (-8689 + 8 * (687 - 1402 * Nu) * Nu) + + delta * (-1470 + Nu * (2143 + 8212 * Nu))) * + S2z + + 154350 * (S1z + delta * S1z + S2z)) - + Complex(0, 6) * params.rDOT5 * + (-11760 * params.eta3 * (S1z + S2z) + + 4 * params.eta2 * + (Complex(0, 57338) + + 35 * (569 + 301 * delta) * S1z + + 35 * (569 - 301 * delta) * S2z) - + 16 * Nu * + (Complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + + 35 * (72 - 71 * delta) * S2z) + + 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + + params.PhiDOT5 * params.r5 * + (2263380 * params.eta3 * (S1z + S2z) + + 3 * Nu * + (Complex(0, 653432) + + 35 * (13283 + 7839 * delta) * S1z + + 35 * (13283 - 7839 * delta) * S2z) - + 219240 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * params.eta2 * + (Complex(0, -291268) + + 105 * (-7669 + 165 * delta) * S1z - + 105 * (7669 + 165 * delta) * S2z)) + + Complex(0, 14) * params.PhiDOT4 * params.r4 * rDOT * + (385170 * params.eta3 * (S1z + S2z) + + 15 * Nu * + (Complex(0, 16914) + (8633 + 2267 * delta) * S1z + + 8633 * S2z - 2267 * delta * S2z) - + 18630 * (S1z + delta * S1z + S2z - delta * S2z) + + 2 * params.eta2 * + (Complex(0, -67904) + + 15 * (-13932 + 679 * delta) * S1z - + 15 * (13932 + 679 * delta) * S2z)) + + 6 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (-6720 * params.eta3 * (S1z + S2z) + + 5 * Nu * + (Complex(0, -241704) + + 7 * (4377 + 3083 * delta) * S1z + + 7 * (4377 - 3083 * delta) * S2z) - + 36960 * (S1z + delta * S1z + S2z - delta * S2z) + + params.eta2 * (Complex(0, -364384) + + 35 * (5059 - 4753 * delta) * S1z + + 35 * (5059 + 4753 * delta) * S2z))) + + 14 * params.Mtot2 * + (Complex(0, 30) * rDOT * + (15280 * params.eta3 * (S1z + S2z) - + 4 * params.eta2 * + (Complex(0, -111) + 3562 * S1z + 682 * delta * S1z + + 945 * kappa1 * params.S1z3 + + (3562 - 682 * delta + + 945 * (-2 + kappa1) * params.S1z2) * + S2z + + 945 * (-2 + kappa2) * S1z * params.S2z2 + + 945 * kappa2 * params.S2z3) + + Nu * (Complex(0, -27520) + + 378 * + (5 * (1 + delta) * kappa1 + + 3 * (3 + delta) * lambda1) * + params.S1z3 + + (29749 - 13605 * delta) * S2z - + 1512 * (1 + delta) * kappa1 * params.S1z2 * S2z - + 378 * + (5 * (-1 + delta) * kappa2 + + 3 * (-3 + delta) * lambda2) * + params.S2z3 + + S1z * (29749 + 13605 * delta + + 1512 * (-1 + delta) * kappa2 * + params.S2z2)) + + 2 * (-8009 * (1 + delta) * S1z - + 567 * (1 + delta) * lambda1 * params.S1z3 + + (-1 + delta) * S2z * + (8009 + 567 * lambda2 * params.S2z2))) + + PhiDOT * r * + (285840 * params.eta3 * (S1z + S2z) - + 30 * params.eta2 * + (Complex(0, 11504) + 1890 * kappa1 * params.S1z3 + + 23090 * S2z - 1823 * delta * S2z + + 1890 * (-2 + kappa1) * params.S1z2 * S2z + + 1890 * kappa2 * params.S2z3 + + S1z * (23090 + 1823 * delta + + 1890 * (-2 + kappa2) * params.S2z2)) + + 30 * (689 * (1 + delta) * S1z - + 1134 * (1 + delta) * lambda1 * params.S1z3 + + (-1 + delta) * S2z * + (-689 + 1134 * lambda2 * params.S2z2)) + + 2 * Nu * + (Complex(0, 415432) - 66840 * S2z + + 15 * (8 * (-557 + 1342 * delta) * S1z + + 189 * + (5 * (1 + delta) * kappa1 + + 6 * (3 + delta) * lambda1) * + params.S1z3 - + 10736 * delta * S2z - + 2457 * (1 + delta) * kappa1 * params.S1z2 * + S2z + + 2457 * (-1 + delta) * kappa2 * S1z * + params.S2z2 - + 189 * + (5 * (-1 + delta) * kappa2 + + 6 * (-3 + delta) * lambda2) * + params.S2z3)))))) / + (317520. * params.r4)) + # + Henry et al. QC spinning hereditary terms + # (2 * M_PI * (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x4p5) + ) + + else: + return complex(0, 0) + +# hQC_l_m() functions contains only the non-spinning hereditary terms at +# particular PN order. + +def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, + params: KeplerVars) -> complex: + + EulerGamma = 0.5772156649015329 + x0 = 0.4375079683656479 + b0 = 2 * 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 * Nu) + + # keeping only the hereditary terms + # if vpnorder == 0: + # return (2 * x) + + # elif vpnorder == 2: + # return ((-5.095238095238095 + (55 * Nu) / 21.) * params.x2) + + if vpnorder == 3: + # return (4 * M_PI * params.x2p5) + return (complex(0, 0.6666666666666666) * params.x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) + + # elif vpnorder == 4: + # return ((-2.874338624338624 - (1069 * Nu) / 108. + (2047 * params.eta2) / 756.) * params.x3) + + elif vpnorder == 5: + # return ((complex(0,-48)*Nu - (214*M_PI)/21. + (68*Nu*M_PI)/21.)*params.x3p5) + # return ((2 * (-107 + 34 * Nu) * M_PI * params.x3p5) / 21.) # This is old implementation + return (complex(0, 0.015873015873015872) * (-107 + 34 * Nu) * params.x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) + + elif vpnorder == 6: + # return ((params.x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * Nu) * M_PI) - 13696 * np.log(16 * x))) / 1680.) # This is the old implementation. + + return (params.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) * Nu * S1z + complex(0, 5.333333333333333) * EulerGamma * Nu * S1z - + (8 * M_PI * S1z) / 3. + (8 * Nu * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - + complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * Nu * S2z + + complex(0, 5.333333333333333) * EulerGamma * Nu * S2z - (8 * M_PI * S2z) / 3. + (8 * Nu * M_PI * S2z) / 3. + + (912 * np.log(2)) / 35. - 64 * EulerGamma * np.log(2) + complex(0, 32) * M_PI * np.log(2) - + complex(0, 10.666666666666666) * S1z * np.log(2) + complex(0, 10.666666666666666) * Nu * S1z * np.log(2) - + complex(0, 10.666666666666666) * S2z * np.log(2) + complex(0, 10.666666666666666) * Nu * S2z * np.log(2) - + 64 * np.log(2) * np.log(2) + (88 * np.log(b0)) / 3. - 32 * EulerGamma * np.log(b0) + + complex(0, 16) * M_PI * np.log(b0) - complex(0, 5.333333333333333) * S1z * np.log(b0) + + complex(0, 5.333333333333333) * Nu * S1z * np.log(b0) - complex(0, 5.333333333333333) * S2z * np.log(b0) + + complex(0, 5.333333333333333) * Nu * S2z * np.log(b0) - 64 * np.log(2) * np.log(b0) - + 16 * np.log(b0) * np.log(b0) - (1712 * np.log(r0)) / 105. + (684 * np.log(x)) / 35. - + 48 * EulerGamma * np.log(x) + complex(0, 24) * M_PI * np.log(x) - complex(0, 8) * S1z * np.log(x) + + complex(0, 8) * Nu * S1z * np.log(x) - complex(0, 8) * S2z * np.log(x) + complex(0, 8) * Nu * S2z * np.log(x) - + 96 * np.log(2) * np.log(x) - 48 * np.log(b0) * np.log(x) - 36 * np.log(x) * np.log(x) - + complex(0, 0.4444444444444444) * delta * (S1z - S2z) * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x)))) + + elif vpnorder == 7: + # return (((-2173 - 4990 * Nu + 1120 * params.eta2) * M_PI * params.x4p5) / 378.) # This is the old implementation. + return (complex(0, 0.0004409171075837742) * params.x4p5 * (23903 - 26076 * EulerGamma + 57452 * Nu - + 59880 * EulerGamma * Nu - 18728 * params.eta2 + 13440 * EulerGamma * params.eta2 + + complex(0, 13038) * M_PI + complex(0, 29940) * Nu * M_PI - complex(0, 6720) * params.eta2 * M_PI - + 8316 * kappa1 * params.S1z2 - 8316 * delta * kappa1 * params.S1z2 + 9072 * EulerGamma * kappa1 * params.S1z2 + + 9072 * delta * EulerGamma * kappa1 * params.S1z2 + 16632 * kappa1 * Nu * params.S1z2 - + 18144 * EulerGamma * kappa1 * Nu * params.S1z2 - complex(0, 4536) * kappa1 * M_PI * params.S1z2 - + complex(0, 4536) * delta * kappa1 * M_PI * params.S1z2 + complex(0, 9072) * kappa1 * Nu * M_PI * params.S1z2 - + 33264 * Nu * S1z * S2z + 36288 * EulerGamma * Nu * S1z * S2z - complex(0, 18144) * Nu * M_PI * S1z * S2z - + 8316 * kappa2 * params.S2z2 + 8316 * delta * kappa2 * params.S2z2 + 9072 * EulerGamma * kappa2 * params.S2z2 - + 9072 * delta * EulerGamma * kappa2 * params.S2z2 + 16632 * kappa2 * Nu * params.S2z2 - + 18144 * EulerGamma * kappa2 * Nu * params.S2z2 - complex(0, 4536) * kappa2 * M_PI * params.S2z2 + + complex(0, 4536) * delta * kappa2 * M_PI * params.S2z2 + complex(0, 9072) * kappa2 * Nu * M_PI * params.S2z2 - + 52152 * np.log(2) - 119760 * Nu * np.log(2) + 26880 * params.eta2 * np.log(2) + + 18144 * kappa1 * params.S1z2 * np.log(2) + 18144 * delta * kappa1 * params.S1z2 * np.log(2) - + 36288 * kappa1 * Nu * params.S1z2 * np.log(2) + 72576 * Nu * S1z * S2z * np.log(2) + + 18144 * kappa2 * params.S2z2 * np.log(2) - 18144 * delta * kappa2 * params.S2z2 * np.log(2) - + 36288 * kappa2 * Nu * params.S2z2 * np.log(2) + 12 * (-2173 - 4990 * Nu + 1120 * params.eta2 + + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(b0) + 18 * (-2173 - 4990 * Nu + + 1120 * params.eta2 + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(x))) + + # 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 * (params.x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * Nu) + + 12 * (628830397253 + 1854914893791 * Nu + 421984442880 * np.log(2)) + 415134720 * EulerGamma * (6099 - 40277 * Nu + complex(0, 70) * (107 - 13 * Nu) * M_PI + 280 * (-107 + 13 * Nu) * np.log(2)) + + 35 * (-28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) + + 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2 - 955450349568 * Nu * np.log(2) + + 3321077760 * (-107 + 13 * Nu) * np.log(2) * np.log(2) - complex(0, 5930496) * M_PI * (6099 - 74773 * Nu + 280 * (-107 + 13 * Nu) * np.log(2))) - 5700491596800 * np.log(mass) + + 6966444925440 * np.log(x) + 69189120 * (420 * (-107 + 13 * Nu) * np.log(b0) * np.log(b0) + + 420 * (-107 + 13 * Nu) * np.log(mass) * np.log(mass) - 14 * np.log(mass) * (-11803 * Nu + + 60 * EulerGamma * (-107 + 13 * Nu) + complex(0, 30) * (107 - 13 * Nu) * M_PI + + 120 * (-107 + 13 * Nu) * np.log(2) + 90 * (-107 + 13 * Nu) * np.log(x)) + 14 * np.log(b0) * (5885 - 6420 * EulerGamma - 11803 * Nu + 780 * EulerGamma * Nu + complex(0, 3210) * M_PI - + complex(0, 390) * Nu * M_PI + 120 * (-107 + 13 * Nu) * np.log(2) + (6420 - 780 * Nu) * np.log(mass) + + 90 * (-107 + 13 * Nu) * np.log(x)) + 5 * np.log(x) * (-74149 * Nu + 252 * EulerGamma * (-107 + 13 * Nu) - + complex(0, 126) * (-107 + 13 * Nu) * M_PI + 504 * (-107 + 13 * Nu) * np.log(2) + + 189 * (-107 + 13 * Nu) * np.log(x)) + 84672 * Nu * np.log(x0))))) + + # return ((params.x5 * (276756480 * EulerGamma * (11449 + 19105 * Nu) - 12 * (846557506853 + 1008017482431 * Nu) + 35 * (28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) - complex(0, 3953664) * (11449 + 109657 * Nu) * M_PI - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2) + 138378240 * (11449 + 19105 * Nu) * np.log(16 * x))) / 7.62810048e10) + + else: + return complex(0, 0) + +def hl_2_m_2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: KeplerVars) -> complex: + + if vpnorder < 0 or vpnorder > 8: + raise ValueError("Error in hl_2_m_2: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms + waveform_modes = ( + hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) + ) + + # cpolar(r, theta) in C is equivalent to rect(r, theta) in Python + phase_factor = rect(1.0, -2 * Phi) + + return amplitude * waveform_modes * phase_factor + +def hl_2_m_min2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: KeplerVars) -> complex: + + if vpnorder < 0 or vpnorder > 8: + raise ValueError("Error in hl_2_m_min2: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Sum the GO and QC terms, then apply the complex conjugate for the m = -2 mode + waveform_modes = ( + hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) + ).conjugate() + + # Calculate the phase factor with positive 2 * Phi + phase_factor = rect(1.0, 2 * Phi) + + return amplitude * waveform_modes * phase_factor + +# H21 + +def hGO_2_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + r0 = 2 * mass / np.exp(0.5) + + if vpnorder == 1: + return 0.6666666666666666j * delta * mass * PhiDOT + + elif vpnorder == 2: + return ( + (-0.5j * params.Mtot2 * + ((1 + delta) * S1z + (-1 + delta) * S2z)) + / params.r2 + ) + + elif vpnorder == 3: + return ( + (0.023809523809523808j * delta * mass * PhiDOT * + (4 * mass * (-9 + 11 * Nu) + + r * ((19 - 24 * Nu) * params.PhiDOT2 * params.r2 + + 2j * (83 + 2 * Nu) * PhiDOT * r * rDOT + + 2 * (-33 + 10 * Nu) * params.rDOT2))) + / r + ) + + elif vpnorder == 4: + return ( + (0.011904761904761904j * params.Mtot2 * + (2 * mass * + ((77 + 59 * Nu + 11 * delta * (7 + Nu)) * S1z + + (-77 - 59 * Nu + 11 * delta * (7 + Nu)) * S2z) + + r * (-2j * PhiDOT * r * rDOT * + (147 * (1 + delta) * S1z + (-83 + 13 * delta) * Nu * S1z + + 147 * (-1 + delta) * S2z + (83 + 13 * delta) * Nu * S2z) + + params.rDOT2 * + ((105 * (1 + delta) - 4 * (13 + 15 * delta) * Nu) * S1z + + (-105 + 15 * delta * (7 - 4 * Nu) + 52 * Nu) * S2z) + + 4 * params.PhiDOT2 * params.r2 * + ((-21 - 21 * delta + 66 * Nu + 4 * delta * Nu) * S1z + + (21 - 21 * delta - 66 * Nu + 4 * delta * Nu) * S2z)))) + / params.r3 + ) + + elif vpnorder == 5: + term1 = ( + (0.0013227513227513227j * delta * mass * PhiDOT * + (10 * params.Mtot2 * (31 - 205 * Nu + 111 * params.eta2) - + 2 * mass * r * + ((-197 + 5 * Nu + 660 * params.eta2) * params.PhiDOT2 * params.r2 + + 1j * (-3167 - 5278 * Nu + 201 * params.eta2) * PhiDOT * r * rDOT + + 8 * (202 + 587 * Nu - 177 * params.eta2) * params.rDOT2) + + 3 * params.r2 * + ((152 - 692 * Nu + 333 * params.eta2) * params.PhiDOT4 * params.r4 + + 2j * (308 - 1607 * Nu + 111 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT - + 3 * (75 - 560 * Nu + 68 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 - + 2j * (-265 + 526 * Nu + 18 * params.eta2) * PhiDOT * r * params.rDOT3 + + (-241 + 550 * Nu - 264 * params.eta2) * params.rDOT4))) + / params.r2 + ) + + # Henry et al. ecc spin terms + term2 = ( + (params.Mtot3 * + (-4 * rDOT * + (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + + kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) - + 1j * PhiDOT * r * + ((-((1 + delta) * (9 + kappa1)) + + 2 * (9 + (4 + 3 * delta) * kappa1) * Nu) * params.S1z2 - + 12 * delta * Nu * S1z * S2z + + (9 + kappa2 - 2 * (9 + 4 * kappa2) * Nu + + delta * (-9 - kappa2 + 6 * kappa2 * Nu)) * params.S2z2))) + / (6.0 * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + (delta * params.Mtot2 * Nu * PhiDOT * + (mass * (195 * PhiDOT * r - 946j * rDOT) + + 9 * r * + (270 * params.PhiDOT3 * params.r3 - + 483j * params.PhiDOT2 * params.r2 * rDOT - + 580 * PhiDOT * r * params.rDOT2 + + 42j * params.rDOT3))) + / (315.0 * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.00033068783068783067j * params.Mtot2 * + (3 * params.r2 * + (4j * PhiDOT * r * params.rDOT3 * + ((-315 * (1 + delta) + 2 * (251 + 463 * delta) * Nu + + 4 * (15 + delta) * params.eta2) * S1z + + (315 - 315 * delta - 502 * Nu + 926 * delta * Nu + + 4 * (-15 + delta) * params.eta2) * S2z) + + 12 * params.PhiDOT2 * params.r2 * params.rDOT2 * + ((189 * (1 + delta) - 2 * (521 + 293 * delta) * Nu + + 7 * (55 + 23 * delta) * params.eta2) * S1z + + (189 * (-1 + delta) + 2 * (521 - 293 * delta) * Nu + + 7 * (-55 + 23 * delta) * params.eta2) * S2z) + + params.rDOT4 * + ((567 * (1 + delta) - 16 * (77 + 64 * delta) * Nu + + 8 * (177 + 173 * delta) * params.eta2) * S1z + + (567 * (-1 + delta) + 16 * (77 - 64 * delta) * Nu + + 8 * (-177 + 173 * delta) * params.eta2) * S2z) - + 4j * params.PhiDOT3 * params.r3 * rDOT * + ((936 * (1 + delta) - 5 * (979 + 215 * delta) * Nu + + 2 * (1353 + 293 * delta) * params.eta2) * S1z + + (936 * (-1 + delta) + 5 * (979 - 215 * delta) * Nu + + 2 * (-1353 + 293 * delta) * params.eta2) * S2z) + + 4 * params.PhiDOT4 * params.r4 * + ((-252 * (1 + delta) + (1315 + 857 * delta) * Nu + + 4 * (-285 + 43 * delta) * params.eta2) * S1z + + (252 + 5 * Nu * (-263 + 228 * Nu) + + delta * (-252 + Nu * (857 + 172 * Nu))) * S2z)) - + 2 * mass * r * + (-1j * PhiDOT * r * rDOT * + ((2043 * (1 + delta) + (37 + 2597 * delta) * Nu + + (10635 + 139 * delta) * params.eta2) * S1z + + (2043 * (-1 + delta) + (-37 + 2597 * delta) * Nu + + (-10635 + 139 * delta) * params.eta2) * S2z) + + params.PhiDOT2 * params.r2 * + ((-765 - Nu * (667 + 7773 * Nu) + + delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S1z + + (765 + Nu * (667 + 7773 * Nu) + + delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S2z) + + 4 * params.rDOT2 * + ((-234 * (1 + delta) - 4 * (560 + 901 * delta) * Nu + + (483 + 1111 * delta) * params.eta2) * S1z + + (234 + 7 * (320 - 69 * Nu) * Nu + + delta * (-234 + Nu * (-3604 + 1111 * Nu))) * S2z)) + + 2 * params.Mtot2 * + (1134 * kappa1 * (-1 - delta + (3 + delta) * Nu) * params.S1z3 + + 1134 * (1 + delta) * (-2 + kappa1) * Nu * params.S1z2 * S2z + + S1z * + (-5661 - 5661 * delta - 17156 * Nu - 9172 * delta * Nu + + 231 * params.eta2 + 775 * delta * params.eta2 + + 1134 * (-1 + delta) * (-2 + kappa2) * Nu * params.S2z2) + + S2z * (5661 - 5661 * delta + 17156 * Nu - 9172 * delta * Nu - + 231 * params.eta2 + 775 * delta * params.eta2 + + 1134 * kappa2 * (1 - delta + (-3 + delta) * Nu) * + params.S2z2))) + ) / params.r4 + + + return term1 + term2 + + elif vpnorder == 7: + + spin_terms = ( + -23760 * params.Mtot3 * + (params.PhiDOT3 * params.r4 * + (-12j * (-35 + 107 * Nu) * S1z + + 5 * (126 - 462 * Nu + 80 * params.eta2 + + kappa1 * (-2 - 79 * Nu + 153 * params.eta2)) * params.S1z2 + + S2z * (-420j + 1284j * Nu + + 10 * (-63 + kappa2) * S2z + + 5 * (462 + 79 * kappa2) * Nu * S2z - + 5 * (80 + 153 * kappa2) * params.eta2 * S2z)) - + 1j * params.PhiDOT2 * params.r3 * rDOT * + (-24j * (-35 + 202 * Nu) * S1z + + 5 * (-14 * Nu * (3 + 58 * Nu) + + kappa1 * (-409 + 1096 * Nu + 6 * params.eta2)) * params.S1z2 + + S2z * (-840j + 2045 * kappa2 * S2z + + 10 * (406 - 3 * kappa2) * params.eta2 * S2z + + Nu * (4848j + 210 * S2z - 5480 * kappa2 * S2z))) - + 4j * r * params.rDOT3 * + (3j * (26 + 57 * Nu) * S1z + + 5 * (4 * params.eta2 + + kappa1 * (-7 + 49 * Nu + 15 * params.eta2)) * params.S1z2 - + S2z * (78j - 35 * kappa2 * S2z + + 5 * (4 + 15 * kappa2) * params.eta2 * S2z + + Nu * (171j + 245 * kappa2 * S2z))) + + 2j * mass * rDOT * + (-4j * (-78 + 769 * Nu) * S1z + + 5 * ((14 - 59 * Nu) * Nu + + kappa1 * (-70 - 77 * Nu + 18 * params.eta2)) * params.S1z2 + + S2z * (-312j + 350 * kappa2 * S2z + + 5 * (59 - 18 * kappa2) * params.eta2 * S2z + + Nu * (3076j - 70 * S2z + 385 * kappa2 * S2z))) + + PhiDOT * params.r2 * params.rDOT2 * + (6j * (-62 + 219 * Nu) * S1z - + 5 * (189 - 756 * Nu + 388 * params.eta2 + + kappa1 * (98 - 266 * Nu + 276 * params.eta2)) * params.S1z2 + + S2z * (372j + 945 * S2z + 490 * kappa2 * S2z + + 20 * (97 + 69 * kappa2) * params.eta2 * S2z - + 2 * Nu * (657j + 35 * (54 + 19 * kappa2) * S2z))) + + mass * PhiDOT * r * + (8j * (-61 + 480 * Nu) * S1z + + 5 * (-392 + 448 * Nu + 474 * params.eta2 + + kappa1 * (-11 + 150 * Nu + 58 * params.eta2)) * params.S1z2 - + S2z * (-488j - 5 * (392 + 11 * kappa2) * S2z + + 10 * (237 + 29 * kappa2) * params.eta2 * S2z + + 10 * Nu * (384j + (224 + 75 * kappa2) * S2z)))) + ) + + orbital_terms = ( + delta * mass * + (-240 * mass * PhiDOT * params.r3 * + ((197936 - 139360 * Nu - 367105 * params.eta2 + + 253245 * params.eta3) * params.PhiDOT4 * params.r4 + + 1j * (279236 - 483940 * Nu - 2817805 * params.eta2 + + 459180 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT - + 6 * (38627 + 89295 * Nu - 492740 * params.eta2 + + 75975 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 - + 1j * (-731008 + 2287930 * Nu + 981060 * params.eta2 + + 10275 * params.eta3) * PhiDOT * r * params.rDOT3 + + (-327667 + 436705 * Nu + 659790 * params.eta2 - + 438255 * params.eta3) * params.rDOT4) + + 900 * PhiDOT * params.r4 * + (2 * (-2594 + 27609 * Nu - 74032 * params.eta2 + + 25974 * params.eta3) * params.PhiDOT6 * params.r6 + + 4j * (-5730 + 58833 * Nu - 137842 * params.eta2 + + 17123 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + + 2 * (-114 - 41622 * Nu + 147569 * params.eta2 + + 4196 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + + 4j * (-9554 + 70788 * Nu - 156227 * params.eta2 + + 5810 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + + (17619 - 138450 * Nu + 322600 * params.eta2 - + 80816 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - + 2j * (8793 - 52230 * Nu + 69340 * params.eta2 + + 2536 * params.eta3) * PhiDOT * r * params.rDOT5 + + 2 * (3957 - 24534 * Nu + 42584 * params.eta2 - + 20800 * params.eta3) * params.rDOT6) - + 2 * params.Mtot3 * + (-23760j * rDOT * + (5 * (Nu * (-14 + 31 * Nu) + 7 * kappa1 * (10 + 31 * Nu)) * params.S1z2 + + 2 * S1z * (-156j + 155 * params.eta2 * S2z + + 2 * Nu * (613j + 390 * S2z)) + + S2z * (-312j + 350 * kappa2 * S2z + + 155 * params.eta2 * S2z + + Nu * (2452j + 35 * (-2 + 31 * kappa2) * S2z))) + + PhiDOT * r * + (8946400 * params.eta3 - + 8 * (6991786 + 724680j * S1z + + 7425 * (392 + 11 * kappa1) * params.S1z2 + + 724680j * S2z + + 7425 * (392 + 11 * kappa2) * params.S2z2) - + 3600 * params.eta2 * + (-628 + 33 * (-19 + 92 * kappa1) * params.S1z2 - + 7326 * S1z * S2z + + 33 * (-19 + 92 * kappa2) * params.S2z2) + + 15 * Nu * + (994455 * M_PI2 + + 8 * (-2249485 + + 7920 * (-21 + 8 * kappa1) * params.S1z2 + + 283536j * S2z + + 7920 * (-21 + 8 * kappa2) * params.S2z2 - + 1584 * S1z * (-179j + 170 * S2z))))) + + 3 * params.Mtot2 * r * + (31680j * params.rDOT3 * + (5 * (4 * params.eta2 + 7 * kappa1 * (-1 + 5 * Nu)) * params.S1z2 + + S1z * (78j + 40 * params.eta2 * S2z + + Nu * (327j + 420 * S2z)) + + S2z * (78j - 35 * kappa2 * S2z + + 20 * params.eta2 * S2z + + Nu * (327j + 175 * kappa2 * S2z))) - + 22 * PhiDOT * r * params.rDOT2 * + (2553200 * params.eta3 - + 24 * (268267 + 5580j * S1z + + 525 * (27 + 14 * kappa1) * params.S1z2 + + 5580j * S2z + + 525 * (27 + 14 * kappa2) * params.S2z2) - + 200 * params.eta2 * + (39445 + 72 * (-4 + 21 * kappa1) * params.S1z2 - + 3600 * S1z * S2z + + 72 * (-4 + 21 * kappa2) * params.S2z2) + + 25 * Nu * + (23247 * M_PI2 + + 8 * (-69259 + 1026j * S1z + + 126 * (27 + 5 * kappa1) * params.S1z2 + + 1026j * S2z + + 126 * (27 + 5 * kappa2) * params.S2z2))) + + params.PhiDOT3 * params.r3 * + (10071200 * params.eta3 + + 96 * (-421183 - 34650j * S1z + + 825 * (-63 + kappa1) * params.S1z2 - + 34650j * S2z + + 825 * (-63 + kappa2) * params.S2z2) - + 400 * params.eta2 * + (64177 + 792 * (-5 + 6 * kappa1) * params.S1z2 - + 17424 * S1z * S2z + + 792 * (-5 + 6 * kappa2) * params.S2z2) + + 15 * Nu * + (426195 * M_PI2 + + 8 * (-509635 + + 330 * (210 + 83 * kappa1) * params.S1z2 + + 29304j * S2z + + 330 * (210 + 83 * kappa2) * params.S2z2 - + 792 * S1z * (-37j + 70 * S2z)))) - + 2j * params.PhiDOT2 * params.r2 * rDOT * + (-8330400 * params.eta3 + + 8 * (-2810116 - 415800j * S1z + + 1012275 * kappa1 * params.S1z2 - + 415800j * S2z + + 1012275 * kappa2 * params.S2z2) + + 4800 * params.eta2 * + (13411 + 33 * (19 + 12 * kappa1) * params.S1z2 + + 462 * S1z * S2z + + 33 * (19 + 12 * kappa2) * params.S2z2) + + 5 * Nu * + (1278585 * M_PI2 - + 8 * (5139685 + + 990 * (-21 + 139 * kappa1) * params.S1z2 - + 313632j * S2z + + 990 * (-21 + 139 * kappa2) * params.S2z2 - + 3564 * S1z * (88j + 185 * S2z)))))) + ) + + log_term = ( + -13559040 * delta * params.Mtot3 * PhiDOT * r * + (2 * mass - 3 * params.PhiDOT2 * params.r3 + + 6j * PhiDOT * params.r2 * rDOT + + 6 * r * params.rDOT2) * + np.log(r / r0) + ) + + return ( + -1.0020843354176688e-7j * + (spin_terms + orbital_terms + log_term) + ) / params.r4 + + else: + return 0.0 + 0.0j + +def hQC_2_m_1( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + + delta: float = np.sqrt(1.0 - 4.0 * Nu) + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + r0: float = b0 + + # Common log terms to simplify the messy 4PN-7PN expressions + log_term_base = (-7.0 + 6.0 * EulerGamma - 3j * M_PI + np.log(64.0) + 6.0 * np.log(b0) + 9.0 * np.log(x)) + + if vpnorder == 4: + return (-2.0 * delta * params.x3 * log_term_base) / 9.0 + + elif vpnorder == 5: + spin_factor = ((1.0 + delta) * S1z + (-1.0 + delta) * S2z) + return (spin_factor * params.x3p5 * log_term_base) / 6.0 + + elif vpnorder == 6: + return -0.007936507936507936 * (delta * (-17.0 + 6.0 * Nu) * params.x4 * log_term_base) + + elif vpnorder == 7: + # Heavily nested 3.5PN (order 7) term + term1 = (params.x4p5 * ( + -98 * S1z + 84 * EulerGamma * S1z + 3017 * Nu * S1z - 2586 * EulerGamma * Nu * S1z - + 42j * M_PI * S1z + 1293j * Nu * M_PI * S1z + 98 * S2z - 84 * EulerGamma * S2z - + 3017 * Nu * S2z + 2586 * EulerGamma * Nu * S2z + 42j * M_PI * S2z - 1293j * Nu * M_PI * S2z + + 84 * S1z * np.log(2) - 2586 * Nu * S1z * np.log(2) - 84 * S2z * np.log(2) + 2586 * Nu * S2z * np.log(2) + + 84 * S1z * np.log(b0) - 2586 * Nu * S1z * np.log(b0) - 84 * S2z * np.log(b0) + 2586 * Nu * S2z * np.log(b0) + + 126 * S1z * np.log(x) - 3879 * Nu * S1z * np.log(x) - 126 * S2z * np.log(x) + 3879 * Nu * S2z * np.log(x) + + 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 * Nu * S1z) / 12.0 - (29 * EulerGamma * Nu * S1z) / 14.0 - + 0.16666666666666666j * M_PI * S1z + 1.0357142857142858j * Nu * M_PI * S1z - + (7 * S2z) / 18.0 + (EulerGamma * S2z) / 3.0 + (29 * Nu * S2z) / 12.0 - (29 * EulerGamma * Nu * S2z) / 14.0 - + 0.16666666666666666j * M_PI * S2z + 1.0357142857142858j * Nu * M_PI * S2z + + 1.7523809523809524j * np.log(2) - 2.6666666666666665j * EulerGamma * np.log(2) - + (4 * M_PI * np.log(2)) / 3.0 + (S1z * np.log(2)) / 3.0 - (29 * Nu * S1z * np.log(2)) / 14.0 + + (S2z * np.log(2)) / 3.0 - (29 * Nu * S2z * np.log(2)) / 14.0 - 1.3333333333333333j * np.log(2)**2 - + 1.3333333333333333j * np.log(b0)**2 - 1.3587301587301588j * np.log(r0) + + (np.log(b0) * (392j - 336j * EulerGamma - 168 * M_PI + 42 * S1z - 261 * Nu * S1z + 42 * S2z - 261 * Nu * S2z - 336j * np.log(2) - 504j * np.log(x))) / 126.0 + + 2.6285714285714286j * np.log(x) - 4j * EulerGamma * np.log(x) - 2 * M_PI * np.log(x) + + (S1z * np.log(x)) / 2.0 - (87 * Nu * S1z * np.log(x)) / 28.0 + (S2z * np.log(x)) / 2.0 - + (87 * Nu * S2z * np.log(x)) / 28.0 - 4j * np.log(2) * np.log(x) - 3j * np.log(x)**2 + ) + )) / 252.0 + return term1 + + else: + return 0.0 + 0.0j + +def hl_2_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_2_m_1: Input PN order parameter should be between [0, 8]." + ) + + # Calculate the pre-factor: (4 * M * eta * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + go_component: complex = hGO_2_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_2_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + phase_factor: complex = np.exp(-1j * Phi) + + # Combine terms + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_2_m_min1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_2_m_min1: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # These are the same functions used for the m=1 mode + go_component: complex = hGO_2_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_2_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # In C: conj(hGO + hQC) + # In Python: use the .conjugate() method or np.conj() + amplitude_term: complex = (go_component + qc_component).conjugate() + + # In C: cpolar(1, 1 * Phi) -> exp(i * Phi) + # Note the sign change from the m=1 mode + phase_factor: complex = np.exp(1j * Phi) + + return pre_factor * amplitude_term * phase_factor + +# H33 + +def hGO_3_m_3( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + r0 = 2 * mass / np.exp(0.5) + + 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_d2 = combination_d ** 2 + combination_d5 = combination_d ** 5 + + combination_e = 1j * PhiDOT * r + rDOT + combination_e3 = combination_e ** 3 + + if vpnorder == 1: + return ( + (np.sqrt(0.11904761904761904) * delta * + (2 * r * combination_a3 + + mass * (-7j * PhiDOT * r + 4 * rDOT))) + / (2.0 * r) + ) + + elif vpnorder == 3: + return ( + (np.sqrt(0.11904761904761904) * delta * + (6 * (-5 + 19 * Nu) * params.r2 * combination_b4 * + (1j * PhiDOT * r + rDOT) + + 2 * params.Mtot2 * + (-3j * (-101 + 43 * Nu) * PhiDOT * r + + (-109 + 86 * Nu) * rDOT) + + 3 * mass * r * + (-12j * (1 + 4 * Nu) * params.PhiDOT3 * params.r3 + + 6 * (14 + 31 * Nu) * params.PhiDOT2 * params.r2 * rDOT + + 3j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 - + 4 * (8 + 17 * Nu) * params.rDOT3))) + / (36.0 * params.r2) + ) + + elif vpnorder == 4: + return ( + (-0.125j * np.sqrt(0.11904761904761904) * params.Mtot2 * + (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) + + r * (2 * params.rDOT2 * + (6 * (1 + delta) * S1z - 5 * (5 + 3 * delta) * Nu * S1z + + (-6 + delta * (6 - 15 * Nu) + 25 * Nu) * S2z) + + params.PhiDOT2 * params.r2 * + (-24 * (1 + delta) * S1z + (119 + 33 * delta) * Nu * S1z + + (24 - 119 * Nu + 3 * delta * (-8 + 11 * Nu)) * S2z) + + 2j * PhiDOT * r * rDOT * + (-18 * (1 + delta) * S1z + (77 + 39 * delta) * Nu * S1z + + (18 - 77 * Nu + 3 * delta * (-6 + 13 * Nu)) * S2z)))) + / params.r3 + ) + + elif vpnorder == 5: + term1 = ( + delta * + (30 * (183 - 1579 * Nu + 3387 * params.eta2) * params.r3 * + combination_c2 * combination_d5 + + 10 * params.Mtot3 * + (-1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r + + 4 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + + 2 * params.Mtot2 * r * + (-11j * (-5353 - 13493 * Nu + 4671 * params.eta2) * params.PhiDOT3 * params.r3 + + (-75243 - 142713 * Nu + 192821 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT + + 220j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 - + 10 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + + 3 * mass * params.r2 * + (2j * (-7633 + 9137 * Nu + 28911 * params.eta2) * params.PhiDOT5 * params.r5 - + 4 * (-8149 + 1576 * Nu + 43533 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT - + 2j * (-9297 - 19517 * Nu + 64839 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - + 32 * (-1288 + 3667 * Nu + 4056 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 - + 5j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 + + 20 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) + / (1584.0 * np.sqrt(210) * params.r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.125j * np.sqrt(1.0714285714285714) * params.Mtot3 * + (7 * PhiDOT * r + 2j * rDOT) * + (kappa1 * (-1 - delta + 2 * (2 + delta) * Nu) * params.S1z2 + + S2z * (-4 * delta * Nu * S1z + + kappa2 * (1 - delta + 2 * (-2 + delta) * Nu) * S2z)) + / params.r3 + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + -(delta * params.Mtot2 * Nu * + (668 * params.Mtot2 + + 2 * mass * r * + (4081 * params.PhiDOT2 * params.r2 + + 297j * PhiDOT * r * rDOT - 452 * params.rDOT2) + + 5 * params.r2 * + (1329 * params.PhiDOT4 * params.r4 - + 2926j * params.PhiDOT3 * params.r3 * rDOT - + 384 * params.PhiDOT2 * params.r2 * params.rDOT2 - + 408j * PhiDOT * r * params.rDOT3 + + 200 * params.rDOT4))) + / (36.0 * np.sqrt(210) * params.r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + -0.006944444444444444j * params.Mtot2 * + (10 * params.Mtot2 * + ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + + 8 * (12 + 47 * delta) * params.eta2) * S1z + + (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + + 8 * (-12 + 47 * delta) * params.eta2) * S2z) + + 2 * mass * r * + (2 * params.PhiDOT2 * params.r2 * + ((1320 * (1 + delta) - 2 * (4469 + 211 * delta) * Nu + + (8709 + 2777 * delta) * params.eta2) * S1z + + (1320 * (-1 + delta) + 8938 * Nu - 422 * delta * Nu + + (-8709 + 2777 * delta) * params.eta2) * S2z) + + 3j * PhiDOT * r * rDOT * + ((2000 * (1 + delta) - (9147 + 3173 * delta) * Nu + + (8911 + 5273 * delta) * params.eta2) * S1z + + (2000 * (-1 + delta) + (9147 - 3173 * delta) * Nu + + (-8911 + 5273 * delta) * params.eta2) * S2z) + + 10 * params.rDOT2 * + ((-105 * (1 + delta) + (541 + 77 * delta) * Nu - + 2 * (462 + 247 * delta) * params.eta2) * S1z + + (105 + Nu * (-541 + 924 * Nu) + + delta * (-105 + (77 - 494 * Nu) * Nu)) * S2z)) - + 3 * params.r2 * + (-3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + ((480 * (1 + delta) - (1711 + 1889 * delta) * Nu + + 2 * (-1161 + 757 * delta) * params.eta2) * S1z + + (480 * (-1 + delta) + (1711 - 1889 * delta) * Nu + + 2 * (1161 + 757 * delta) * params.eta2) * S2z) + + 2 * params.PhiDOT4 * params.r4 * + ((350 * (1 + delta) - 4 * (404 + 461 * delta) * Nu + + (883 + 769 * delta) * params.eta2) * S1z + + (350 * (-1 + delta) + 4 * (404 - 461 * delta) * Nu + + (-883 + 769 * delta) * params.eta2) * S2z) + + 2j * params.PhiDOT3 * params.r3 * rDOT * + ((660 * (1 + delta) - (4061 + 2899 * delta) * Nu + + (2643 + 4789 * delta) * params.eta2) * S1z + + (660 * (-1 + delta) + (4061 - 2899 * delta) * Nu + + (-2643 + 4789 * delta) * params.eta2) * S2z) + + 10 * params.rDOT4 * + ((-30 * (1 + delta) + (187 + 101 * delta) * Nu - + 2 * (159 + 61 * delta) * params.eta2) * S1z + + (30 + Nu * (-187 + 318 * Nu) + + delta * (-30 + (101 - 122 * Nu) * Nu)) * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + ((90 + Nu * (-1321 + 5118 * Nu) + + delta * (90 + Nu * (-319 + 714 * Nu))) * S1z + + (-90 + (1321 - 5118 * Nu) * Nu + + delta * (90 + Nu * (-319 + 714 * Nu))) * S2z))) + / (np.sqrt(210) * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + M_PI2 = M_PI ** 2 + + spin_terms = ( + 504504 * params.Mtot3 * + (2 * mass * + (rDOT * + (S1z * (108 - 498 * Nu + + 5j * (-24 - 5 * kappa1 + 3 * (76 + kappa1) * Nu + + 4 * (-111 + 13 * kappa1) * params.eta2) * S1z) + + 6 * (-18 + 83 * Nu) * S2z - + 5j * (-24 - 5 * kappa2 + 3 * (76 + kappa2) * Nu + + 4 * (-111 + 13 * kappa2) * params.eta2) * params.S2z2) + + PhiDOT * r * + (S1z * (-3j * (-99 + 581 * Nu) + + 5 * (-24 + 399 * kappa1 + 48 * (7 - 19 * Nu) * Nu + + kappa1 * Nu * (-1629 + 188 * Nu)) * S1z) + + 3j * (-99 + 581 * Nu) * S2z - + 5 * (-24 + 399 * kappa2 + 48 * (7 - 19 * Nu) * Nu + + kappa2 * Nu * (-1629 + 188 * Nu)) * params.S2z2)) + + r * (3 * PhiDOT * r * params.rDOT2 * + (S1z * (216j + 545 * kappa1 * S1z + + 40 * (45 + 8 * kappa1) * params.eta2 * S1z - + 30 * Nu * (50j + 20 * S1z + 73 * kappa1 * S1z)) + + 12j * (-18 + 125 * Nu) * S2z - + 5 * (109 * kappa2 - 6 * (20 + 73 * kappa2) * Nu + + 8 * (45 + 8 * kappa2) * params.eta2) * params.S2z2) + + 2 * params.rDOT3 * + (S1z * (-54 + 145j * kappa1 * S1z - + 30j * Nu * (11j + 2 * Nu * S1z + + kappa1 * (17 + 8 * Nu) * S1z)) + + 6 * (9 - 55 * Nu) * S2z + + 5j * (12 * params.eta2 + + kappa2 * (-29 + 6 * Nu * (17 + 8 * Nu))) * params.S2z2) + + 6 * params.PhiDOT2 * params.r2 * rDOT * + (S1z * (297 - 465 * Nu + + 5j * (6 * (20 - 87 * Nu) * Nu + + kappa1 * (-50 + 3 * Nu * (47 + 76 * Nu))) * S1z) + + 3 * (-99 + 155 * Nu) * S2z - + 5j * (6 * (20 - 87 * Nu) * Nu + + kappa2 * (-50 + 3 * Nu * (47 + 76 * Nu))) * params.S2z2) + + params.PhiDOT3 * params.r3 * + (3j * (531 - 1295 * Nu) * S1z + + 10 * (-33 * kappa1 + 6 * (30 + 13 * kappa1) * Nu + + 4 * (-96 + 67 * kappa1) * params.eta2) * params.S1z2 + + S2z * (-1593j + 330 * kappa2 * S2z + + 5 * Nu * (777j - + 4 * (90 + 39 * kappa2 - 192 * Nu + + 134 * kappa2 * Nu) * S2z))))) + ) + + orbital_terms = ( + delta * + (-17640 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * + params.r4 * combination_b6 * combination_e3 + + 168 * params.Mtot2 * params.r2 * + (1j * (-7508635 + 7 * Nu * (-1318438 + Nu * (-10231834 + 9667755 * Nu))) * + params.PhiDOT5 * params.r5 + + 7 * (1235591 + Nu * (884445 + (23935218 - 26913443 * Nu) * Nu)) * + params.PhiDOT4 * params.r4 * rDOT - + 1j * (8961149 + 7 * Nu * (-31755709 + Nu * (-11134798 + 22187331 * Nu))) * + params.PhiDOT3 * params.r3 * params.rDOT2 - + (-36806435 + 7 * Nu * (33178545 + Nu * (24565078 + 22873537 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT3 - + 5j * (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * + PhiDOT * r * params.rDOT4 + + 5 * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * + params.rDOT5) + + 1764 * mass * params.r3 * + (-2j * (239087 + Nu * (-1206515 + Nu * (422631 + 3979375 * Nu))) * + params.PhiDOT7 * params.r7 + + 2 * (621284 + Nu * (-2279907 + 2 * Nu * (-1180187 + 5876531 * Nu))) * + params.PhiDOT6 * params.r6 * rDOT + + 2j * (39270 + Nu * (1235486 - 5319747 * Nu + 4406349 * params.eta2)) * + params.PhiDOT5 * params.r5 * params.rDOT2 + + 8 * (349111 + 4 * Nu * (-519370 + 33 * Nu * (10939 + 42635 * Nu))) * + params.PhiDOT4 * params.r4 * params.rDOT3 + + 2j * (1212607 + 3 * Nu * (-2012698 - 67827 * Nu + 7955628 * params.eta2)) * + params.PhiDOT3 * params.r3 * params.rDOT4 + + 4 * (201135 + 2 * Nu * (-773107 + Nu * (1214819 + 1157652 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT5 + + 5j * (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * + PhiDOT * r * params.rDOT6 - + 40 * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * + params.rDOT7) + + 2 * params.Mtot4 * + (4 * rDOT * + (269279500 * params.eta3 + + 2 * (-174108226 + + 63063 * S1z * (108j + 5 * (24 + 5 * kappa1) * S1z) + + 63063 * S2z * (108j + 5 * (24 + 5 * kappa2) * S2z)) - + 21 * Nu * + (103100846 + 1846845 * M_PI2 + 1693692j * S2z - + 6006 * (S1z * (-282j + 5 * (-180 + 7 * kappa1) * S1z) - + 980 * S1z * S2z + + 5 * (-180 + 7 * kappa2) * params.S2z2)) - + 2940 * params.eta2 * + (-122855 + + 4719 * ((-6 + 7 * kappa1) * params.S1z2 - + 26 * S1z * S2z + + (-6 + 7 * kappa2) * params.S2z2))) + + 1j * PhiDOT * r * + (-1176172480 * params.eta3 + + 8 * (-74084729 + + 189189 * S1z * (99j + 5 * (-8 + 133 * kappa1) * S1z) + + 189189 * S2z * (99j + 5 * (-8 + 133 * kappa2) * S2z)) + + 35280 * params.eta2 * + (56255 + + 429 * ((-22 + 65 * kappa1) * params.S1z2 - + 174 * S1z * S2z + + (-22 + 65 * kappa2) * params.S2z2)) - + 147 * Nu * + (-65012788 + 4485195 * M_PI2 + 3943368j * S2z + + 10296 * (S1z * (383j + 5 * (-96 + 277 * kappa1) * S1z) - + 3220 * S1z * S2z + + 5 * (-96 + 277 * kappa2) * params.S2z2)))) + + params.Mtot3 * r * + (-12j * PhiDOT * r * params.rDOT2 * + (-1035895280 * params.eta3 - + 2 * (-547993687 + + 63063 * S1z * (216j + 545 * kappa1 * S1z) + + 63063 * S2z * (216j + 545 * kappa2 * S2z)) + + 77 * Nu * + (42451610 + 1511055 * M_PI2 + 1749384j * S2z + + 6552 * (S1z * (267j + 25 * (6 + 11 * kappa1) * S1z) - + 5 * S1z * S2z + + 25 * (6 + 11 * kappa2) * params.S2z2)) + + 490 * params.eta2 * + (-5802767 + + 5148 * ((-6 + 23 * kappa1) * params.S1z2 - + 58 * S1z * S2z + + (-6 + 23 * kappa2) * params.S2z2))) + + 4 * params.rDOT3 * + (-1359334480 * params.eta3 - + 4 * (-150254558 + + 63063 * S1z * (54j + 145 * kappa1 * S1z) + + 63063 * S2z * (54j + 145 * kappa2 * S2z)) + + 231 * Nu * + (8490448 + 503685 * M_PI2 + 242424j * S2z + + 2184 * (S1z * (111j + 110 * kappa1 * S1z) + + 70 * S1z * S2z + + 110 * kappa2 * params.S2z2)) + + 11760 * params.eta2 * + (-312980 + + 429 * ((3 + 25 * kappa1) * params.S1z2 - + 44 * S1z * S2z + + (3 + 25 * kappa2) * params.S2z2))) + + 6 * params.PhiDOT2 * params.r2 * rDOT * + (2368900688 * params.eta3 + + 8 * (-812986529 + + 63063 * S1z * (297j + 250 * kappa1 * S1z) + + 63063 * S2z * (297j + 250 * kappa2 * S2z)) - + 1176 * params.eta2 * + (2423171 + + 4290 * ((-3 + 41 * kappa1) * params.S1z2 - + 88 * S1z * S2z + + (-3 + 41 * kappa2) * params.S2z2)) + + 539 * Nu * + (-24139772 + 647595 * M_PI2 + 120744j * S2z - + 936 * (S1z * (-129j + 600 * S1z + 205 * kappa1 * S1z) - + 460 * S1z * S2z + + 5 * (120 + 41 * kappa2) * params.S2z2))) + + 1j * params.PhiDOT3 * params.r3 * + (-4538040136 * params.eta3 - + 88 * (259018351 + + 17199 * S1z * (-531j + 110 * kappa1 * S1z) + + 17199 * S2z * (-531j + 110 * kappa2 * S2z)) + + 2352 * params.eta2 * + (7332973 + + 12870 * ((5 + 23 * kappa1) * params.S1z2 - + 36 * S1z * S2z + + (5 + 23 * kappa2) * params.S2z2)) + + 21 * Nu * + (49864815 * M_PI2 + + 8 * (-88128538 - 2099097j * S2z + + 9009 * (S1z * (-233j + 40 * (15 + kappa1) * S1z) + + 360 * S1z * S2z + + 40 * (15 + kappa2) * params.S2z2)))))) + ) + + log_term = ( + 74954880 * delta * params.Mtot3 * + (22j * mass * PhiDOT * r + + 59j * params.PhiDOT3 * params.r4 + 8 * mass * rDOT + + 66 * params.PhiDOT2 * params.r3 * rDOT + + 24j * PhiDOT * params.r2 * params.rDOT2 - + 4 * r * params.rDOT3) * + np.log(r / r0) + ) + + return ( + 1j * (spin_terms + orbital_terms + log_term) + / (2.4216192e7 * np.sqrt(210) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_3_m_3( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + + delta: float = np.sqrt(1.0 - 4.0 * Nu) + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + r0: float = b0 + + if vpnorder == 4: + return ( + (3.0 * np.sqrt(0.04285714285714286) * delta * params.x3 * ( + -97.0 + 60.0 * EulerGamma - 30.0j * M_PI + + 60.0 * np.log(2.0) + 60.0 * np.log(3.0) + + 60.0 * np.log(b0) + 90.0 * np.log(x) + )) / 4.0 + ) + + elif vpnorder == 6: + numerator_6 = (delta * params.x4 * ( + 188568.0 - 116640.0 * EulerGamma - 70537.0 * Nu + 43740.0 * EulerGamma * Nu + + 58320.0j * M_PI - 21870.0j * Nu * M_PI - + 116640.0 * np.log(2.0) + 43740.0 * Nu * np.log(2.0) - + 116640.0 * np.log(3.0) + 43740.0 * Nu * np.log(3.0) + + 14580.0 * (-8.0 + 3.0 * Nu) * np.log(b0) + + 21870.0 * (-8.0 + 3.0 * Nu) * np.log(x) + )) + 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 * np.log(b0) * ( + -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * M_PI + + 5.0 * (-4.0 - 4.0 * delta + 19.0 * Nu + 5.0 * delta * Nu) * S1z + + 20.0 * S2z - 20.0 * delta * S2z - 95.0 * Nu * S2z + 25.0 * delta * Nu * S2z + + 120.0j * delta * np.log(2.0) + 120.0j * delta * np.log(3.0) + + 180.0j * delta * np.log(x) + ) + + numerator_7 = (params.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 * Nu * S1z - 643223.0 * delta * Nu * S1z + 1508220.0 * EulerGamma * Nu * S1z + + 396900.0 * delta * EulerGamma * Nu * S1z + 158760.0j * M_PI * S1z + 158760.0j * delta * M_PI * S1z - + 754110.0j * Nu * M_PI * S1z - 198450.0j * delta * Nu * M_PI * S1z - + 513324.0 * S2z + 513324.0 * delta * S2z + 317520.0 * EulerGamma * S2z - + 317520.0 * delta * EulerGamma * S2z + 2439857.0 * Nu * S2z - 643223.0 * delta * Nu * S2z - + 1508220.0 * EulerGamma * Nu * S2z + 396900.0 * delta * EulerGamma * Nu * S2z - + 158760.0j * M_PI * S2z + 158760.0j * delta * M_PI * S2z + + 754110.0j * Nu * M_PI * S2z - 198450.0j * delta * Nu * M_PI * S2z - + 2490264.0j * delta * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + + 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - + 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * Nu * S1z * np.log(2.0) + + 396900.0 * delta * Nu * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - + 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * Nu * S2z * np.log(2.0) + + 396900.0 * delta * Nu * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - + 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + + 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - + 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + + 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - + 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * Nu * S2z * np.log(3.0) + + 396900.0 * delta * Nu * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + + 952560.0j * delta * (np.log(3.0)**2) + 952560.0j * delta * (np.log(b0)**2) + + 589680.0j * delta * np.log(r0) - 3735396.0j * delta * np.log(x) + + 2857680.0j * delta * EulerGamma * np.log(x) + 1428840.0 * delta * M_PI * np.log(x) - + 476280.0 * S1z * np.log(x) - 476280.0 * delta * S1z * np.log(x) + + 2262330.0 * Nu * S1z * np.log(x) + 595350.0 * delta * Nu * S1z * np.log(x) + + 476280.0 * S2z * np.log(x) - 476280.0 * delta * S2z * np.log(x) - + 2262330.0 * Nu * S2z * np.log(x) + 595350.0 * delta * Nu * S2z * np.log(x) + + 2857680.0j * delta * np.log(2.0) * np.log(x) + + 2857680.0j * delta * np.log(3.0) * np.log(x) + + 2143260.0j * delta * (np.log(x)**2) + + log_b0_term + )) + return numerator_7 / (2352.0 * np.sqrt(210.0)) + + else: + return 0.0 + 0.0j + +def hl_3_m_3( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R + # Note: This specific pre-factor is identical across several modes in the C source + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + go_component: complex = hGO_3_m_3( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_3( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) + # The higher 'm' index means the phase evolves 3 times faster than the orbit + phase_factor: complex = np.exp(-3j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_3_m_min3( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R + # Note: This specific pre-factor is identical across several modes in the C source + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # We assume hGO_3_m_3 and hQC_3_m_3 are defined elsewhere in your package + go_component: complex = hGO_3_m_3( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_3( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + # In C: conj(hGO + hQC) + # In Python: use the .conjugate() method or np.conj() + amplitude_term: complex = (go_component + qc_component).conjugate() + + # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) + # The higher 'm' index means the phase evolves 3 times faster than the orbit + phase_factor: complex = np.exp(3j * Phi) + + return pre_factor * amplitude_term * phase_factor + +# H32 + +def hGO_3_m_2( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + + if vpnorder == 2: + return ( + -(np.sqrt(0.7142857142857143) * mass * (-1 + 3 * Nu) * PhiDOT * + (4 * PhiDOT * r + 1j * rDOT)) + / 6.0 + ) + + elif vpnorder == 3: + return ( + (np.sqrt(0.7142857142857143) * params.Mtot2 * Nu * + (4 * PhiDOT * r + 1j * rDOT) * (S1z + S2z)) + / (3.0 * params.r2) + ) + + elif vpnorder == 4: + return ( + -(mass * PhiDOT * + (2 * mass * + ((167 - 925 * Nu + 1615 * params.eta2) * PhiDOT * r + + 5j * (-82 + 239 * Nu + 55 * params.eta2) * rDOT) - + 3 * r * + (2 * (-13 - 25 * Nu + 355 * params.eta2) * params.PhiDOT3 * params.r3 - + 60j * (-8 + 25 * Nu + params.eta2) * params.PhiDOT2 * params.r2 * rDOT + + 12 * (-23 + 70 * Nu + 10 * params.eta2) * PhiDOT * r * params.rDOT2 + + 5j * (-13 + 38 * Nu + 10 * params.eta2) * params.rDOT3))) + / (108.0 * np.sqrt(35) * r) + ) + + elif vpnorder == 5: + term1 = ( + (params.Mtot2 * Nu * PhiDOT * + (7j * mass + + r * (49j * params.PhiDOT2 * params.r2 + + 90 * PhiDOT * r * rDOT - 6j * params.rDOT2))) + / (4.0 * np.sqrt(35) * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + (np.sqrt(0.7142857142857143) * params.Mtot2 * + (2j * mass * rDOT * + ((-12 + Nu * (97 + 4 * Nu) + delta * (-12 + 5 * Nu)) * S1z + + (-12 + delta * (12 - 5 * Nu) + Nu * (97 + 4 * Nu)) * S2z) + + 4 * mass * PhiDOT * r * + (-((12 + delta * (12 - 23 * Nu) + Nu * (53 + 8 * Nu)) * S1z) - + (12 + Nu * (53 + 8 * Nu) + delta * (-12 + 23 * Nu)) * S2z) - + 3 * r * + (16 * params.eta2 * PhiDOT * r * + (4 * params.PhiDOT2 * params.r2 - + 2j * PhiDOT * r * rDOT + params.rDOT2) * + (S1z + S2z) + + 30j * params.PhiDOT2 * params.r2 * rDOT * + (S1z + delta * S1z + S2z - delta * S2z) + + Nu * (-4 * params.PhiDOT3 * params.r3 * + ((5 + 17 * delta) * S1z + (5 - 17 * delta) * S2z) - + 1j * params.PhiDOT2 * params.r2 * rDOT * + ((189 + 17 * delta) * S1z + (189 - 17 * delta) * S2z) + + 20 * PhiDOT * r * params.rDOT2 * + (-((-3 + delta) * S1z) + (3 + delta) * S2z) - + 4j * params.rDOT3 * + ((-4 + delta) * S1z - (4 + delta) * S2z))))) + / (72.0 * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + -(mass * PhiDOT * + (4 * params.Mtot2 * + (2 * (5377 + 6438 * Nu - 79866 * params.eta2 + 37348 * params.eta3) * + PhiDOT * r - + 5j * (-4115 + 18399 * Nu - 20276 * params.eta2 + 7 * params.eta3) * + rDOT) - + 4 * mass * r * + ((4599 - 15737 * Nu + 36259 * params.eta2 + 108563 * params.eta3) * + params.PhiDOT3 * params.r3 - + 1j * (-34053 + 59698 * Nu + 192949 * params.eta2 + 16193 * params.eta3) * + params.PhiDOT2 * params.r2 * rDOT + + (-59058 + 77983 * Nu + 322468 * params.eta2 - 4264 * params.eta3) * + PhiDOT * r * params.rDOT2 + + 5j * (-3387 + 8518 * Nu + 8968 * params.eta2 + 884 * params.eta3) * + params.rDOT3) + + 3 * params.r2 * + (4 * (-710 + 3892 * Nu - 10655 * params.eta2 + 24000 * params.eta3) * + params.PhiDOT5 * params.r5 + + 11j * (-1484 + 11693 * Nu - 25006 * params.eta2 + 428 * params.eta3) * + params.PhiDOT4 * params.r4 * rDOT + + 4 * (4161 - 25618 * Nu + 29489 * params.eta2 + 22078 * params.eta3) * + params.PhiDOT3 * params.r3 * params.rDOT2 + + 44j * (-151 + 1067 * Nu - 2419 * params.eta2 + 57 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT3 + + 4 * (2041 - 11680 * Nu + 19334 * params.eta2 + 3368 * params.eta3) * + PhiDOT * r * params.rDOT4 + + 5j * (477 - 2624 * Nu + 3862 * params.eta2 + 1160 * params.eta3) * + params.rDOT5))) + / (4752.0 * np.sqrt(35) * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + (np.sqrt(0.7142857142857143) * params.Mtot3 * + (2 * mass * + (2j * (1 + delta - 2 * Nu) * S1z + + Nu * ((1 + delta) * (-6 + kappa1) + 12 * Nu) * params.S1z2 + + 8 * Nu * (-1 + 3 * Nu) * S1z * S2z + + S2z * (-2j * (-1 + delta + 2 * Nu) + + Nu * (-6 - delta * (-6 + kappa2) + kappa2 + 12 * Nu) * S2z)) + + r * (4 * params.rDOT2 * + (2j * (1 + delta - 2 * Nu) * S1z + + Nu * ((1 + delta) * (-2 + kappa1) + 4 * Nu) * params.S1z2 + + 8 * params.eta2 * S1z * S2z + + S2z * (-2j * (-1 + delta + 2 * Nu) + + Nu * (-2 - delta * (-2 + kappa2) + kappa2 + 4 * Nu) * S2z)) + + 2 * params.PhiDOT2 * params.r2 * + (14j * (1 + delta - 2 * Nu) * S1z + + (6 * (1 + delta) * kappa1 - + (26 + 23 * kappa1 + delta * (26 + 11 * kappa1)) * Nu + + 4 * (1 + 9 * kappa1) * params.eta2) * params.S1z2 - + 64 * params.eta2 * S1z * S2z + + S2z * (-14j * (-1 + delta + 2 * Nu) + + 2 * Nu * (-13 + 13 * delta + 2 * Nu) * S2z + + kappa2 * (6 + delta * (-6 + 11 * Nu) + + Nu * (-23 + 36 * Nu)) * S2z)) + + PhiDOT * r * rDOT * + (40 * (1 + delta - 2 * Nu) * S1z - + 1j * (8 * Nu * (-2 - 2 * delta + Nu) + + kappa1 * (3 + delta * (3 + 11 * Nu) + + Nu * (5 + 18 * Nu))) * params.S1z2 + + 20j * (-3 + Nu) * Nu * S1z * S2z + + S2z * (-40 * (-1 + delta + 2 * Nu) - + 1j * (8 * Nu * (-2 + 2 * delta + Nu) + + kappa2 * (3 - delta * (3 + 11 * Nu) + + Nu * (5 + 18 * Nu))) * S2z))))) + / (24.0 * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + return ( + -0.000014029180695847363 * + (params.Mtot2 * + (3 * params.r2 * + (-120 * params.eta3 * + (3565 * params.PhiDOT5 * params.r5 + + 2321j * params.PhiDOT4 * params.r4 * rDOT + + 8244 * params.PhiDOT3 * params.r3 * params.rDOT2 - + 869j * params.PhiDOT2 * params.r2 * params.rDOT3 - + 56 * PhiDOT * r * params.rDOT4 - + 120j * params.rDOT5) * + (S1z + S2z) + + 2475 * params.PhiDOT2 * params.r2 * + (6 * params.PhiDOT3 * params.r3 + + 77j * params.PhiDOT2 * params.r2 * rDOT - + 72 * PhiDOT * r * params.rDOT2 + + 6j * params.rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) - + 3 * Nu * + (22j * params.PhiDOT4 * params.r4 * rDOT * + (36322j + 5 * (2993 + 3893 * delta) * S1z + + 5 * (2993 - 3893 * delta) * S2z) - + 25j * params.rDOT5 * + ((1053 + 443 * delta) * S1z + (1053 - 443 * delta) * S2z) + + 44j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (-5444j + 5 * (1424 + 849 * delta) * S1z + + 7120 * S2z - 4245 * delta * S2z) - + 20 * PhiDOT * r * params.rDOT4 * + (-1782j + (5963 + 2969 * delta) * S1z + + 5963 * S2z - 2969 * delta * S2z) + + 4 * params.PhiDOT5 * params.r5 * + (-86889j + 10 * (2063 + 225 * delta) * S1z + + 20630 * S2z - 2250 * delta * S2z) + + 4 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (234861j + 40 * (-1824 + 97 * delta) * S1z - + 40 * (1824 + 97 * delta) * S2z)) + + 2 * params.eta2 * + (params.PhiDOT5 * params.r5 * + (-1549757j + 300 * (1448 + 1311 * delta) * S1z + + 300 * (1448 - 1311 * delta) * S2z) + + 11j * params.PhiDOT4 * params.r4 * rDOT * + (329548j + 15 * (4113 + 1411 * delta) * S1z + + 61695 * S2z - 21165 * delta * S2z) + + 22j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (-23971j + 15 * (3829 + 243 * delta) * S1z + + 57435 * S2z - 3645 * delta * S2z) + + 150j * params.rDOT5 * + ((-503 + 92 * delta) * S1z - (503 + 92 * delta) * S2z) + + 10 * PhiDOT * r * params.rDOT4 * + (4565j + 6 * (-6327 + 991 * delta) * S1z - + 6 * (6327 + 991 * delta) * S2z) + + 21 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (161403j + 10 * (-1897 + 1471 * delta) * S1z - + 10 * (1897 + 1471 * delta) * S2z))) - + 6 * mass * r * + (60 * params.eta3 * + (2417 * params.PhiDOT3 * params.r3 + + 7258j * params.PhiDOT2 * params.r2 * rDOT - + 4381 * PhiDOT * r * params.rDOT2 + + 480j * params.rDOT3) * + (S1z + S2z) - + 165 * + (1161 * params.PhiDOT3 * params.r3 - + 536j * params.PhiDOT2 * params.r2 * rDOT - + 2412 * PhiDOT * r * params.rDOT2 - + 270j * params.rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) + + 2 * Nu * + (1j * params.PhiDOT2 * params.r2 * rDOT * + (1015784j + 5 * (30849 + 88721 * delta) * S1z + + 5 * (30849 - 88721 * delta) * S2z) + + params.PhiDOT3 * params.r3 * + (-173371j + 5 * (61569 + 10789 * delta) * S1z + + 307845 * S2z - 53945 * delta * S2z) - + 5 * PhiDOT * r * params.rDOT2 * + (-115368j + (177417 + 52307 * delta) * S1z + + 177417 * S2z - 52307 * delta * S2z) + + 100j * params.rDOT3 * + ((-1545 + 181 * delta) * S1z - (1545 + 181 * delta) * S2z)) + + params.eta2 * + (20 * PhiDOT * r * params.rDOT2 * + (-11187j - 48074 * S1z + 11057 * delta * S1z - + 48074 * S2z - 11057 * delta * S2z) + + 725j * params.rDOT3 * + (-73 * S1z + 31 * delta * S1z - 73 * S2z - 31 * delta * S2z) + + params.PhiDOT3 * params.r3 * + (603141j - 543040 * S1z + 404620 * delta * S1z - + 20 * (27152 + 20231 * delta) * S2z) + + 1j * params.PhiDOT2 * params.r2 * rDOT * + (-1798104j - 648485 * S1z + 105755 * delta * S1z - + 5 * (129697 + 21151 * delta) * S2z))) + + 10 * params.Mtot2 * + (24 * params.eta3 * + (6981 * PhiDOT * r + 1600j * rDOT) * + (S1z + S2z) - + 66 * (2027 * PhiDOT * r + 380j * rDOT) * + (S1z + delta * S1z + S2z - delta * S2z) + + 30j * Nu * rDOT * + (297 * (1 + delta) * kappa1 * params.S1z3 + + 297 * (1 + delta) * kappa1 * params.S1z2 * S2z + + S1z * (17261 - 1641 * delta - + 297 * (-1 + delta) * kappa2 * params.S2z2) + + S2z * (17261 + 1641 * delta - + 297 * (-1 + delta) * kappa2 * params.S2z2)) + + 8 * Nu * PhiDOT * r * + (-7315j - + 4455 * (1 + delta) * kappa1 * params.S1z3 + + 3 * (3881 - 7757 * delta) * S2z - + 4455 * (1 + delta) * kappa1 * params.S1z2 * S2z + + 4455 * (-1 + delta) * kappa2 * params.S2z3 + + 3 * S1z * + (3881 + 7757 * delta + + 1485 * (-1 + delta) * kappa2 * params.S2z2)) + + 3 * params.eta2 * + (-5j * rDOT * + (S1z * (18793 + 223 * delta + 1188 * kappa1 * params.S1z2) + + (18793 - 223 * delta + 1188 * (-2 + kappa1) * params.S1z2) * S2z + + 1188 * (-2 + kappa2) * S1z * params.S2z2 + + 1188 * kappa2 * params.S2z3) + + 4 * PhiDOT * r * + (-4939j - 23359 * S1z - 5563 * delta * S1z + + 5940 * kappa1 * params.S1z3 + + (-23359 + 5563 * delta + + 5940 * (-2 + kappa1) * params.S1z2) * S2z + + 5940 * (-2 + kappa2) * S1z * params.S2z2 + + 5940 * kappa2 * params.S2z3))))) + / (np.sqrt(35) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_3_m_2( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> 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 + b0: float = 2.0 * mass / np.exp(0.5) + EulerGamma: float = 0.5772156649015329 + + # Pre-calculating the shared log term using np.log + common_log_term: complex = ( + -10.0 + 6.0 * EulerGamma - 3.0j * np.pi + + np.log(4096.0) + 6.0 * np.log(b0) + 9.0 * np.log(x) + ) + + if vpnorder == 5: + return ( + -0.4444444444444444j * np.sqrt(0.7142857142857143) * (-1.0 + 3.0 * Nu) * params.x3p5 * common_log_term + ) + + elif vpnorder == 6: + return ( + 0.8888888888888888j * np.sqrt(0.7142857142857143) * Nu * (S1z + S2z) * params.x4 * common_log_term + ) + + elif vpnorder == 7: + numerator = ( + -0.024691358024691357j * (193.0 - 680.0 * Nu + 230.0 * params.eta2) * params.x4p5 * common_log_term + ) + return numerator / np.sqrt(35.0) + + else: + return 0.0 + 0.0j + +def hl_3_m_2( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package + go_component: complex = hGO_3_m_2( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_2( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) + phase_factor: complex = np.exp(-2j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_3_m_min2( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package + go_component: complex = hGO_3_m_2( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_2( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + amplitude_term: complex = (go_component + qc_component).conjugate() + + # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) + phase_factor: complex = np.exp(2j * Phi) + + return pre_factor * amplitude_term * phase_factor + +# H31 + +def hGO_3_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + r0 = 2 * mass / np.exp(0.5) + + 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 + + if vpnorder == 1: + return ( + delta * (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 * Nu) * params.r2 * combination_b2 * combination_a3 + + 2 * params.Mtot2 * + (1j * (-101 + 43 * Nu) * PhiDOT * r + + (109 - 86 * Nu) * rDOT) + + 3 * mass * r * + (-4j * (-9 + 14 * Nu) * params.PhiDOT3 * params.r3 + + 6 * (2 + 9 * Nu) * params.PhiDOT2 * params.r2 * rDOT - + 1j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 + + 4 * (8 + 17 * Nu) * params.rDOT3)) + / (36.0 * np.sqrt(14) * params.r2) + ) + + elif vpnorder == 4: + return ( + 0.041666666666666664j * params.Mtot2 * + (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) - + r * (2 * params.rDOT2 * + (-6 * (1 + delta) * S1z + 5 * (5 + 3 * delta) * Nu * S1z + + 6 * S2z - 6 * delta * S2z + + 5 * (-5 + 3 * delta) * Nu * S2z) + + params.PhiDOT2 * params.r2 * + ((24 + 24 * delta - 87 * Nu + 31 * delta * Nu) * S1z + + (-24 + 24 * delta + 87 * Nu + 31 * delta * Nu) * S2z) + + 2j * PhiDOT * r * rDOT * + ((6 + 6 * delta - 31 * Nu + 35 * delta * Nu) * S1z + + (-6 + 6 * delta + 31 * Nu + 35 * delta * Nu) * S2z))) + / (np.sqrt(14) * params.r3) + ) + + elif vpnorder == 5: + term1 = ( + delta * + (-18j * (183 - 1579 * Nu + 3387 * params.eta2) * params.r3 * + combination_b3 * combination_a4 + + 2 * params.Mtot3 * + (1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r - + 12 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + + 2 * params.Mtot2 * r * + (-1j * (-8641 - 59189 * Nu + 31959 * params.eta2) * params.PhiDOT3 * params.r3 + + (-32635 - 29345 * Nu + 29541 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT - + 44j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 + + 6 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + + 3 * mass * params.r2 * + (2j * (-2479 - 4505 * Nu + 16785 * params.eta2) * params.PhiDOT5 * params.r5 + + 4 * (817 + 1220 * Nu - 7449 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT + + 6j * (-1679 + 1469 * Nu + 12233 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - + 32 * (-460 + 421 * Nu + 2514 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 + + 1j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 - + 12 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) + / (4752.0 * np.sqrt(14) * params.r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + params.Mtot3 * + (kappa1 * + (-1j * (-13 - 13 * delta + 68 * Nu + 42 * delta * Nu) * PhiDOT * r - + 34 * (1 + delta) * rDOT + 4 * (26 + 9 * delta) * Nu * rDOT) * + params.S1z2 + + S2z * (12 * delta * Nu * (7j * PhiDOT * r - 6 * rDOT) * S1z + + kappa2 * + (-1j * (13 - 13 * delta - 68 * Nu + 42 * delta * Nu) * PhiDOT * r + + 2 * (17 - 17 * delta - 52 * Nu + 18 * delta * Nu) * rDOT) * + S2z)) + / (24.0 * np.sqrt(14) * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + delta * params.Mtot2 * Nu * + (668 * params.Mtot2 - + 2 * mass * r * + (727 * params.PhiDOT2 * params.r2 - + 99j * PhiDOT * r * rDOT + 452 * params.rDOT2) + + params.r2 * + (-499 * params.PhiDOT4 * params.r4 + + 1534j * params.PhiDOT3 * params.r3 * rDOT + + 3072 * params.PhiDOT2 * params.r2 * params.rDOT2 - + 680j * PhiDOT * r * params.rDOT3 + + 1000 * params.rDOT4)) + / (180.0 * np.sqrt(14) * params.r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.0023148148148148147j * params.Mtot2 * + (2 * params.Mtot2 * + ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + + 8 * (12 + 47 * delta) * params.eta2) * S1z + + (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + + 8 * (-12 + 47 * delta) * params.eta2) * S2z) + + 3 * params.r2 * + (2 * params.rDOT4 * + ((30 + Nu * (-187 + 318 * Nu) + + delta * (30 + Nu * (-101 + 122 * Nu))) * S1z + + (-30 + (187 - 318 * Nu) * Nu + + delta * (30 + Nu * (-101 + 122 * Nu))) * S2z) + + 2 * params.PhiDOT4 * params.r4 * + ((90 - Nu * (28 + 579 * Nu) + + delta * (90 + Nu * (-800 + 551 * Nu))) * S1z + + (-90 + Nu * (28 + 579 * Nu) + + delta * (90 + Nu * (-800 + 551 * Nu))) * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + ((186 - Nu * (745 + 354 * Nu) + + delta * (186 + Nu * (-191 + 554 * Nu))) * S1z + + (-186 + Nu * (745 + 354 * Nu) + + delta * (186 + Nu * (-191 + 554 * Nu))) * S2z) + + 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + ((32 + Nu * (-451 + 230 * Nu) + + delta * (32 + Nu * (691 + 626 * Nu))) * S1z + + (-32 + (451 - 230 * Nu) * Nu + + delta * (32 + Nu * (691 + 626 * Nu))) * S2z) + + 2j * params.PhiDOT3 * params.r3 * rDOT * + ((-12 + Nu * (-341 + 315 * Nu) + + delta * (-12 + Nu * (-91 + 1213 * Nu))) * S1z + + (12 + (341 - 315 * Nu) * Nu + + delta * (-12 + Nu * (-91 + 1213 * Nu))) * S2z)) - + 2 * mass * r * + (2 * params.PhiDOT2 * params.r2 * + ((-312 * (1 + delta) + 2 * (827 - 923 * delta) * Nu + + 5 * (-201 + 131 * delta) * params.eta2) * S1z + + (-312 * (-1 + delta) - 2 * (827 + 923 * delta) * Nu + + 5 * (201 + 131 * delta) * params.eta2) * S2z) + + 2 * params.rDOT2 * + ((105 + Nu * (-541 + 924 * Nu) + + delta * (105 + Nu * (-77 + 494 * Nu))) * S1z + + (-105 + (541 - 924 * Nu) * Nu + + delta * (105 + Nu * (-77 + 494 * Nu))) * S2z) + + 1j * PhiDOT * r * rDOT * + ((1104 - 7 * Nu * (439 + 597 * Nu) + + delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S1z + + (-1104 + 7 * Nu * (439 + 597 * Nu) + + delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S2z))) + / (np.sqrt(14) * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + M_PI2 = np.pi ** 2 + + spin_terms = ( + 1513512 * params.Mtot3 * + (2 * mass * + (PhiDOT * r * + (1j * (-97 + 631 * Nu) * S1z + + 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + + 3 * kappa1 * (-39 + Nu * (149 + 4 * Nu))) * params.S1z2 + + S2z * (97j - 631j * Nu - + 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + + 3 * kappa2 * (-39 + Nu * (149 + 4 * Nu))) * S2z)) + + rDOT * + (2 * (-18 + 83 * Nu) * S1z - + 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + + kappa1 * (155 + Nu * (-373 + 164 * Nu))) * params.S1z2 + + S2z * (36 - 166 * Nu + + 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + + kappa2 * (155 + Nu * (-373 + 164 * Nu))) * S2z))) + + r * (2 * params.rDOT3 * + (S1z * (18 - 110 * Nu - + 5j * (69 * kappa1 - 214 * kappa1 * Nu + + 4 * (5 + 4 * kappa1) * params.eta2) * S1z) + + 2 * (-9 + 55 * Nu) * S2z + + 5j * (69 * kappa2 - 214 * kappa2 * Nu + + 4 * (5 + 4 * kappa2) * params.eta2) * params.S2z2) + + params.PhiDOT3 * params.r3 * + (S1z * (255j - 1403j * Nu + + 10 * (28 * (3 - 8 * Nu) * Nu + + kappa1 * (51 + 2 * Nu * (-91 + 118 * Nu))) * S1z) + + 1j * (-255 + 1403 * Nu) * S2z - + 10 * (28 * (3 - 8 * Nu) * Nu + + kappa2 * (51 + 2 * Nu * (-91 + 118 * Nu))) * params.S2z2) + + 2 * params.PhiDOT2 * params.r2 * rDOT * + (S1z * (255 - 1079 * Nu + + 5j * (2 * (60 - 361 * Nu) * Nu + + kappa1 * (6 + Nu * (-47 + 164 * Nu))) * S1z) + + (-255 + 1079 * Nu) * S2z - + 5j * (2 * (60 - 361 * Nu) * Nu + + kappa2 * (6 + Nu * (-47 + 164 * Nu))) * params.S2z2) + + PhiDOT * r * params.rDOT2 * + (4j * (-114 + 781 * Nu) * S1z + + 5 * (-213 * kappa1 - 72 * Nu + 278 * kappa1 * Nu + + 8 * (7 + 44 * kappa1) * params.eta2) * params.S1z2 + + S2z * (456j + 1065 * kappa2 * S2z + + 2 * Nu * (-1562j - 5 * (-36 + 139 * kappa2 + + 4 * (7 + 44 * kappa2) * Nu) * S2z))))) + ) + + orbital_terms = ( + delta * + (52920 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * + params.r4 * combination_b4 * combination_a5 + + 840 * params.Mtot2 * params.r2 * + ((-2555489 + 7 * Nu * (820078 + Nu * (-6623390 + 4948497 * Nu))) * + params.PhiDOT5 * params.r5 + + 1j * (3537631 + 7 * Nu * (-2817653 + Nu * (-7052042 + 4017147 * Nu))) * + params.PhiDOT4 * params.r4 * rDOT + + 3 * (-1428997 + 7 * Nu * (-1230747 + Nu * (-237418 + 4061717 * Nu))) * + params.PhiDOT3 * params.r3 * params.rDOT2 + + 1j * (-5153011 + 7 * Nu * (-2375327 + 9 * Nu * (218846 + 1640185 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT3 + + (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * + PhiDOT * r * params.rDOT4 + + 3j * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * + params.rDOT5) - + 8820 * mass * params.r3 * + (2 * (111737 + Nu * (-366573 + Nu * (-618923 + 2278593 * Nu))) * + params.PhiDOT7 * params.r7 + + 2j * (101844 + Nu * (-273675 - 871630 * Nu + 2069774 * params.eta2)) * + params.PhiDOT6 * params.r6 * rDOT + + 2 * (341322 + Nu * (-1429938 + Nu * (-1206083 + 7690681 * Nu))) * + params.PhiDOT5 * params.r5 * params.rDOT2 + + 8j * (90241 + 2 * Nu * (-206022 + Nu * (-62113 + 1003558 * Nu))) * + params.PhiDOT4 * params.r4 * params.rDOT3 + + 2 * (410547 + Nu * (-2269686 + Nu * (762091 + 8400052 * Nu))) * + params.PhiDOT3 * params.r3 * params.rDOT4 + + 4j * (217935 + 2 * Nu * (-573699 + 5 * Nu * (18671 + 445748 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT5 + + (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * + PhiDOT * r * params.rDOT6 + + 24j * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * + params.rDOT7) + + 2 * params.Mtot4 * + (-4178597424j * rDOT + + 84j * rDOT * + (38468500 * params.eta3 + + 648648j * (S1z + S2z) - + 90090 * ((-24 + 155 * kappa1) * params.S1z2 + + (-24 + 155 * kappa2) * params.S2z2) - + 420 * params.eta2 * + (-122855 + + 3003 * ((2 + 11 * kappa1) * params.S1z2 - + 18 * S1z * S2z + + (2 + 11 * kappa2) * params.S2z2)) + + 3 * Nu * + (-103100846 - 1846845 * M_PI2 - + 564564j * S2z + + 6006 * (S1z * (-94j + 5 * (-52 + 63 * kappa1) * S1z) - + 20 * S1z * S2z + + 5 * (-52 + 63 * kappa2) * params.S2z2))) + + PhiDOT * r * + (1176172480 * params.eta3 + + 8 * (74084729 - + 189189 * S1z * (97j + 5 * (-8 + 117 * kappa1) * S1z) - + 189189 * S2z * (97j + 5 * (-8 + 117 * kappa2) * S2z)) - + 176400 * params.eta2 * + (11251 + + 429 * ((2 + 13 * kappa1) * params.S1z2 - + 22 * S1z * S2z + + (2 + 13 * kappa2) * params.S2z2)) + + 147 * Nu * + (-65012788 + 4485195 * M_PI2 + + 4499352j * S2z + + 10296 * (S1z * (437j + 15 * (-32 + 71 * kappa1) * S1z) - + 3860 * S1z * S2z + + 15 * (-32 + 71 * kappa2) * params.S2z2)))) - + 3 * params.Mtot3 * r * + (-4j * params.rDOT3 * + (601018232 - 1359334480 * params.eta3 - + 756756 * S1z * (6j + 115 * kappa1 * S1z) - + 756756 * S2z * (6j + 115 * kappa2 * S2z) + + 231 * Nu * + (8490448 + 503685 * M_PI2 + + 80808j * S2z + + 2184 * (S1z * (37j + 190 * kappa1 * S1z) + + 70 * S1z * S2z + + 190 * kappa2 * params.S2z2)) + + 58800 * params.eta2 * + (-62596 + + 429 * ((-1 + 5 * kappa1) * params.S1z2 - + 12 * S1z * S2z + + (-1 + 5 * kappa2) * params.S2z2))) - + 14j * params.PhiDOT2 * params.r2 * rDOT * + (-229522160 * params.eta3 + + 8 * (48303859 + + 135135 * S1z * (-17j + 2 * kappa1 * S1z) + + 135135 * S2z * (-17j + 2 * kappa2 * S2z)) + + 2520 * params.eta2 * + (100913 + + 286 * ((-31 + 5 * kappa1) * params.S1z2 - + 72 * S1z * S2z + + (-31 + 5 * kappa2) * params.S2z2)) + + 7 * Nu * + (125038052 + 2374515 * M_PI2 + + 5858424j * S2z - + 10296 * (S1z * (-569j + 25 * (-24 + 7 * kappa1) * S1z) + + 700 * S1z * S2z + + 25 * (-24 + 7 * kappa2) * params.S2z2))) + + 4 * PhiDOT * r * params.rDOT2 * + (-1095987374 + 1035895280 * params.eta3 + + 378378 * S1z * (152j + 355 * kappa1 * S1z) + + 378378 * S2z * (152j + 355 * kappa2 * S2z) - + 490 * params.eta2 * + (-5802767 + + 5148 * ((2 + 23 * kappa1) * params.S1z2 - + 42 * S1z * S2z + + (2 + 23 * kappa2) * params.S2z2)) - + 77 * Nu * + (42451610 + 1511055 * M_PI2 + + 3623256j * S2z - + 6552 * (S1z * (-553j + 5 * (18 + 37 * kappa1) * S1z) + + 965 * S1z * S2z + + 5 * (18 + 37 * kappa2) * params.S2z2))) + + 7 * params.PhiDOT3 * params.r3 * + (512893080 * params.eta3 - + 136 * (-2089567 + + 135135 * S1z * (1j + 2 * kappa1 * S1z) + + 135135 * S2z * (1j + 2 * kappa2 * S2z)) - + 560 * params.eta2 * + (2457671 + + 2574 * ((11 + 53 * kappa1) * params.S1z2 - + 84 * S1z * S2z + + (11 + 53 * kappa2) * params.S2z2)) + + 3 * Nu * + (16621605 * M_PI2 + + 8 * (27468722 + + 2681679j * S2z + + 3003 * (S1z * (893j - 840 * S1z + 800 * kappa1 * S1z) - + 3160 * S1z * S2z + + 40 * (-21 + 20 * kappa2) * params.S2z2)))))) + ) + + log_term = ( + 74954880 * delta * params.Mtot3 * + (mass * (-22j * PhiDOT * r - 24 * rDOT) + + 3 * r * + (7j * params.PhiDOT3 * params.r3 + + 14 * params.PhiDOT2 * params.r2 * rDOT - + 8j * PhiDOT * r * params.rDOT2 + + 4 * params.rDOT3)) * + np.log(r / r0) + ) + + return ( + 1j * (spin_terms + orbital_terms + log_term) + / (3.6324288e8 * np.sqrt(14) * params.r4) + ) + + else: + return 0.0 + 0.0j + + + + From ab7ec46e658fe93dbf39ae515828da26ee3d3a5a Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Wed, 22 Apr 2026 22:22:03 +0530 Subject: [PATCH 07/36] changed to use of numpy instead of math --- esigmapy/python_codes/esigma_pn_inspiral.py | 2822 +++++++++++++++++++ 1 file changed, 2822 insertions(+) create mode 100644 esigmapy/python_codes/esigma_pn_inspiral.py diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py new file mode 100644 index 0000000..c16d6b7 --- /dev/null +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -0,0 +1,2822 @@ +""" +esigma_pn_inspiral.py +==================== +Python translation of ESIGMA_PNInspiral.c + +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 = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant +LOG2 = 0.693147180559945309417232121458 +LOG3 = 1.09861228866810969139524523692 +LOG4 = 1.38629436111989061883446424292 +LOG5 = 1.60943791243410037460075933323 +REAL8_FAIL_NAN = float('nan') + +# ------ 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 + e2 = e * e + pre = 192.0 * np.sqrt(ef) / (985.0 * e2) + return pre * (np.sqrt(ef) * phi_e(e) - phi_e_tilde(e)) + + +def psi_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 18816.0 / (55691.0 * e2 * np.sqrt(ef)) + pf2 = 16382.0 * np.sqrt(ef) / (55691.0 * e2) + b1 = np.sqrt(ef) * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) + b2 = np.sqrt(ef) * psi_e(e) - psi_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def zed_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 924.0 / (19067.0 * e2 * np.sqrt(ef)) + pf2 = 12243.0 * np.sqrt(ef) / (76268.0 * e2) + b1 = -ef * np.sqrt(ef) * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) + b2 = np.sqrt(ef) * zed_e(e) - zed_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def kappa_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + den = 769.0 / 96.0 - 3059665.0 * LOG2 / 700566.0 + 8190315.0 * LOG3 / 1868176.0 + pf = np.sqrt(ef) / e2 + b1 = np.sqrt(ef) * 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)""" + 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) + e0 = 2.0 * eta * 96.0 / 15.0 + return (num / den) if e else e0 + +## --------- 1PN terms ------------ + +def x_dot_1pn(e: float, eta: float) -> float: + """Eq. (A27)""" + 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) + e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) + return (num / den) if e else e0 + +## --------- 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + M = m1 + m2 + if e: + 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 * ef**5 * M**4) + else: + 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) + + +def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: + """Eq. (A28)""" + pre = eta * x**6 * np.sqrt(x) + if e: + 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) -> float: + """Eq. (A29)""" + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + + ef = 1.0 - e2 + eta2 = eta * eta + + # Small-e limit + e0_lim = eta * (68206.0 / 2835.0 + + 27322.0 * eta / 315.0 + + 1888.0 * eta2 / 45.0) + + if e: return e0_lim + else: + 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 + 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**2 + pre = 64.0 * eta / 5.0 + + if e: + 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) + else: + return pre * ( + ((1 + 80*kappa1)*m1*m1*S1z*S1z + + 158*m1*m2*S1z*S2z + + (1 + 80*kappa2)*m2*m2*S2z*S2z) + ) / (16.0 * M2) + +## --------- 2.5PN terms ------------ + +def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: + """See Huerta et al.""" + ef = 1.0 - e*e + ef2 = ef * ef + e2 = e * e + pre = eta * x**7 * np.sqrt(x) + + if 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + ef = 1.0 - e2 + ef_sqrt = np.sqrt(ef) + M2 = (m1 + m2)**2 + M4 = M2 * M2 + M6 = M4 * M2 + pre = 64.0 * eta / 5.0 + + if e: + 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 + ) + +## --------- 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 e: + 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.)""" + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + e10 = e8 * e2 + + ef = 1.0 - e2 + 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 + + 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)""" + 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 + M2 = (m1 + m2)**2 + M4 = M2 * M2 + pre = 64.0 * eta / 5.0 + + if e: + 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 + + 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 + + M = m1 + m2 + M2 = M * M + M4 = M2 * M2 + M6 = M4 * 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) + ) + + # --- 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 e 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 e 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 e 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 e else val_e0 + +## --------- 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 e 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 e else val_e0 + + +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 e 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 e 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 not e: + 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 not e: + 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 not e: + return 0.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + M = m1 + m2 + pre = e / (ef**4) * (m1*m2) / (90.0 * M**4) + 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 not e: + return 0.0 + pre = 32.0 * eta * e * x**7 / 5.0 + return pre * (-985.0 * np.pi * phi_e_rad(e) / 48.0) + +## ---------- 2PN ------------- + +def e_dot_2pn(e: float, eta: float) -> float: + e_2_pn = 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) + + if e != 0.0: + 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) + ) + else: + e_2_pn = 0.0 + + 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 not e: + 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 e == 0.0: + 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 not e: + return 0.0 + pre = 32.0 * eta * e * x**4 * x**4 * 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 not e: + 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 e == 0.0: + 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.np.pi * np.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.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 not e: + 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) + + +import math + +def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: + if e == 0.0: + 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 not e: + 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 not e: + 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 + + +# ============ 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) + + +# ================== 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 + xsqx = x * np.sqrt(x) + x5 = x**5 + + # ------------------------- + # 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) * x2 * np.sqrt(x) + + 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) * x3 * np.sqrt(x) + inst += x_dot_3_5_pn(e, eta) * x3 * np.sqrt(x) + inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) + inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) + + 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) + + if radiation_pn_order >= 9: + inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * np.sqrt(x) + + 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 + xsqx = x * np.sqrt(x) + + # ------------------------- + # 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) * x2 * np.sqrt(x) + + if radiation_pn_order >= 6: + inst += e_dot_3pn(e, eta, x) * x3 + inst += e_dot_3_5pn(e, eta) * x3 * np.sqrt(x) + 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 += 0.0 # placeholder for higher order terms, not implemented + + 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.np.pi: + mean_anomaly -= 2 * np.np.pi + while mean_anomaly < - np.np.pi: + mean_anomaly += 2 * np.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.np.pi: + l -= 2 * np.np.pi + while l < - np.np.pi: + l += 2 * np.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.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.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 e != 0: + 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 + From 7da0c811bddccf832f239daec127a69a44621e12 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Wed, 22 Apr 2026 22:23:50 +0530 Subject: [PATCH 08/36] rename files --- esigmapy/python_codes/ESIGMA_GOterms.py | 628 ----- esigmapy/python_codes/ESIGMA_PNInspiral.py | 2823 -------------------- 2 files changed, 3451 deletions(-) delete mode 100644 esigmapy/python_codes/ESIGMA_GOterms.py delete mode 100644 esigmapy/python_codes/ESIGMA_PNInspiral.py diff --git a/esigmapy/python_codes/ESIGMA_GOterms.py b/esigmapy/python_codes/ESIGMA_GOterms.py deleted file mode 100644 index ea4638b..0000000 --- a/esigmapy/python_codes/ESIGMA_GOterms.py +++ /dev/null @@ -1,628 +0,0 @@ -import math -import cmath -from typing import Any - -def Complex(real: float, imag: float) -> complex: - """Helper function to mimic the C-style Complex constructor.""" - return complex(real, imag) - -# All the hGO_l_m functions contain the 3PN non-spinning general orbit (GO) terms -# from Mishra et al. arXiv:1501.07096 and 3.5PN quasi-circular spinning -# corrections as given in Henry et al, arXiv:2209.00374v2 - -# The (2,2) mode has newly computed 4PN non-spinning quasi-circular piece from -# arXiv:2304.11185 - -# H22 - -def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, - PhiDOT: float, vpnorder: int, S1z: float, S2z: float, - x: float, params: Any) -> complex: - - # Note: In Python, `params` should be an object (like a dataclass or SimpleNamespace) - # so that attributes can be accessed via dot notation (e.g., params.PhiDOT2). - - # For black holes kappa and lambda is 1 - kappa1 = 1.0 - kappa2 = 1.0 - lambda1 = 1.0 - lambda2 = 1.0 - - delta = math.sqrt(1 - 4 * Nu) - - 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 - - # Mapping M_PI2 to pi^2 (standard in PN GW theory for this coefficient) - M_PI = math.pi - M_PI2 = math.pi ** 2 - - if vpnorder == 0: - return (mass / r + params.PhiDOT2 * params.r2 + - Complex(0, 2) * PhiDOT * r * rDOT - params.rDOT2) - - elif vpnorder == 2: - return ((21 * params.Mtot2 * (-10 + Nu) - - 27 * (-1 + 3 * Nu) * params.r2 * combination_b * combination_a3 + - mass * r * - ((11 + 156 * Nu) * params.PhiDOT2 * params.r2 + - Complex(0, 10) * (5 + 27 * Nu) * PhiDOT * r * rDOT - - 3 * (15 + 32 * Nu) * params.rDOT2)) / - (42. * params.r2)) - - elif vpnorder == 3: - return ((params.Mtot2 * (Complex(0, -1) * rDOT * - ((3 + 3 * delta - 8 * Nu) * S1z + - (3 - 3 * delta - 8 * Nu) * S2z) + - PhiDOT * r * - ((-3 - 3 * delta + 5 * Nu) * S1z + - (-3 + 3 * delta + 5 * Nu) * S2z))) / - (3. * params.r2)) - # (<--This is the general orbit term) - # (This is the quasi-circular limit of the general orbit term-->) - # - ((-4 * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x2p5) / 3.) + - # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - Nu * (S1z + S2z)) * params.x2p5) / 3.) - # (<--This is Quentins quasi-circular term) - - elif vpnorder == 4: - return ((6 * params.Mtot3 * (3028 + 1267 * Nu + 158 * params.eta2) + - 9 * (83 - 589 * Nu + 1111 * params.eta2) * params.r3 * - combination_b2 * combination_a4 + - params.Mtot2 * r * - ((-11891 - 36575 * Nu + 13133 * params.eta2) * params.PhiDOT2 * - params.r2 + - Complex(0, 8) * (-773 - 3767 * Nu + 2852 * params.eta2) * - PhiDOT * r * rDOT - - 6 * (-619 + 2789 * Nu + 934 * params.eta2) * params.rDOT2) - - 3 * mass * params.r2 * - (2 * (-835 - 19 * Nu + 2995 * params.eta2) * params.PhiDOT4 * - params.r4 + - Complex(0, 6) * (-433 - 721 * Nu + 1703 * params.eta2) * - params.PhiDOT3 * params.r3 * rDOT + - 6 * (-33 + 1014 * Nu + 232 * params.eta2) * params.PhiDOT2 * - params.r2 * params.rDOT2 + - Complex(0, 4) * (-863 + 1462 * Nu + 2954 * params.eta2) * - PhiDOT * r * params.rDOT3 - - 3 * (-557 + 664 * Nu + 1712 * params.eta2) * params.rDOT4)) / - (1512. * params.r3) + - (3 * params.Mtot3 * - (S1z * (4 * Nu * S2z + (1 + delta - 2 * Nu) * S1z * kappa1) - - (-1 + delta + 2 * Nu) * params.S2z2 * kappa2)) / - (4. * params.r3)) - # This is where circular limit terms added and subtracted - # - ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + - # ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) - - elif vpnorder == 5: - return ((params.Mtot2 * Nu * - (2 * mass * (Complex(0, -702) * PhiDOT * r + rDOT) + - 3 * r * - (Complex(0, -316) * params.PhiDOT3 * params.r3 - - 847 * params.PhiDOT2 * params.r2 * rDOT + - Complex(0, 184) * PhiDOT * r * params.rDOT2 - - 122 * params.rDOT3))) / - (105. * params.r3) - # Henry et al. QC spin terms - # + ((2*(56*delta*Nu*(-S1z + S2z) + 101*Nu*(S1z + S2z) + 132*params.eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) - + - # Henry et al. ecc spin terms - ((params.Mtot2 * - (mass * - ((238 + delta * (238 - 141 * Nu) + Nu * (-181 + 474 * Nu)) * - PhiDOT * r * S1z + - Complex(0, 8) * - (55 + delta * (55 - 19 * Nu) + - 2 * Nu * (-50 + 43 * Nu)) * - rDOT * S1z + - (238 + delta * (-238 + 141 * Nu) + Nu * (-181 + 474 * Nu)) * - PhiDOT * r * S2z + - Complex(0, 8) * - (55 + delta * (-55 + 19 * Nu) + - 2 * Nu * (-50 + 43 * Nu)) * - rDOT * S2z) - - r * (PhiDOT * r * params.rDOT2 * - (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * Nu + - 188 * params.eta2) * - S1z) + - (18 * (-1 + delta) + 5 * (63 + 55 * delta) * Nu - - 188 * params.eta2) * - S2z) - - Complex(0, 2) * params.rDOT3 * - ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * Nu - - 4 * params.eta2) * - S1z + - (-27 + 27 * delta + 30 * Nu - 42 * delta * Nu - - 4 * params.eta2) * - S2z) + - Complex(0, 2) * params.PhiDOT2 * params.r2 * rDOT * - ((51 + 88 * Nu * (-3 + 5 * Nu) + - delta * (51 + 62 * Nu)) * - S1z + - (51 + 88 * Nu * (-3 + 5 * Nu) - - delta * (51 + 62 * Nu)) * - S2z) + - params.PhiDOT3 * params.r3 * - ((120 * (1 + delta) + (-483 + 83 * delta) * Nu + - 234 * params.eta2) * - S1z + - (120 + 3 * Nu * (-161 + 78 * Nu) - - delta * (120 + 83 * Nu)) * - S2z)))) / - (84. * params.r3))) - - elif vpnorder == 6: - return ((4 * params.Mtot4 * - (-8203424 + 2180250 * params.eta2 + 592600 * params.eta3 + - 15 * Nu * (-5503804 + 142065 * M_PI2)) - - 2700 * (-507 + 6101 * Nu - 25050 * params.eta2 + 34525 * params.eta3) * - params.r4 * combination_b3 * combination_a5 + - params.Mtot3 * r * - (params.PhiDOT2 * - (337510808 - 198882000 * params.eta2 + - 56294600 * params.eta3 + - Nu * (183074880 - 6392925 * M_PI2)) * - params.r2 + - Complex(0, 110) * PhiDOT * - (-5498800 - 785120 * params.eta2 + 909200 * params.eta3 + - 3 * Nu * (-1849216 + 38745 * M_PI2)) * - r * rDOT + - 2 * - (51172744 - 94929000 * params.eta2 - 5092400 * params.eta3 + - 45 * Nu * (2794864 + 142065 * M_PI2)) * - params.rDOT2) - - 20 * params.Mtot2 * params.r2 * - ((-986439 + 1873255 * Nu - 9961400 * params.eta2 + - 6704345 * params.eta3) * - params.PhiDOT4 * params.r4 + - Complex(0, 4) * - (-273687 - 978610 * Nu - 4599055 * params.eta2 + - 2783005 * params.eta3) * - params.PhiDOT3 * params.r3 * rDOT + - (-181719 + 19395325 * Nu + 8237980 * params.eta2 + - 2612735 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT2 + - Complex(0, 8) * - (-234312 + 1541140 * Nu + 1230325 * params.eta2 + - 1828625 * params.eta3) * - PhiDOT * r * params.rDOT3 - - 3 * - (-370268 + 1085140 * Nu + 2004715 * params.eta2 + - 1810425 * params.eta3) * - params.rDOT4) + - 300 * mass * params.r3 * - (4 * - (12203 - 36427 * Nu - 27334 * params.eta2 + - 149187 * params.eta3) * - params.PhiDOT6 * params.r6 + - Complex(0, 2) * - (44093 - 68279 * Nu - 295346 * params.eta2 + - 541693 * params.eta3) * - params.PhiDOT5 * params.r5 * rDOT + - 2 * - (27432 - 202474 * Nu + 247505 * params.eta2 + - 394771 * params.eta3) * - params.PhiDOT4 * params.r4 * params.rDOT2 + - Complex(0, 2) * - (97069 - 383990 * Nu - 8741 * params.eta2 + - 1264800 * params.eta3) * - params.PhiDOT3 * params.r3 * params.rDOT3 + - (-42811 + 53992 * Nu + 309136 * params.eta2 - - 470840 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT4 + - Complex(0, 2) * - (51699 - 252256 * Nu + 131150 * params.eta2 + - 681160 * params.eta3) * - PhiDOT * r * params.rDOT5 - - 3 * - (16743 - 75104 * Nu + 26920 * params.eta2 + - 207200 * params.eta3) * - params.rDOT6)) / - (3.3264e6 * params.r4) - # Henry et al. QC spin terms - # + (((4*(1 + delta)*(-7 + 9*kappa1) - 7*(9 + 17*delta)*Nu - 9*(15 + 7*delta)*kappa1*Nu + 12*(7 - 17*kappa1)*params.eta2)*params.S1z2 + 2*S1z*(Complex(0,-42)*(1 + delta - 2*Nu) - 84*(1 + delta - Nu)*M_PI + Nu*(-271 + 288*Nu)*S2z) + S2z*(12*(7 - 17*kappa2)*params.eta2*S2z + 4*(-1 + delta)*(Complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + Nu*(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 * - (params.Mtot3 * - (2 * mass * - (S1z * (Complex(0, 14) * (1 + delta - 2 * Nu) + - 42 * (1 + delta - 2 * Nu) * Nu * S1z + - kappa1 * - (438 + delta * (438 + 103 * Nu) + - Nu * (-773 + 108 * Nu)) * - S1z) + - 2 * - (Complex(0, -7) * (-1 + delta + 2 * Nu) + - (995 - 192 * Nu) * Nu * S1z) * - S2z - - (42 * Nu * (-1 + delta + 2 * Nu) + - kappa2 * (-438 + (773 - 108 * Nu) * Nu + - delta * (438 + 103 * Nu))) * - params.S2z2) + - r * (params.rDOT2 * - (Complex(0, 56) * (1 + delta - 2 * Nu) * S1z + - (-56 * (1 + delta - 2 * Nu) * Nu + - kappa1 * (291 * (1 + delta) - - 2 * (445 + 154 * delta) * Nu + - 24 * params.eta2)) * - params.S1z2 + - 4 * Nu * (-3 + 44 * Nu) * S1z * S2z + - S2z * (Complex(0, -56) * (-1 + delta + 2 * Nu) + - 56 * Nu * (-1 + delta + 2 * Nu) * S2z + - kappa2 * - (291 - 890 * Nu + 24 * params.eta2 + - delta * (-291 + 308 * Nu)) * - S2z)) + - params.PhiDOT2 * params.r2 * - (Complex(0, 196) * (1 + delta - 2 * Nu) * S1z + - (56 * Nu * (7 + 7 * delta + Nu) + - kappa1 * (-153 * (1 + delta) - - 2 * (62 + 215 * delta) * Nu + - 804 * params.eta2)) * - params.S1z2 + - 8 * (60 - 187 * Nu) * Nu * S1z * S2z + - S2z * (Complex(0, -196) * (-1 + delta + 2 * Nu) + - 56 * Nu * (7 - 7 * delta + Nu) * S2z + - kappa2 * - (-153 + 4 * Nu * (-31 + 201 * Nu) + - delta * (153 + 430 * Nu)) * - S2z)) + - 2 * PhiDOT * r * rDOT * - (Complex(0, -1) * - (117 * (1 + delta) * kappa1 - - 434 * (1 + delta) * Nu + - (-23 + 211 * delta) * kappa1 * Nu + - 2 * (14 - 195 * kappa1) * params.eta2) * - params.S1z2 + - 4 * S1z * - (35 * (1 + delta - 2 * Nu) + - Complex(0, 1) * (9 - 209 * Nu) * Nu * S2z) + - S2z * (-140 * (-1 + delta + 2 * Nu) + - Complex(0, 1) * - (-14 * Nu * (-31 + 31 * delta + 2 * Nu) + - kappa2 * (117 * (-1 + delta) + - (23 + 211 * delta) * Nu + - 390 * params.eta2)) * - S2z))))) / - params.r4)) - # + Henry et al. QC spinning hereditary terms - # (((-8 * M_PI * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x4) / 3.)) - - elif vpnorder == 7: - return ( - # Henry et al QC spin terms - # ((3318*params.eta3*(S1z + S2z) + Nu*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*params.S1z3 - 1008*params.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*params.S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*params.S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*params.eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*params.S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. - # + - # Henry et al. ecc+spin terms - ((params.Mtot2 * - (-3 * mass * r * - (Complex(0, -16) * params.rDOT3 * - (Complex(0, 12) * Nu * (-16703 + 4427 * Nu) + - 35 * Nu * - (4578 + Nu * (-4288 + 765 * Nu) + - delta * (3748 + 802 * Nu)) * - S1z + - 35 * - (delta * (942 - 2 * Nu * (1874 + 401 * Nu)) + - Nu * (4578 + Nu * (-4288 + 765 * Nu))) * - S2z - - 32970 * (S1z + delta * S1z + S2z)) + - 4 * PhiDOT * r * params.rDOT2 * - (-338520 * params.eta3 * (S1z + S2z) + - 48930 * (S1z + delta * S1z + S2z - delta * S2z) + - Nu * (Complex(0, 3420696) - - 35 * (14154 + 21167 * delta) * S1z - - 495390 * S2z + 740845 * delta * S2z) + - params.eta2 * (Complex(0, -612336) + - 245 * (3566 - 1565 * delta) * S1z + - 245 * (3566 + 1565 * delta) * S2z)) + - params.PhiDOT3 * params.r3 * - (2515380 * params.eta3 * (S1z + S2z) - - 5 * Nu * - (Complex(0, 1859936) + - 7 * (82329 + 37061 * delta) * S1z + - 7 * (82329 - 37061 * delta) * S2z) - - 128100 * (S1z + delta * S1z + S2z - delta * S2z) + - 4 * params.eta2 * - (Complex(0, 381348) + - 35 * (-18505 + 1777 * delta) * S1z - - 35 * (18505 + 1777 * delta) * S2z)) + - Complex(0, 8) * params.PhiDOT2 * params.r2 * rDOT * - (779100 * params.eta3 * (S1z + S2z) + - 5 * Nu * - (Complex(0, 828806) + - 7 * (4839 + 5971 * delta) * S1z + - 7 * (4839 - 5971 * delta) * S2z) - - 62475 * (S1z + delta * S1z + S2z - delta * S2z) + - params.eta2 * (Complex(0, -976002) + - 35 * (-29599 + 3109 * delta) * S1z - - 35 * (29599 + 3109 * delta) * S2z))) + - 3 * params.r2 * - (Complex(0, 4) * params.PhiDOT2 * params.r2 * params.rDOT3 * - (Complex(0, 2) * (65451 - 350563 * Nu) * Nu + - 105 * Nu * - (6020 + delta * (3114 + 411 * Nu) + - Nu * (-4513 + 9136 * Nu)) * - S1z + - 105 * - (-3 * delta * (-408 + Nu * (1038 + 137 * Nu)) + - Nu * (6020 + Nu * (-4513 + 9136 * Nu))) * - S2z - - 128520 * (S1z + delta * S1z + S2z)) + - PhiDOT * r * params.rDOT4 * - (Complex(0, -128) * Nu * (2487 + 18334 * Nu) - - 105 * Nu * - (8689 + 8 * Nu * (-687 + 1402 * Nu) + - delta * (2143 + 8212 * Nu)) * - S1z + - 105 * - (Nu * (-8689 + 8 * (687 - 1402 * Nu) * Nu) + - delta * (-1470 + Nu * (2143 + 8212 * Nu))) * - S2z + - 154350 * (S1z + delta * S1z + S2z)) - - Complex(0, 6) * params.rDOT5 * - (-11760 * params.eta3 * (S1z + S2z) + - 4 * params.eta2 * - (Complex(0, 57338) + - 35 * (569 + 301 * delta) * S1z + - 35 * (569 - 301 * delta) * S2z) - - 16 * Nu * - (Complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + - 35 * (72 - 71 * delta) * S2z) + - 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + - params.PhiDOT5 * params.r5 * - (2263380 * params.eta3 * (S1z + S2z) + - 3 * Nu * - (Complex(0, 653432) + - 35 * (13283 + 7839 * delta) * S1z + - 35 * (13283 - 7839 * delta) * S2z) - - 219240 * (S1z + delta * S1z + S2z - delta * S2z) + - 4 * params.eta2 * - (Complex(0, -291268) + - 105 * (-7669 + 165 * delta) * S1z - - 105 * (7669 + 165 * delta) * S2z)) + - Complex(0, 14) * params.PhiDOT4 * params.r4 * rDOT * - (385170 * params.eta3 * (S1z + S2z) + - 15 * Nu * - (Complex(0, 16914) + (8633 + 2267 * delta) * S1z + - 8633 * S2z - 2267 * delta * S2z) - - 18630 * (S1z + delta * S1z + S2z - delta * S2z) + - 2 * params.eta2 * - (Complex(0, -67904) + - 15 * (-13932 + 679 * delta) * S1z - - 15 * (13932 + 679 * delta) * S2z)) + - 6 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (-6720 * params.eta3 * (S1z + S2z) + - 5 * Nu * - (Complex(0, -241704) + - 7 * (4377 + 3083 * delta) * S1z + - 7 * (4377 - 3083 * delta) * S2z) - - 36960 * (S1z + delta * S1z + S2z - delta * S2z) + - params.eta2 * (Complex(0, -364384) + - 35 * (5059 - 4753 * delta) * S1z + - 35 * (5059 + 4753 * delta) * S2z))) + - 14 * params.Mtot2 * - (Complex(0, 30) * rDOT * - (15280 * params.eta3 * (S1z + S2z) - - 4 * params.eta2 * - (Complex(0, -111) + 3562 * S1z + 682 * delta * S1z + - 945 * kappa1 * params.S1z3 + - (3562 - 682 * delta + - 945 * (-2 + kappa1) * params.S1z2) * - S2z + - 945 * (-2 + kappa2) * S1z * params.S2z2 + - 945 * kappa2 * params.S2z3) + - Nu * (Complex(0, -27520) + - 378 * - (5 * (1 + delta) * kappa1 + - 3 * (3 + delta) * lambda1) * - params.S1z3 + - (29749 - 13605 * delta) * S2z - - 1512 * (1 + delta) * kappa1 * params.S1z2 * S2z - - 378 * - (5 * (-1 + delta) * kappa2 + - 3 * (-3 + delta) * lambda2) * - params.S2z3 + - S1z * (29749 + 13605 * delta + - 1512 * (-1 + delta) * kappa2 * - params.S2z2)) + - 2 * (-8009 * (1 + delta) * S1z - - 567 * (1 + delta) * lambda1 * params.S1z3 + - (-1 + delta) * S2z * - (8009 + 567 * lambda2 * params.S2z2))) + - PhiDOT * r * - (285840 * params.eta3 * (S1z + S2z) - - 30 * params.eta2 * - (Complex(0, 11504) + 1890 * kappa1 * params.S1z3 + - 23090 * S2z - 1823 * delta * S2z + - 1890 * (-2 + kappa1) * params.S1z2 * S2z + - 1890 * kappa2 * params.S2z3 + - S1z * (23090 + 1823 * delta + - 1890 * (-2 + kappa2) * params.S2z2)) + - 30 * (689 * (1 + delta) * S1z - - 1134 * (1 + delta) * lambda1 * params.S1z3 + - (-1 + delta) * S2z * - (-689 + 1134 * lambda2 * params.S2z2)) + - 2 * Nu * - (Complex(0, 415432) - 66840 * S2z + - 15 * (8 * (-557 + 1342 * delta) * S1z + - 189 * - (5 * (1 + delta) * kappa1 + - 6 * (3 + delta) * lambda1) * - params.S1z3 - - 10736 * delta * S2z - - 2457 * (1 + delta) * kappa1 * params.S1z2 * - S2z + - 2457 * (-1 + delta) * kappa2 * S1z * - params.S2z2 - - 189 * - (5 * (-1 + delta) * kappa2 + - 6 * (-3 + delta) * lambda2) * - params.S2z3)))))) / - (317520. * params.r4)) - # + Henry et al. QC spinning hereditary terms - # (2 * M_PI * (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x4p5) - ) - - else: - return complex(0, 0) - - - -# hQC_l_m() functions contains only the non-spinning hereditary terms at -# particular PN order. - -def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: Any) -> complex: - - EulerGamma = 0.5772156649015329 - x0 = 0.4375079683656479 - b0 = 2 * mass / math.exp(0.5) - r0 = b0 - kappa1 = 1.0 # for black holes kappa and lambda is 1 - kappa2 = 1.0 - delta = math.sqrt(1 - 4 * Nu) - - M_PI = math.pi - M_PI2 = math.pi ** 2 - - # keeping only the hereditary terms - # if vpnorder == 0: - # return (2 * x) - - # elif vpnorder == 2: - # return ((-5.095238095238095 + (55 * Nu) / 21.) * params.x2) - - if vpnorder == 3: - # return (4 * M_PI * params.x2p5) - return (complex(0, 0.6666666666666666) * params.x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * math.log(2) + 12 * math.log(b0) + 18 * math.log(x))) - - # elif vpnorder == 4: - # return ((-2.874338624338624 - (1069 * Nu) / 108. + (2047 * params.eta2) / 756.) * params.x3) - - elif vpnorder == 5: - # return ((complex(0,-48)*Nu - (214*M_PI)/21. + (68*Nu*M_PI)/21.)*params.x3p5) - # return ((2 * (-107 + 34 * Nu) * M_PI * params.x3p5) / 21.) # This is old implementation - return (complex(0, 0.015873015873015872) * (-107 + 34 * Nu) * params.x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * math.log(2) + 12 * math.log(b0) + 18 * math.log(x))) - - elif vpnorder == 6: - # return ((params.x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * Nu) * M_PI) - 13696 * math.log(16 * x))) / 1680.) # This is the old implementation. - - return (params.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) * Nu * S1z + complex(0, 5.333333333333333) * EulerGamma * Nu * S1z - - (8 * M_PI * S1z) / 3. + (8 * Nu * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - - complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * Nu * S2z + - complex(0, 5.333333333333333) * EulerGamma * Nu * S2z - (8 * M_PI * S2z) / 3. + (8 * Nu * M_PI * S2z) / 3. + - (912 * math.log(2)) / 35. - 64 * EulerGamma * math.log(2) + complex(0, 32) * M_PI * math.log(2) - - complex(0, 10.666666666666666) * S1z * math.log(2) + complex(0, 10.666666666666666) * Nu * S1z * math.log(2) - - complex(0, 10.666666666666666) * S2z * math.log(2) + complex(0, 10.666666666666666) * Nu * S2z * math.log(2) - - 64 * math.log(2) * math.log(2) + (88 * math.log(b0)) / 3. - 32 * EulerGamma * math.log(b0) + - complex(0, 16) * M_PI * math.log(b0) - complex(0, 5.333333333333333) * S1z * math.log(b0) + - complex(0, 5.333333333333333) * Nu * S1z * math.log(b0) - complex(0, 5.333333333333333) * S2z * math.log(b0) + - complex(0, 5.333333333333333) * Nu * S2z * math.log(b0) - 64 * math.log(2) * math.log(b0) - - 16 * math.log(b0) * math.log(b0) - (1712 * math.log(r0)) / 105. + (684 * math.log(x)) / 35. - - 48 * EulerGamma * math.log(x) + complex(0, 24) * M_PI * math.log(x) - complex(0, 8) * S1z * math.log(x) + - complex(0, 8) * Nu * S1z * math.log(x) - complex(0, 8) * S2z * math.log(x) + complex(0, 8) * Nu * S2z * math.log(x) - - 96 * math.log(2) * math.log(x) - 48 * math.log(b0) * math.log(x) - 36 * math.log(x) * math.log(x) - - complex(0, 0.4444444444444444) * delta * (S1z - S2z) * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + - 24 * math.log(2) + 12 * math.log(b0) + 18 * math.log(x)))) - - elif vpnorder == 7: - # return (((-2173 - 4990 * Nu + 1120 * params.eta2) * M_PI * params.x4p5) / 378.) # This is the old implementation. - return (complex(0, 0.0004409171075837742) * params.x4p5 * (23903 - 26076 * EulerGamma + 57452 * Nu - - 59880 * EulerGamma * Nu - 18728 * params.eta2 + 13440 * EulerGamma * params.eta2 + - complex(0, 13038) * M_PI + complex(0, 29940) * Nu * M_PI - complex(0, 6720) * params.eta2 * M_PI - - 8316 * kappa1 * params.S1z2 - 8316 * delta * kappa1 * params.S1z2 + 9072 * EulerGamma * kappa1 * params.S1z2 + - 9072 * delta * EulerGamma * kappa1 * params.S1z2 + 16632 * kappa1 * Nu * params.S1z2 - - 18144 * EulerGamma * kappa1 * Nu * params.S1z2 - complex(0, 4536) * kappa1 * M_PI * params.S1z2 - - complex(0, 4536) * delta * kappa1 * M_PI * params.S1z2 + complex(0, 9072) * kappa1 * Nu * M_PI * params.S1z2 - - 33264 * Nu * S1z * S2z + 36288 * EulerGamma * Nu * S1z * S2z - complex(0, 18144) * Nu * M_PI * S1z * S2z - - 8316 * kappa2 * params.S2z2 + 8316 * delta * kappa2 * params.S2z2 + 9072 * EulerGamma * kappa2 * params.S2z2 - - 9072 * delta * EulerGamma * kappa2 * params.S2z2 + 16632 * kappa2 * Nu * params.S2z2 - - 18144 * EulerGamma * kappa2 * Nu * params.S2z2 - complex(0, 4536) * kappa2 * M_PI * params.S2z2 + - complex(0, 4536) * delta * kappa2 * M_PI * params.S2z2 + complex(0, 9072) * kappa2 * Nu * M_PI * params.S2z2 - - 52152 * math.log(2) - 119760 * Nu * math.log(2) + 26880 * params.eta2 * math.log(2) + - 18144 * kappa1 * params.S1z2 * math.log(2) + 18144 * delta * kappa1 * params.S1z2 * math.log(2) - - 36288 * kappa1 * Nu * params.S1z2 * math.log(2) + 72576 * Nu * S1z * S2z * math.log(2) + - 18144 * kappa2 * params.S2z2 * math.log(2) - 18144 * delta * kappa2 * params.S2z2 * math.log(2) - - 36288 * kappa2 * Nu * params.S2z2 * math.log(2) + 12 * (-2173 - 4990 * Nu + 1120 * params.eta2 + - 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - - 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * math.log(b0) + 18 * (-2173 - 4990 * Nu + - 1120 * params.eta2 + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - - 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * math.log(x))) - - # 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 * (params.x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * Nu) + - 12 * (628830397253 + 1854914893791 * Nu + 421984442880 * math.log(2)) + 415134720 * EulerGamma * (6099 - 40277 * Nu + complex(0, 70) * (107 - 13 * Nu) * M_PI + 280 * (-107 + 13 * Nu) * math.log(2)) + - 35 * (-28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) + - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2 - 955450349568 * Nu * math.log(2) + - 3321077760 * (-107 + 13 * Nu) * math.log(2) * math.log(2) - complex(0, 5930496) * M_PI * (6099 - 74773 * Nu + 280 * (-107 + 13 * Nu) * math.log(2))) - 5700491596800 * math.log(mass) + - 6966444925440 * math.log(x) + 69189120 * (420 * (-107 + 13 * Nu) * math.log(b0) * math.log(b0) + - 420 * (-107 + 13 * Nu) * math.log(mass) * math.log(mass) - 14 * math.log(mass) * (-11803 * Nu + - 60 * EulerGamma * (-107 + 13 * Nu) + complex(0, 30) * (107 - 13 * Nu) * M_PI + - 120 * (-107 + 13 * Nu) * math.log(2) + 90 * (-107 + 13 * Nu) * math.log(x)) + 14 * math.log(b0) * (5885 - 6420 * EulerGamma - 11803 * Nu + 780 * EulerGamma * Nu + complex(0, 3210) * M_PI - - complex(0, 390) * Nu * M_PI + 120 * (-107 + 13 * Nu) * math.log(2) + (6420 - 780 * Nu) * math.log(mass) + - 90 * (-107 + 13 * Nu) * math.log(x)) + 5 * math.log(x) * (-74149 * Nu + 252 * EulerGamma * (-107 + 13 * Nu) - - complex(0, 126) * (-107 + 13 * Nu) * M_PI + 504 * (-107 + 13 * Nu) * math.log(2) + - 189 * (-107 + 13 * Nu) * math.log(x)) + 84672 * Nu * math.log(x0))))) - - # return ((params.x5 * (276756480 * EulerGamma * (11449 + 19105 * Nu) - 12 * (846557506853 + 1008017482431 * Nu) + 35 * (28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) - complex(0, 3953664) * (11449 + 109657 * Nu) * M_PI - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2) + 138378240 * (11449 + 19105 * Nu) * math.log(16 * x))) / 7.62810048e10) - - else: - return complex(0, 0) - - -def hl_2_m_2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, - PhiDOT: float, R: float, vpnorder: int, S1z: float, - S2z: float, x: float, params: Any) -> complex: - - if vpnorder < 0 or vpnorder > 8: - raise ValueError("Error in hl_2_m_2: Input PN order parameter should be between [0, 8].") - - else: - # Calculate the leading amplitude coefficient - amplitude = (4 * mass * Nu * math.sqrt(math.pi / 5.0)) / R - - # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms - waveform_modes = ( - hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + - hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) - ) - - # cpolar(r, theta) in C is equivalent to cmath.rect(r, theta) in Python - phase_factor = cmath.rect(1.0, -2 * Phi) - - return amplitude * waveform_modes * phase_factor - -def hl_2_m_min2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, - PhiDOT: float, R: float, vpnorder: int, S1z: float, - S2z: float, x: float, params: Any) -> complex: - - if vpnorder < 0 or vpnorder > 8: - raise ValueError("Error in hl_2_m_min2: Input PN order parameter should be between [0, 8].") - - else: - # Calculate the leading amplitude coefficient - amplitude = (4 * mass * Nu * math.sqrt(math.pi / 5.0)) / R - - # Sum the GO and QC terms, then apply the complex conjugate for the m = -2 mode - waveform_modes = ( - hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + - hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) - ).conjugate() - - # Calculate the phase factor with positive 2 * Phi - phase_factor = cmath.rect(1.0, 2 * Phi) - - return amplitude * waveform_modes * phase_factor \ No newline at end of file diff --git a/esigmapy/python_codes/ESIGMA_PNInspiral.py b/esigmapy/python_codes/ESIGMA_PNInspiral.py deleted file mode 100644 index caba6f5..0000000 --- a/esigmapy/python_codes/ESIGMA_PNInspiral.py +++ /dev/null @@ -1,2823 +0,0 @@ -""" -ESIGMA_PNInspiral.py -==================== -Python translation of ENIGMA_PNInspiral.c - -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 -> math.pi -* M_PI2 -> math.pi**2 (macro used in original) -* M_PI3 -> math.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 math -from math import pi, sqrt, log, cos, sin - -# ------ Constants -------------------------------------------------- -EULER_GAMMA = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant -LOG2 = 0.693147180559945309417232121458 -LOG3 = 1.09861228866810969139524523692 -LOG4 = 1.38629436111989061883446424292 -LOG5 = 1.60943791243410037460075933323 -REAL8_FAIL_NAN = float('nan') - -# ------ 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 * 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 * 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 * 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 - e2 = e * e - pre = 192.0 * sqrt(ef) / (985.0 * e2) - return pre * (sqrt(ef) * phi_e(e) - phi_e_tilde(e)) - - -def psi_e_rad(e: float) -> float: - ef = 1.0 - e * e - e2 = e * e - pf1 = 18816.0 / (55691.0 * e2 * sqrt(ef)) - pf2 = 16382.0 * sqrt(ef) / (55691.0 * e2) - b1 = sqrt(ef) * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) - b2 = sqrt(ef) * psi_e(e) - psi_e_tilde(e) - return pf1 * b1 + pf2 * b2 - - -def zed_e_rad(e: float) -> float: - ef = 1.0 - e * e - e2 = e * e - pf1 = 924.0 / (19067.0 * e2 * sqrt(ef)) - pf2 = 12243.0 * sqrt(ef) / (76268.0 * e2) - b1 = -ef * sqrt(ef) * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) - b2 = sqrt(ef) * zed_e(e) - zed_e_tilde(e) - return pf1 * b1 + pf2 * b2 - - -def kappa_e_rad(e: float) -> float: - ef = 1.0 - e * e - e2 = e * e - den = 769.0 / 96.0 - 3059665.0 * LOG2 / 700566.0 + 8190315.0 * LOG3 / 1868176.0 - pf = sqrt(ef) / e2 - b1 = sqrt(ef) * 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 = 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 = 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)""" - 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 * sqrt(ef) - e0 = 2.0 * eta * 96.0 / 15.0 - return (num / den) if e else e0 - -## --------- 1PN terms ------------ - -def x_dot_1pn(e: float, eta: float) -> float: - """Eq. (A27)""" - 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 * sqrt(ef) - num = -eta * (t0 + t2 + t4 + t6) - e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) - return (num / den) if e else e0 - -## --------- 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)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - ef = 1.0 - e2 - M = m1 + m2 - if e: - 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 * ef**5 * M**4) - else: - 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) - - -def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: - """Eq. (A28)""" - pre = eta * x**6 * sqrt(x) - if e: - return pre * 256.0 * pi * phi_e(e) / 5.0 - else: - return pre * 256.0 * pi / 5.0 - -## --------- 2PN terms ------------ - -def x_dot_2pn(e: float, eta: float) -> float: - """Eq. (A29)""" - - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e4 * e4 - - ef = 1.0 - e2 - eta2 = eta * eta - - # Small-e limit - e0_lim = eta * (68206.0 / 2835.0 + - 27322.0 * eta / 315.0 + - 1888.0 * eta2 / 45.0) - - if e: return e0_lim - else: - sqrt_ef = 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 - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - ef = 1.0 - e2 - ef_sqrt = sqrt(ef) - ef_55 = ef**4 * ef * ef_sqrt - M2 = (m1 + m2)**2 - M4 = M2**2 - pre = 64.0 * eta / 5.0 - - if e: - 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) - else: - return pre * ( - ((1 + 80*kappa1)*m1*m1*S1z*S1z + - 158*m1*m2*S1z*S2z + - (1 + 80*kappa2)*m2*m2*S2z*S2z) - ) / (16.0 * M2) - -## --------- 2.5PN terms ------------ - -def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: - """See Huerta et al.""" - ef = 1.0 - e*e - ef2 = ef * ef - e2 = e * e - pre = eta * x**7 * sqrt(x) - - if e: - b1 = 256.0 * pi * phi_e(e) / ef - b2 = (-17599.0 * pi * psi_n(e) / 35.0 - - 2268.0 * eta * pi * zed_n(e) / 5.0 - - 788.0 * e2 * pi * phi_e_rad(e) / ef2) - return pre * (b1 + 2.0*b2/3.0) - else: - b1e0 = 256.0 * pi - b2e0 = -17599.0 * pi / 35.0 - 2268.0 * eta * 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)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e6 * e2 - ef = 1.0 - e2 - ef_sqrt = sqrt(ef) - M2 = (m1 + m2)**2 - M4 = M2 * M2 - M6 = M4 * M2 - pre = 64.0 * eta / 5.0 - - if e: - 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 - ) - -## --------- 3PN terms ------------ - -def x_dot_hereditary_3(e: float, eta: float, x: float) -> float: - """See Huerta et al.""" - pi2 = pi * pi - pre = eta * x**8 - - if e: - x_3_term = (64.0 * - (-116761.0 * kappa_e(e) + - (19600.0*pi2 - 59920.0*EULER_GAMMA - 59920.0*LOG4 - 89880.0*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*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.)""" - - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e4 * e4 - e10 = e8 * e2 - - ef = 1.0 - e2 - eta2 = eta * eta - pi2 = pi * 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 * log(x) / 525.0 - ) - - if abs(e)<1e-12: return e0_lim - - sqrt_ef = 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 - log((1.0 / ef) + (1.0 / sqrt_ef)) - 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)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e6 * e2 - ef = 1.0 - e2 - ef_sqrt = sqrt(ef) - ef_65 = ef**6 * ef_sqrt - M2 = (m1 + m2)**2 - M4 = M2 * M2 - pre = 64.0 * eta / 5.0 - - if e: - return ( - -9.645061728395062e-6 * - m1 * m2 * 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 * 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 - - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e6 * e2 - - e_fact = 1.0 - e2 - e_fact_sqrt = sqrt(e_fact) - - e_fact_65 = (e_fact**6) * e_fact_sqrt - - M = m1 + m2 - M2 = M * M - M4 = M2 * M2 - M6 = M4 * 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) - ) - - # --- 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 e 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 * ( - 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 e 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 e else val_e0 - - -def x_dot_3_5_pn(e: float, eta: float) -> float: - """3.5PN non-spinning""" - val_e0 = (64.0 * eta * 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 e else val_e0 - -## --------- 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*pi**2/126.0 - - eta*(1472377 + 928158*eta)*pi**2/16128.0 + - 127751*LOG2/1470.0 - 47385*LOG3/1568.0 + - eta*(-850042*LOG2/2205.0 + 47385*LOG3/392.0) + - (124741 - 582500*eta)*log(x)/8820.0 - ) - val_e = val_e0 #placeholder for e-dependent expression - return val_e if e 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 * - 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 e else val_e0 - - -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 e 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*pi**3/12.0 - - pi * (700*eta*(3098001198 + eta*(525268513 + 289286988*eta)) + - 145786798080*euler + - 3*(-343801320119 + 97191198720*LOG2)) / 2.2353408e9 - - 3424*pi*log(x)/105.0 - ) - val_e = val_e0 #placeholder for e-dependent expression - return val_e if e 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 = pi * 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 * 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 * log(x)) / 8820.0 - ) - - + 0.071630658436214 * ( - 12774.514362657092 - - 39782.929590804066 * eta - + 346.61235705608044 * eta2 - + 2.068222621184919 * eta3 - + eta4 - - 4169.536804308797 * eta * 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 not e: - return 0.0 - num = -e * eta * (121.0*e2 + 304.0) - den = 15.0 * ef**2 * sqrt(ef) - return num / den - -## ---------- 1PN ------------- - -def e_dot_1pn(e: float, eta: float) -> float: - """Eq. (A32)""" - if not e: - 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 * 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 not e: - return 0.0 - e2 = e * e - e4 = e2 * e2 - ef = 1.0 - e2 - M = m1 + m2 - pre = e / (ef**4) * (m1*m2) / (90.0 * M**4) - 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 not e: - return 0.0 - pre = 32.0 * eta * e * x**7 / 5.0 - return pre * (-985.0 * pi * phi_e_rad(e) / 48.0) - -## ---------- 2PN ------------- - -def e_dot_2pn(e: float, eta: float) -> float: - e_2_pn = 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) - - if e != 0.0: - pre_factor = -e * eta / (e_fact**4 * sqrt(e_fact)) - e_2_pn = pre_factor * ( - zero_term - + e_2_term - + e_4_term - + e_6_term - + sqrt(e_fact) * (zero_rt + e_2_rt + e_4_rt) - ) - else: - e_2_pn = 0.0 - - 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 not e: - return 0.0 - kappa1 = 1.0; kappa2 = 1.0 - e2 = e * e - e4 = e2 * e2 - ef = 1.0 - e2 - ef_45 = ef**4 * 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 e == 0.0: - 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 = 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 not e: - return 0.0 - pre = 32.0 * eta * e * x**4 * x**4 * sqrt(x) / 5.0 - a2 = (55691.0 * psi_e_rad(e) / 1344.0 + 19067.0 * eta * zed_e_rad(e) / 126.0) * pi - return pre * a2 - -## --------- 3PN ------------- - -def e_rad_hereditary_3(e: float, eta: float, x: float) -> float: - if not e: - return 0.0 - pre = 32.0 * eta * e * x**7 / 5.0 - x32 = x * 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*pi**2/3.0 - 1712.0*EULER_GAMMA/105.0 - 1712.0*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 e == 0.0: - 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 = sqrt(e_fact) - - pi_pow_2 = math.pi * math.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 - ) - * math.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 not e: - return 0.0 - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - ef = 1.0 - e2 - ef_sqrt = sqrt(ef) - ef_55 = ef**4 * ef * ef_sqrt - M2 = (m1+m2)**2 - M4 = M2*M2 - return ( - e * m1 * m2 * 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) - - -import math - -def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: - if e == 0.0: - 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 = 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 * 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 = 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 * sqrt(ef) * ef3) - eta2 = eta*eta - pi2 = pi*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 + 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 * cos(u) - 1.0 - -## --------- 0PN ------------- - -def phi_dot_0pn(e: float, eta: float, u: float) -> float: - """Eq. (A11)""" - cf = cosu_factor(e, u) - return 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 - cos(u))) / (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 not e: - return 0.0 - cf = -1.0 + e * cos(u) - return (2*e*(m1*S1z + m2*S2z)*(e - 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 = 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 * sqrt(ef) * ef * cf5) - return pre * (t0+t2+t4+t6+c1+c2+c3 + 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 not e: - 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 - cos(u)) - ) / ((1-e**2)**1.5 * (m1+m2)**2 * (-1+e*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 = sqrt(e_fact) - - M = m1 + m2 - M_fact_2 = M * M - M_fact_4 = M_fact_2 * M_fact_2 - - cos_u = 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 = 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 = 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 * 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 - + 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 = sqrt(e_fact) - M_fact_2 = (m1 + m2) ** 2 - M_fact_4 = M_fact_2 * M_fact_2 - cos_u = 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 * 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*cos(u)) / ef - b2 = (-18.0 + 2.0*eta - e*(6.0 - 7.0*eta)*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))*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*cos(u)) - d1 = 6.0*ef**2 - n2 = (72.0*(-4.0+7.0*eta) + - 36.0*sqrt(ef)*(-5.0+2.0*eta)*(2.0+e*cos(u)) + - ef*(72.0+30.0*eta+8.0*eta2 + e*(-72.0+7*(33.0-5.0*eta)*eta)*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*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=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*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))*cos(u))) - ) / (6.0 * (-1+e2)**3 * M4) - -## ---------- 3PN -------------------- - -def rel_sep_3pn(e: float, u: float, eta: float) -> float: - pi_pow_2 = pi * 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 - ) * cos(u) - - term4_sqrt = 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 - ) * 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 = 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 = 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 - - -# ============ 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 - sqrt(1.0 - 4.0*eta)) / (2.0*eta) - - -# ================== 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 - xsqx = x * sqrt(x) - x5 = x**5 - - # ------------------------- - # 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) * x2 * sqrt(x) - - 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) * x3 * sqrt(x) - inst += x_dot_3_5_pn(e, eta) * x3 * sqrt(x) - inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * sqrt(x) - inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * sqrt(x) - - 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) - - if radiation_pn_order >= 9: - inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * sqrt(x) - - 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 - xsqx = x * sqrt(x) - - # ------------------------- - # 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) * x2 * sqrt(x) - - if radiation_pn_order >= 6: - inst += e_dot_3pn(e, eta, x) * x3 - inst += e_dot_3_5pn(e, eta) * x3 * sqrt(x) - 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 += 0.0 # placeholder for higher order terms, not implemented - - 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 = 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 = 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 > math.pi: - mean_anomaly -= 2 * math.pi - while mean_anomaly < -math.pi: - mean_anomaly += 2 * math.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 * 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 > math.pi: - l -= 2 * math.pi - while l < -math.pi: - l += 2 * math.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 < math.pi / 3.0): - trial = l / abs(1.0 - e) - if trial * trial > 6.0 * abs(1.0 - e): - # cubic term is dominant - if l < math.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 * sin(u) - l - while abs(newt_err) > newt_thresh: - u -= newt_err / (1.0 - e * cos(u)) - newt_err = u - e * 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 e != 0: - 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 * sqrt(x) - - # Check for NaN (equivalent to XLAL_REAL8_FAIL_NAN) - if math.isnan(dydt[0]) or math.isnan(dydt[1]): - # Raise an exception or return a specific error code - raise ValueError("ODE derivative calculated as NaN") - - return dydt - From 05b7739933fbb14402cef7282bd6136f4c271956 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Wed, 22 Apr 2026 23:01:30 +0530 Subject: [PATCH 09/36] Added 31 and 44 terms Co-authored-by: Copilot --- esigmapy/python_codes/esigma_go_terms.py | 554 ++++++++++++++++++++++- 1 file changed, 553 insertions(+), 1 deletion(-) diff --git a/esigmapy/python_codes/esigma_go_terms.py b/esigmapy/python_codes/esigma_go_terms.py index 25cd460..199feeb 100644 --- a/esigmapy/python_codes/esigma_go_terms.py +++ b/esigmapy/python_codes/esigma_go_terms.py @@ -573,7 +573,7 @@ def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, else: return complex(0, 0) -# hQC_l_m() functions contains only the non-spinning hereditary terms at +# hQC_l_m() functions contain only the non-spinning hereditary terms at # particular PN order. def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, @@ -2540,6 +2540,558 @@ def hGO_3_m_1( else: return 0.0 + 0.0j +def hQC_3_m_1( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + + delta: float = np.sqrt(1.0 - 4.0 * Nu) + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + r0: float = b0 + + if vpnorder == 4: + return ( + -0.005555555555555556 * ( + delta * params.x3 * ( + -97.0 + 60.0 * EulerGamma - 30.0j * np.pi + + 60.0 * np.log(2.0) + 60.0 * np.log(b0) + 90.0 * np.log(x) + ) + ) / np.sqrt(14.0) + ) + + elif vpnorder == 6: + return ( + (delta * params.x4 * ( + -1552.0 + 960.0 * EulerGamma - 6487.0 * Nu + 420.0 * EulerGamma * Nu - + 480.0j * np.pi - 210.0j * Nu * np.pi + 960.0 * np.log(2.0) + + 420.0 * Nu * np.log(2.0) + 60.0 * (16.0 + 7.0 * Nu) * np.log(b0) + + 90.0 * (16.0 + 7.0 * Nu) * np.log(x) + )) / (1080.0 * np.sqrt(14.0)) + ) + + elif vpnorder == 7: + numerator_7 = ( + -9.44822373393802e-6 * ( + params.x4p5 * ( + 53132.0j * delta - 92232.0j * delta * EulerGamma + 35280.0j * delta * EulerGamma**2 - + 46116.0 * delta * np.pi + 35280.0 * delta * EulerGamma * np.pi - 2940.0j * delta * np.pi**2 + + 57036.0 * S1z + 57036.0 * delta * S1z - 35280.0 * EulerGamma * S1z - 35280.0 * delta * EulerGamma * S1z - + 114513.0 * Nu * S1z - 143031.0 * delta * Nu * S1z + 97020.0 * EulerGamma * Nu * S1z + + 114660.0 * delta * EulerGamma * Nu * S1z + 17640.0j * np.pi * S1z + 17640.0j * delta * np.pi * S1z - + 48510.0j * Nu * np.pi * S1z - 57330.0j * delta * Nu * np.pi * S1z - 57036.0 * S2z + + 57036.0 * delta * S2z + 35280.0 * EulerGamma * S2z - 35280.0 * delta * EulerGamma * S2z + + 114513.0 * Nu * S2z - 143031.0 * delta * Nu * S2z - 97020.0 * EulerGamma * Nu * S2z + + 114660.0 * delta * EulerGamma * Nu * S2z - 17640.0j * np.pi * S2z + 17640.0j * delta * np.pi * S2z + + 48510.0j * Nu * np.pi * S2z - 57330.0j * delta * Nu * np.pi * S2z - 92232.0j * delta * np.log(2.0) + + 70560.0j * delta * EulerGamma * np.log(2.0) + 35280.0 * delta * np.pi * np.log(2.0) - + 35280.0 * S1z * np.log(2.0) - 35280.0 * delta * S1z * np.log(2.0) + 97020.0 * Nu * S1z * np.log(2.0) + + 114660.0 * delta * Nu * S1z * np.log(2.0) + 35280.0 * S2z * np.log(2.0) - 35280.0 * delta * S2z * np.log(2.0) - + 97020.0 * Nu * S2z * np.log(2.0) + 114660.0 * delta * Nu * S2z * np.log(2.0) + + 35280.0j * delta * np.log(2.0)**2 - 2490264.0j * delta * np.log(3.0) + + 1905120.0j * delta * EulerGamma * np.log(3.0) + 952560.0 * delta * np.pi * np.log(3.0) - + 317520.0 * S1z * np.log(3.0) - 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + + 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - 317520.0 * delta * S2z * np.log(3.0) - + 1508220.0 * Nu * S2z * np.log(3.0) + 396900.0 * delta * Nu * S2z * np.log(3.0) + + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + 952560.0j * delta * np.log(3.0)**2 + + 35280.0j * delta * np.log(b0)**2 + 21840.0j * delta * np.log(r0) - 138348.0j * delta * np.log(x) + + 105840.0j * delta * EulerGamma * np.log(x) + 52920.0 * delta * np.pi * np.log(x) - 52920.0 * S1z * np.log(x) - + 52920.0 * delta * S1z * np.log(x) + 145530.0 * Nu * S1z * np.log(x) + 171990.0 * delta * Nu * S1z * np.log(x) + + 52920.0 * S2z * np.log(x) - 52920.0 * delta * S2z * np.log(x) - 145530.0 * Nu * S2z * np.log(x) + + 171990.0 * delta * Nu * S2z * np.log(x) + 105840.0j * delta * np.log(2.0) * np.log(x) + + 79380.0j * delta * np.log(x)**2 + + 588.0 * np.log(b0) * ( + -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * np.pi + + 15.0 * (-4.0 - 4.0 * delta + 11.0 * Nu + 13.0 * delta * Nu) * S1z + + 60.0 * S2z - 60.0 * delta * S2z - 165.0 * Nu * S2z + 195.0 * delta * Nu * S2z + + 120.0j * delta * np.log(2.0) + 180.0j * delta * np.log(x) + ) + ) + ) + ) + return numerator_7 / np.sqrt(14.0) + + else: + return 0.0 + 0.0j + +def hl_3_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + # Maintaining the logic of the C-source error check + raise ValueError( + "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module + go_component: complex = hGO_3_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -1 * Phi) -> exp(-i * Phi) + phase_factor: complex = np.exp(-1j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_3_m_min1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + # Maintaining the logic of the C-source error check + raise ValueError( + "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module + go_component: complex = hGO_3_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + amplitude_factor = (go_component + qc_component).conjugate() + + # cpolar(1, 1 * Phi) -> exp(i * Phi) + phase_factor: complex = np.exp(1j * Phi) + + return pre_factor * amplitude_factor * phase_factor + +# H44 + +def hGO_4_m_4( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt((1 - 4 * Nu) + 0j) + 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 + + if vpnorder == 2: + return ( + np.sqrt(0.7142857142857143) * (-1 + 3 * Nu) * + (7 * params.Mtot2 + 6 * params.r2 * combination_a4 + + 3 * mass * r * + (17 * params.PhiDOT2 * params.r2 + + 18j * PhiDOT * r * rDOT - 6 * params.rDOT2)) + / (36.0 * params.r2) + ) + + elif vpnorder == 4: + return ( + (40 * params.Mtot3 * (314 - 987 * Nu + 195 * params.eta2) - + 60 * (23 - 159 * Nu + 291 * params.eta2) * params.r3 * + combination_b * combination_a5 + + params.Mtot2 * r * + ((53143 - 199660 * Nu + 127500 * params.eta2) * params.PhiDOT2 * params.r2 + + 24j * (967 - 4615 * Nu + 5935 * params.eta2) * PhiDOT * r * rDOT - + 10 * (290 - 2033 * Nu + 4365 * params.eta2) * params.rDOT2) - + 3 * mass * params.r2 * + ((613 - 920 * Nu + 6420 * params.eta2) * params.PhiDOT4 * params.r4 - + 8j * (-976 + 1745 * Nu + 3150 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + + 2 * (-6141 + 8980 * Nu + 31500 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 4j * (-1853 + 1730 * Nu + 13230 * params.eta2) * PhiDOT * r * params.rDOT3 - + 20 * (-83 + 30 * Nu + 762 * params.eta2) * params.rDOT4)) + / (1584.0 * np.sqrt(35) * params.r3) + ) + + elif vpnorder == 5: + term1 = ( + params.Mtot2 * Nu * + (6 * mass * (-43j * PhiDOT * r + 9 * rDOT) + + r * (-734j * params.PhiDOT3 * params.r3 + + 129 * params.PhiDOT2 * params.r2 * rDOT + + 156j * PhiDOT * r * params.rDOT2 - + 26 * params.rDOT3)) + / (24.0 * np.sqrt(35) * params.r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + params.Mtot2 * + (-3j * params.PhiDOT2 * params.r3 * rDOT * + ((-250 + 1221 * Nu - 1512 * params.eta2 + + delta * (-250 + 849 * Nu)) * S1z + + (-250 + delta * (250 - 849 * Nu) + 1221 * Nu - + 1512 * params.eta2) * S2z) - + 2 * mass * PhiDOT * r * + ((-130 + 757 * Nu - 1224 * params.eta2 + + delta * (-130 + 513 * Nu)) * S1z + + (-130 + delta * (130 - 513 * Nu) + 757 * Nu - + 1224 * params.eta2) * S2z) - + 2j * mass * rDOT * + ((-100 + 577 * Nu - 864 * params.eta2 + + delta * (-100 + 333 * Nu)) * S1z + + (-100 + delta * (100 - 333 * Nu) + 577 * Nu - + 864 * params.eta2) * S2z) - + 6 * params.PhiDOT3 * params.r4 * + ((-65 + 263 * Nu - 291 * params.eta2 + + delta * (-65 + 282 * Nu)) * S1z + + (-65 + delta * (65 - 282 * Nu) + 263 * Nu - + 291 * params.eta2) * S2z) + + 12 * PhiDOT * params.r2 * params.rDOT2 * + ((-40 + 201 * Nu - 252 * params.eta2 + + delta * (-40 + 129 * Nu)) * S1z + + (-40 + delta * (40 - 129 * Nu) + 201 * Nu - + 252 * params.eta2) * S2z) + + 6j * r * params.rDOT3 * + ((-20 + 107 * Nu - 144 * params.eta2 + + delta * (-20 + 63 * Nu)) * S1z + + (-20 + delta * (20 - 63 * Nu) + 107 * Nu - + 144 * params.eta2) * S2z)) + / (72.0 * np.sqrt(35) * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + (10 * params.Mtot4 * + (-4477296 + 12734393 * Nu - 6895 * params.eta2 + 1043805 * params.eta3) + + 3150 * (-367 + 4337 * Nu - 17462 * params.eta2 + 23577 * params.eta3) * + params.r4 * combination_b2 * combination_a6 + + 2 * params.Mtot3 * r * + ((-36967579 + 245501977 * Nu - 459916170 * params.eta2 + + 150200680 * params.eta3) * params.PhiDOT2 * params.r2 + + 4j * (7571073 - 10780154 * Nu - 56898800 * params.eta2 + + 43665510 * params.eta3) * PhiDOT * r * rDOT - + 10 * (1283609 - 5800627 * Nu + 3725295 * params.eta2 + + 4771935 * params.eta3) * params.rDOT2) - + params.Mtot2 * params.r2 * + ((-28258134 + 3245207 * Nu + 144051250 * params.eta2 + + 136991820 * params.eta3) * params.PhiDOT4 * params.r4 - + 24j * (2371982 - 7733376 * Nu - 7948185 * params.eta2 + + 9074870 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + + 7 * (6557973 - 50558069 * Nu + 59901380 * params.eta2 + + 104752320 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 168j * (52044 - 1084807 * Nu + 1849450 * params.eta2 + + 4171730 * params.eta3) * PhiDOT * r * params.rDOT3 - + 35 * (1083 - 1246819 * Nu + 2524240 * params.eta2 + + 5995845 * params.eta3) * params.rDOT4) - + 105 * mass * params.r3 * + ((116396 - 551405 * Nu + 560658 * params.eta2 + + 293036 * params.eta3) * params.PhiDOT6 * params.r6 + + 2j * (158192 - 670661 * Nu + 177718 * params.eta2 + + 2163976 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + + (-393665 + 1322392 * Nu + 1589680 * params.eta2 - + 8622660 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 - + 8j * (-23048 + 209397 * Nu - 487057 * params.eta2 + + 260396 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 - + (630647 - 3391000 * Nu + 2501958 * params.eta2 + + 7664096 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - + 2j * (218975 - 1037408 * Nu + 148970 * params.eta2 + + 3699480 * params.eta3) * PhiDOT * r * params.rDOT5 + + 10 * (10233 - 44864 * Nu - 13050 * params.eta2 + + 203280 * params.eta3) * params.rDOT6)) + / (1.44144e6 * np.sqrt(35) * params.r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + np.sqrt(0.7142857142857143) * params.Mtot3 * (-1 + 3 * Nu) * + (12 * mass + + r * (53 * params.PhiDOT2 * params.r2 + + 26j * PhiDOT * r * rDOT - 8 * params.rDOT2)) * + (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + + S2z * (4 * Nu * S1z + kappa2 * S2z - delta * kappa2 * S2z - + 2 * kappa2 * Nu * S2z)) + / (48.0 * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + # Henry et al. ecc+spin terms + return ( + params.Mtot2 * + (14 * params.Mtot2 * + (120 * params.eta3 * + (24635 * PhiDOT * r + 18657j * rDOT) * + (S1z + S2z) - + 60 * (10039 * PhiDOT * r + 7706j * rDOT) * + (S1z + delta * S1z + S2z - delta * S2z) + + 5 * Nu * + (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 * params.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 * params.r2 * + (1680 * params.eta3 * + (2833 * params.PhiDOT5 * params.r5 + + 18796j * params.PhiDOT4 * params.r4 * rDOT - + 13185 * params.PhiDOT3 * params.r3 * params.rDOT2 + + 1186j * params.PhiDOT2 * params.r2 * params.rDOT3 - + 5863 * PhiDOT * r * params.rDOT4 - + 2100j * params.rDOT5) * + (S1z + S2z) - + 1050 * + (454 * params.PhiDOT5 * params.r5 + + 1195j * params.PhiDOT4 * params.r4 * rDOT - + 1950 * params.PhiDOT3 * params.r3 * params.rDOT2 - + 442j * params.PhiDOT2 * params.r2 * params.rDOT3 - + 384 * PhiDOT * r * params.rDOT4 - + 184j * params.rDOT5) * + (S1z + delta * S1z + S2z - delta * S2z) - + 6 * params.eta2 * + (2 * params.PhiDOT5 * params.r5 * + (-2459811j + 35 * (26517 + 10223 * delta) * S1z + + (928095 - 357805 * delta) * S2z) - + 10j * params.rDOT5 * + (35291j + 28 * (2183 + 1155 * delta) * S1z + + (61124 - 32340 * delta) * S2z) + + 1j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (917901j + 1120 * (-191 + 1616 * delta) * S1z - + 1120 * (191 + 1616 * delta) * S2z) + + 5 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (85426j + 7 * (-148363 + 4365 * delta) * S1z - + 7 * (148363 + 4365 * delta) * S2z) - + 4 * PhiDOT * r * params.rDOT4 * + (280067j + 70 * (5844 + 4817 * delta) * S1z - + 70 * (-5844 + 4817 * delta) * S2z) + + 1j * params.PhiDOT4 * params.r4 * rDOT * + (10375501j + 70 * (65831 + 22871 * delta) * S1z - + 70 * (-65831 + 22871 * delta) * S2z)) + + Nu * (-40j * params.rDOT5 * + (12203j + 42 * (843 + 656 * delta) * S1z - + 42 * (-843 + 656 * delta) * S2z) + + 4j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (-266071j + 210 * (-2279 + 1010 * delta) * S1z - + 210 * (2279 + 1010 * delta) * S2z) - + 16 * PhiDOT * r * params.rDOT4 * + (58753j + 105 * (2030 + 1947 * delta) * S1z - + 105 * (-2030 + 1947 * delta) * S2z) + + params.PhiDOT5 * params.r5 * + (-8997592j + 105 * (39959 + 15835 * delta) * S1z - + 105 * (-39959 + 15835 * delta) * S2z) - + 10 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (254228j + 21 * (65293 + 34551 * delta) * S1z - + 21 * (-65293 + 34551 * delta) * S2z) + + 2j * params.PhiDOT4 * params.r4 * rDOT * + (12351083j + 105 * (43193 + 37913 * delta) * S1z - + 105 * (-43193 + 37913 * delta) * S2z))) + + mass * r * + (2520 * params.eta3 * + (8036 * params.PhiDOT3 * params.r3 + + 41814j * params.PhiDOT2 * params.r2 * rDOT - + 30537 * PhiDOT * r * params.rDOT2 - + 9064j * params.rDOT3) * + (S1z + S2z) - + 210 * + (11849 * params.PhiDOT3 * params.r3 + + 31868j * params.PhiDOT2 * params.r2 * rDOT + + 3572 * PhiDOT * r * params.rDOT2 + + 1508j * params.rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) - + 12 * params.eta2 * + (1j * params.PhiDOT2 * params.r2 * rDOT * + (-1150397j + 35 * (154765 + 157449 * delta) * S1z + + 5416775 * S2z - 5510715 * delta * S2z) - + PhiDOT * r * params.rDOT2 * + (2306552j + 35 * (4931 + 110733 * delta) * S1z + + 172585 * S2z - 3875655 * delta * S2z) + + params.PhiDOT3 * params.r3 * + (12461121j + 35 * (18331 + 87381 * delta) * S1z + + 641585 * S2z - 3058335 * delta * S2z) - + 25j * params.rDOT3 * + (36676j + 35 * (137 + 1053 * delta) * S1z + + 4795 * S2z - 36855 * delta * S2z)) + + Nu * (-200j * params.rDOT3 * + (-2501j + 42 * (-308 + 51 * delta) * S1z - + 42 * (308 + 51 * delta) * S2z) + + 2 * PhiDOT * r * params.rDOT2 * + (-1951984j - 105 * (-37907 + 14661 * delta) * S1z + + 105 * (37907 + 14661 * delta) * S2z) + + 8j * params.PhiDOT2 * params.r2 * rDOT * + (-10005028j + 105 * (40991 + 31689 * delta) * S1z - + 105 * (-40991 + 31689 * delta) * S2z) + + params.PhiDOT3 * params.r3 * + (88418488j + 105 * (57793 + 266391 * delta) * S1z - + 105 * (-57793 + 266391 * delta) * S2z)))) + / (332640.0 * np.sqrt(35) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_4_m_4( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + + if vpnorder == 5: + # Pre-factor: Complex(0, 0.07407407407407407) / sqrt(35) + return ( + (0.07407407407407407j * params.x3p5 * ( + 1888.0 - 960.0 * EulerGamma - 5661.0 * Nu + 2880.0 * EulerGamma * Nu + + 480.0j * np.pi - 1440.0j * Nu * np.pi - 2880.0 * np.log(2.0) + + 8640.0 * Nu * np.log(2.0) + 960.0 * (-1.0 + 3.0 * Nu) * np.log(b0) + + 1440.0 * (-1.0 + 3.0 * Nu) * np.log(x) + )) / np.sqrt(35.0) + ) + + elif vpnorder == 7: + # Pre-factor: Complex(0, 1.0020843354176687e-6) / sqrt(35) + return ( + (1.0020843354176687e-6j * params.x4p5 * ( + -752360448.0 + 382556160.0 * EulerGamma + 2620364605.0 * Nu - + 1333248000.0 * EulerGamma * Nu - 898312500.0 * params.eta2 + + 458035200.0 * EulerGamma * params.eta2 - 191278080.0j * np.pi + + 666624000.0j * Nu * np.pi - 229017600.0j * params.eta2 * np.pi + + 1147668480.0 * np.log(2.0) - 3999744000.0 * Nu * np.log(2.0) + + 1374105600.0 * params.eta2 * np.log(2.0) + + 215040.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(b0) + + 322560.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(x) + )) / np.sqrt(35.0) + ) + + else: + return 0.0 + 0.0j + +def hl_4_m_4( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure + go_component: complex = hGO_4_m_4( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params + ) + qc_component: complex = hQC_4_m_4( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -4 * Phi) -> exp(-i * 4 * Phi) + phase_factor: complex = np.exp(-4j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_4_m_min4( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure + go_component: complex = hGO_4_m_4( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params + ) + qc_component: complex = hQC_4_m_4( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + amplitude_factor = (go_component + qc_component).conjugate() + + # cpolar(1, 4 * Phi) -> exp(i * 4 * Phi) + phase_factor: complex = np.exp(4j * Phi) + return pre_factor * amplitude_factor * phase_factor +# H43 From 930fcd0564dda144187cb734a2ebb310ec368310 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Wed, 29 Apr 2026 22:36:44 +0530 Subject: [PATCH 10/36] First implementation of python codes to produce esigma. Waveform generation is successful but high-frequency behavior is divergent -- tested for the (2, 2) mode. --- .ipynb_checkpoints/Untitled-checkpoint.ipynb | 6 + Untitled.ipynb | 33 + build/lib/esigmapy/__init__.py | 41 + build/lib/esigmapy/blend.py | 537 +++ build/lib/esigmapy/generator.py | 1210 ++++++ build/lib/esigmapy/legacy.py | 125 + build/lib/esigmapy/python_codes/__init__.py | 1 + .../esigmapy/python_codes/esigma_go_terms.py | 3343 +++++++++++++++++ .../python_codes/esigma_go_terms_draft.py | 1696 +++++++++ .../python_codes/esigma_pn_inspiral.py | 2844 ++++++++++++++ .../esigmapy/python_codes/esigma_pn_main.py | 501 +++ .../esigmapy/python_codes/generator_python.py | 1211 ++++++ build/lib/esigmapy/surrogate/__init__.py | 4 + build/lib/esigmapy/surrogate/generator.py | 820 ++++ build/lib/esigmapy/surrogate/surrogate.py | 556 +++ build/lib/esigmapy/utils.py | 189 + esigmapy.egg-info/PKG-INFO | 174 + esigmapy.egg-info/SOURCES.txt | 23 + esigmapy.egg-info/dependency_links.txt | 1 + esigmapy.egg-info/entry_points.txt | 2 + esigmapy.egg-info/requires.txt | 7 + esigmapy.egg-info/top_level.txt | 1 + esigmapy/__pycache__/__init__.cpython-311.pyc | Bin 0 -> 2853 bytes esigmapy/__pycache__/blend.cpython-311.pyc | Bin 0 -> 18716 bytes .../__pycache__/generator.cpython-311.pyc | Bin 0 -> 47601 bytes esigmapy/__pycache__/legacy.cpython-311.pyc | Bin 0 -> 7949 bytes esigmapy/__pycache__/utils.cpython-311.pyc | Bin 0 -> 7879 bytes esigmapy/generator.py | 13 +- .../__pycache__/__init__.cpython-311.pyc | Bin 0 -> 180 bytes .../esigma_go_terms_draft.cpython-311.pyc | Bin 0 -> 63964 bytes .../esigma_pn_inspiral.cpython-311.pyc | Bin 0 -> 125694 bytes .../esigma_pn_main.cpython-311.pyc | Bin 0 -> 13950 bytes .../generator_python.cpython-311.pyc | Bin 0 -> 47909 bytes esigmapy/python_codes/esigma_go_terms.py | 246 ++ .../python_codes/esigma_go_terms_draft.py | 1696 +++++++++ esigmapy/python_codes/esigma_pn_inspiral.py | 48 +- esigmapy/python_codes/esigma_pn_main.py | 510 +++ esigmapy/python_codes/generator_python.py | 1241 ++++++ .../ESIGMA_tutorial-checkpoint.ipynb | 670 ++++ .../Untitled-checkpoint.ipynb | 6 + .../esigma_python-checkpoint.ipynb | 359 ++ notebooks/ESIGMA_tutorial.ipynb | 116 +- notebooks/esigma_python.ipynb | 365 ++ 43 files changed, 18564 insertions(+), 31 deletions(-) create mode 100644 .ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 Untitled.ipynb create mode 100644 build/lib/esigmapy/__init__.py create mode 100644 build/lib/esigmapy/blend.py create mode 100644 build/lib/esigmapy/generator.py create mode 100644 build/lib/esigmapy/legacy.py create mode 100644 build/lib/esigmapy/python_codes/__init__.py create mode 100644 build/lib/esigmapy/python_codes/esigma_go_terms.py create mode 100644 build/lib/esigmapy/python_codes/esigma_go_terms_draft.py create mode 100644 build/lib/esigmapy/python_codes/esigma_pn_inspiral.py create mode 100644 build/lib/esigmapy/python_codes/esigma_pn_main.py create mode 100644 build/lib/esigmapy/python_codes/generator_python.py create mode 100644 build/lib/esigmapy/surrogate/__init__.py create mode 100644 build/lib/esigmapy/surrogate/generator.py create mode 100644 build/lib/esigmapy/surrogate/surrogate.py create mode 100644 build/lib/esigmapy/utils.py create mode 100644 esigmapy.egg-info/PKG-INFO create mode 100644 esigmapy.egg-info/SOURCES.txt create mode 100644 esigmapy.egg-info/dependency_links.txt create mode 100644 esigmapy.egg-info/entry_points.txt create mode 100644 esigmapy.egg-info/requires.txt create mode 100644 esigmapy.egg-info/top_level.txt create mode 100644 esigmapy/__pycache__/__init__.cpython-311.pyc create mode 100644 esigmapy/__pycache__/blend.cpython-311.pyc create mode 100644 esigmapy/__pycache__/generator.cpython-311.pyc create mode 100644 esigmapy/__pycache__/legacy.cpython-311.pyc create mode 100644 esigmapy/__pycache__/utils.cpython-311.pyc create mode 100644 esigmapy/python_codes/__pycache__/__init__.cpython-311.pyc create mode 100644 esigmapy/python_codes/__pycache__/esigma_go_terms_draft.cpython-311.pyc create mode 100644 esigmapy/python_codes/__pycache__/esigma_pn_inspiral.cpython-311.pyc create mode 100644 esigmapy/python_codes/__pycache__/esigma_pn_main.cpython-311.pyc create mode 100644 esigmapy/python_codes/__pycache__/generator_python.cpython-311.pyc create mode 100644 esigmapy/python_codes/esigma_go_terms_draft.py create mode 100644 esigmapy/python_codes/esigma_pn_main.py create mode 100644 esigmapy/python_codes/generator_python.py create mode 100644 notebooks/.ipynb_checkpoints/ESIGMA_tutorial-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb create mode 100644 notebooks/esigma_python.ipynb diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000..89e3f04 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c90ab439-6d4c-406b-8602-1bcba3c748b0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "esigmapy-dev", + "language": "python", + "name": "esigmapy-dev" + }, + "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 +} diff --git a/build/lib/esigmapy/__init__.py b/build/lib/esigmapy/__init__.py new file mode 100644 index 0000000..183475f --- /dev/null +++ b/build/lib/esigmapy/__init__.py @@ -0,0 +1,41 @@ +from __future__ import absolute_import + +from . import blend, generator, legacy, utils +from .generator import * + + +def get_version_information(): + import os + + version_file = os.path.join( + os.path.dirname(os.path.dirname(__file__)), "esigmapy/.version" + ) + try: + with open(version_file, "r") as f: + return f.readline().rstrip() + except EnvironmentError: + print("No version information file '.version' found") + + +# pycbc wrapper +import inspect +_params = inspect.signature(get_imr_esigma_waveform).parameters.keys() +def pycbc_esigma(**params): + from pycbc.waveform.waveform import parse_mode_array + + # Pass all parameters the model suppports + pt = {p: params[p] for p in _params if p in params} + pt['f_ref'] = pt['f_lower'] if pt['f_ref'] == 0 else pt['f_ref'] + + gen_wav = get_imr_esigma_waveform + if 'skip_merger' in params: + gen_wav = get_inspiral_esigma_waveform + + modes = params.pop('mode_array', [(2,2), (2,-2)]) + modes = parse_mode_array({'mode_array':modes})['mode_array'] + + return gen_wav(pt.pop('mass1'), pt.pop('mass2'), + pt.pop('f_lower'), pt.pop('delta_t'), + **pt, modes_to_use=modes) + +__version__ = get_version_information() diff --git a/build/lib/esigmapy/blend.py b/build/lib/esigmapy/blend.py new file mode 100644 index 0000000..d2be5ce --- /dev/null +++ b/build/lib/esigmapy/blend.py @@ -0,0 +1,537 @@ +# Copyright (C) 2023 Kartikey Sharma, Prayush Kumar +# +"""Master function to hybridise any complex timeseries using the 'frequency' as a user input, +specifically to be used for gravitational waveform hybridisation, fine-tuned for a single mode. +""" + +import numpy as np +import scipy.optimize +from scipy.integrate import cumulative_trapezoid + + +def find_first_value_location_in_series(frq_timeseries, frq_desired): + if frq_desired < np.min(frq_timeseries): + raise Exception("Desired frequency out of bounds, lower than min frequency") + + if frq_desired > np.max(frq_timeseries): + raise Exception("Desired frequency out of bounds, higher than max frequency") + """ + We reverse the array and traverse it to find the location where the i_th value is more than + the desired value while the i+1_th value is less, hence locating the desired value somewhere + between those two points. We then choose the value closer to the value desired (among i and i+1) + and call it the location of the desired value. + """ + + for idx, f_value in enumerate(frq_timeseries): + if idx != len(frq_timeseries) - 1: + if ( + frq_timeseries[idx] <= frq_desired + and frq_timeseries[idx + 1] >= frq_desired + ): + fr1 = frq_timeseries[idx] + fr2 = frq_timeseries[idx + 1] + + if abs(frq_desired - fr1) <= abs(frq_desired - fr2): + final_idx = idx + else: + final_idx = idx + 1 + break + return final_idx + + +def find_last_value_location_in_series(frq_timeseries, frq_desired): + if frq_desired < np.min(frq_timeseries): + raise Exception( + f"""Desired value {frq_desired} out of bounds, lower than min value {np.min(frq_timeseries)}""" + ) + + if frq_desired > np.max(frq_timeseries): + raise Exception( + f"""Desired value {frq_desired} out of bounds, higher than max value {np.max(frq_timeseries)}""" + ) + """ + We reverse the array and traverse it to find the location where the i_th value is more than + the desired value while the i+1_th value is less, hence locating the desired value somewhere + between those two points. We then choose the value closer to the value desired (among i and i+1) + and call it the location of the desired value. + """ + + reversed_freq_timeseries = frq_timeseries[::-1] + final_idx = len(reversed_freq_timeseries) - 1 + + for idx, f_value in enumerate(reversed_freq_timeseries): + if idx != len(reversed_freq_timeseries) - 1: + if ( + reversed_freq_timeseries[idx] >= frq_desired + and reversed_freq_timeseries[idx + 1] <= frq_desired + ): + fr1 = reversed_freq_timeseries[idx] + fr2 = reversed_freq_timeseries[idx + 1] + + if abs(frq_desired - fr1) <= abs(frq_desired - fr2): + final_idx = idx + else: + final_idx = idx + 1 + break + return len(frq_timeseries) - 1 - final_idx + + +def mismatch_discrete(w1, w2, sample_indices_insp, sample_indices_mr): + w1_d = w1[sample_indices_insp] + w2_d = w2[sample_indices_mr] # can't give the same comb to w2 + w2sq = np.square(np.abs(w2_d)) + # w1sq = np.square(np.abs(w2_d)) # another normalising factor can be (w1sq + w2sq) / 2 + diff = np.abs(w1_d - w2_d) + diffsq = np.square(diff) + mm = 0.5 * (np.sum(diffsq) / np.sum(w2sq)) + return mm + + +def align_in_phase( + inspiral, + merger_ringdown, + sample_indices_insp, + sample_indices_mr, + t1_index_insp, + t2_index_insp, + t1_index_mr, + t2_index_mr, + m_mode=2, + align_merger_to_inspiral=True, +): + if len(inspiral) == 0: + raise IOError( + f"""You passed an inspiral waveform of zero length, to align + with merger-ringdown!""" + ) + if (t2_index_insp + 1 - t1_index_insp) < 1: + raise IOError( + f"""You have passed a very narrow window for the inspiral + waveform's hybridization. As per your input, the inspiral + waveform from index {t1_index_insp} to {t2_index_insp + 1} + should be used to attach merger-ringdown""" + ) + + # Function alignes the two waveforms using the phase, optimised over the attachment region + # m from l,m mode + def optfn_ph(phaseshift_correction): + if align_merger_to_inspiral: + phase_corrected_inspiral = inspiral + phase_corrected_merger_ringdown = merger_ringdown * np.exp( + 1j * m_mode * phaseshift_correction + ) + else: + phase_corrected_inspiral = inspiral * np.exp( + 1j * m_mode * phaseshift_correction + ) + phase_corrected_merger_ringdown = merger_ringdown + m_d = mismatch_discrete( + phase_corrected_inspiral[t1_index_insp : t2_index_insp + 1], + phase_corrected_merger_ringdown[t1_index_mr : t2_index_mr + 1], + sample_indices_insp, + sample_indices_mr, + ) + return m_d + + phase_optimizer = scipy.optimize.minimize(optfn_ph, 0) + phaseshift_required_for_alignment = phase_optimizer.x + + if align_merger_to_inspiral: + aligned = merger_ringdown * np.exp( + 1j * m_mode * phaseshift_required_for_alignment + ) + return (inspiral, aligned, phaseshift_required_for_alignment) + else: + aligned = inspiral * np.exp(1j * m_mode * phaseshift_required_for_alignment) + return (aligned, merger_ringdown, phaseshift_required_for_alignment) + + +def blend_series(x1, x2, t1_index_insp, t2_index_insp, t1_index_mr, t2_index_mr): + assert ( + t1_index_mr - t2_index_mr == t1_index_insp - t2_index_insp + ), "Inconsistent indices passed to blending function" + + # blending fn is an array + blfn_var = np.arange(t1_index_insp, t2_index_insp) + tau = np.square( + ( + np.sin( + (np.pi / 2) + * (blfn_var - t1_index_insp) + / (t2_index_insp - t1_index_insp) + ) + ) + ) + + x_hyb = (1 - tau) * x1[t1_index_insp:t2_index_insp] + tau * x2[ + t1_index_mr:t2_index_mr + ] + return x_hyb + + +def compute_amplitude(waveform): + amplitude = np.abs(waveform) + return amplitude + + +def compute_phase(waveform): + phase = np.unwrap(-np.angle(waveform)) + return phase + + +def compute_frequency(phase, delta_t): + frequency = np.gradient(phase, delta_t) / (2 * np.pi) + return frequency + + +def blend_modes( + inspiral_modes, + merger_ringdown_modes, + inspiral_orbital_frequency, + frq_attach, + frq_width=10.0, + delta_t=1.0 / 4096, + no_sp=8, + modes_to_blend=[(2, 2), (3, 3), (4, 4)], + mode_to_align_by=(2, 2), + blend_using_avg_orbital_frequency=True, + blend_aligning_merger_to_inspiral=True, + include_conjugate_modes=True, + verbose=False, +): + """blend inspiral and merger-ringdown modes + + Inputs + ------ + inspiral_modes: dict + Dictionary indexed by (l, m) containing numpy-like arrays of + complex-valued mode timeseries. + merger_ringdown_modes: dict + Dictionary indexed by (l, m) containing numpy-like arrays of + complex-valued mode timeseries. + + frq_attach: float + Frequency (Hz) at which to align the inspiral and merger-ringdown modes. + frq_width: {10.0, float} + Frequency (Hz) window around the central attachment frequency over which + hybridization of modes is performed. + delta_t: {1/4096, float} + Sample rate for timeseries (Hz) + no_sp: {8, int} + + modes_to_blend: {[(2, 2), (3, 3), (4, 4)], list} + List of modes as tuples of (l, m) values to blend + mode_to_align_by: {(2, 2), tuple} + One specific mode (l, m) value that is to be treated as baseline for + time/phase alignment. We recommend using only the (2, 2) mode for this. + blend_using_avg_orbital_frequency: (False, bool) + Flag that enables use of orbit averaged orbital frequency for all + calculations of inspiral to merger-ringdown attachment + blend_aligning_merger_to_inspiral: (True, bool) + Flag that ensures any phase difference between inspiral and merger-rd + modes is removed by phase shifting the merger-rd portion. If set to + False, the inspiral portion is phase shifted instead + include_conjugate_modes: {True, bool} + When set to True, we also consider (l, -m) modes in addition to (l, m) ones. + verbose: {True, bool} + Set this to True to enable logging output. + """ + if frq_width <= 0: + raise IOError( + f"""You are trying to blend over a frequency window of + negative length (= {frq_width}Hz). Fix this.""" + ) + if blend_using_avg_orbital_frequency: + if len(inspiral_orbital_frequency) != len(inspiral_modes[mode_to_align_by]): + raise IOError( + f"""You asked for hybridization using orbital frequency, but + the orbital frequency array and inspiral modes array have + different lengths: {len(inspiral_orbital_frequency)}, {len(inspiral_modes[mode_to_align_by])}""" + ) + modes_not_aligned_by = modes_to_blend.copy() + if mode_to_align_by in modes_not_aligned_by: + modes_not_aligned_by.remove(mode_to_align_by) + if include_conjugate_modes: + for el, em in modes_to_blend.copy(): + if (el, -em) not in modes_to_blend: + modes_to_blend.append((el, -em)) + for el, em in modes_not_aligned_by.copy(): + if (el, -em) not in modes_not_aligned_by: + modes_not_aligned_by.append((el, -em)) + + # Input checks + for lm in modes_to_blend: + if lm not in inspiral_modes or lm not in merger_ringdown_modes: + raise IOError( + "We cannot blend {} mode as its missing in the input inspiral modes" + " ({}) or the merger ringdown modes ({})".format( + lm, lm in inspiral_modes, lm in merger_ringdown_modes + ) + ) + if verbose: + print("Hybridizing the following modes: {}".format(modes_to_blend)) + print("By aligning {} mode".format(mode_to_align_by)) + print( + "..and inheriting the phase/time shifts for alignment of {} modes".format( + modes_not_aligned_by + ) + ) + + # Get amplitude and phase for all modes + phase_insp = {} + frq_insp = {} + phase_mr = {} + frq_mr = {} + + for el, em in modes_to_blend: + phase_insp[(el, em)] = compute_phase(inspiral_modes[(el, em)]) + frq_insp[(el, em)] = compute_frequency(phase_insp[(el, em)], delta_t) + + phase_mr[(el, em)] = compute_phase(merger_ringdown_modes[(el, em)]) + frq_mr[(el, em)] = compute_frequency(phase_mr[(el, em)], delta_t) + + if verbose: + print( + f"INSPIRAL mode ({el}, {em}) goes from {np.min(frq_insp[(el, em)])}Hz to" + f" {np.max(frq_insp[(el, em)])}Hz" + ) + print( + f"MERGER mode ({el}, {em}) goes from {np.min(frq_mr[(el, em)])}Hz to" + f" {np.max(frq_mr[(el, em)])}Hz" + ) + + """ first we need to find the attachment region, based on the frequency """ + + """ + We search left to right in merger-ringdown to avoid frequency fluctuations + after the merger, and right to left in inspiral to avoid frequency degeneracy + caused by eccentricity + """ + el, em = mode_to_align_by + + t1_index_mr = find_first_value_location_in_series( + frq_mr[(el, em)], frq_attach - frq_width / 2 + ) + + t2_index_mr = find_first_value_location_in_series( + frq_mr[(el, em)], frq_attach + frq_width / 2 + ) + """ + For eccentric inspiral, there will be multiple instances of the + same frequency. Pick the one having the highest index value (i.e. + the one at the rightmost occurance in time) + + """ + if blend_using_avg_orbital_frequency: + t2_index_insp = find_last_value_location_in_series( + inspiral_orbital_frequency, (frq_attach + frq_width / 2) / em + ) + if verbose > 1: + print( + f"""Hybridizing using orbital frequency. Frequency + {frq_attach + frq_width / 2}Hz found at {t2_index_insp}. + The same frequency would have been found at index + {find_last_value_location_in_series(frq_insp[(el, em)], + frq_attach + frq_width / 2)} of mode frequency evolution. + """ + ) + else: + t2_index_insp = find_last_value_location_in_series( + frq_insp[(el, em)], frq_attach + frq_width / 2 + ) + + # another way to define t2_index_mr is through number of points in the inspiral window + t1_index_insp = t2_index_insp - (t2_index_mr - t1_index_mr) + + if verbose > 4: + print( + f"""In the merger-ringdown waveform, the hybridization frequency window + [{frq_attach - frq_width / 2}, {frq_attach + frq_width / 2}] + was found at indices [{t1_index_mr}, {t2_index_mr}] + """ + ) + print( + f"""In the inspiral waveform, the same window is to be found at + indices [{t1_index_insp}, {t2_index_insp}.] + """ + ) + """ + Theoretically, we NEED a timeshift to align the waveforms in frequency. + Instead of shifting one of the two waveforms for alignment, we are defining + the time such that the frequencies are pre-aligned to the best of the + discrete interval errors. That is: + deltaT (timeshift) = t1_index_insp - t1_index_mr + The mathematical way is to optimise the difference in frequencies over the matching + region and using that to determine deltaT, hence arriving at t1_index_mr. + """ + + sample_indices_insp = ( + np.linspace(t1_index_insp, t2_index_insp, no_sp).astype(int) - t1_index_insp + ) + sample_indices_mr = ( + sample_indices_insp # since the attachment region in both has the same length + ) + """ alignment using corrective phase addition """ + + inspiral_modes_aligned = {} + amp_insp_aligned = {} + phase_insp_aligned = {} + frq_insp_aligned = {} + + merger_ringdown_modes_aligned = {} + amp_mr_aligned = {} + phase_mr_aligned = {} + frq_mr_aligned = {} + + ( + inspiral_modes_aligned[(el, em)], + merger_ringdown_modes_aligned[(el, em)], + phase_correction, + ) = align_in_phase( + inspiral_modes[(el, em)], + merger_ringdown_modes[(el, em)], + sample_indices_insp, + sample_indices_mr, + t1_index_insp, + t2_index_insp, + t1_index_mr, + t2_index_mr, + m_mode=em, + align_merger_to_inspiral=blend_aligning_merger_to_inspiral, + ) + + amp_insp_aligned[(el, em)] = compute_amplitude(inspiral_modes_aligned[(el, em)]) + phase_insp_aligned[(el, em)] = compute_phase(inspiral_modes_aligned[(el, em)]) + + amp_mr_aligned[(el, em)] = compute_amplitude( + merger_ringdown_modes_aligned[(el, em)] + ) + phase_mr_aligned[(el, em)] = compute_phase(merger_ringdown_modes_aligned[(el, em)]) + + for el, em in modes_not_aligned_by: + if blend_aligning_merger_to_inspiral: + inspiral_modes_aligned[(el, em)] = inspiral_modes[(el, em)] + merger_ringdown_modes_aligned[(el, em)] = merger_ringdown_modes[ + (el, em) + ] * np.exp(1j * em * phase_correction) + else: + inspiral_modes_aligned[(el, em)] = inspiral_modes[(el, em)] * np.exp( + 1j * em * phase_correction + ) + merger_ringdown_modes_aligned[(el, em)] = merger_ringdown_modes[(el, em)] + amp_insp_aligned[(el, em)] = compute_amplitude(inspiral_modes_aligned[(el, em)]) + phase_insp_aligned[(el, em)] = compute_phase(inspiral_modes_aligned[(el, em)]) + amp_mr_aligned[(el, em)] = compute_amplitude( + merger_ringdown_modes_aligned[(el, em)] + ) + phase_mr_aligned[(el, em)] = compute_phase( + merger_ringdown_modes_aligned[(el, em)] + ) + + """ + It would be same as frq_mr as the corrected phase factor will be canceled in the derivative, + defining frq_insp_aligned just for consistency + """ + frq_insp_aligned = frq_insp + frq_mr_aligned = frq_mr + + """ Performing attachment using the blending function """ + + amp_hyb_window = {} + amp_hyb_full = {} + frq_hyb_window = {} + phase_hyb_window = {} + phase_hyb_full = {} + hybrid_modes = {} + + for el, em in modes_to_blend: + amp_hyb_window[(el, em)] = blend_series( + amp_insp_aligned[(el, em)], + amp_mr_aligned[(el, em)], + t1_index_insp, + t2_index_insp, + t1_index_mr, + t2_index_mr, + ) + frq_hyb_window[(el, em)] = blend_series( + frq_insp_aligned[(el, em)], + frq_mr_aligned[(el, em)], + t1_index_insp, + t2_index_insp, + t1_index_mr, + t2_index_mr, + ) + """ Integrating frq_hyb to obtain phase_hyb and removing discontinuities, + compiling amp_hyb and phase_hyb to obtain the hybrid waveform. """ + + phase_hyb_window[(el, em)] = (2 * np.pi) * cumulative_trapezoid( + frq_hyb_window[(el, em)], dx=delta_t, initial=0 + ) + + """ Right now the phase is integrated only inside the hybrid window, + need to add constants to preserve phase continuity and compile full IMR phase """ + + def remove_phase_discontinuity(phase_insp_, phase_hyb_window_, phase_mr_): + if blend_aligning_merger_to_inspiral: + delta1 = phase_insp_[t1_index_insp] - phase_hyb_window_[0] + phase_hyb_1 = np.append( + phase_insp_[:t1_index_insp], phase_hyb_window_ + delta1 + ) + delta2 = phase_hyb_1[t2_index_insp - 1] - phase_mr_[t2_index_mr - 1] + phase_hyb_2 = np.append( + phase_hyb_1[: t2_index_insp - 1], phase_mr_[t2_index_mr - 1 :] + delta2 + ) + else: + delta1 = phase_hyb_window_[0] - phase_insp_[t1_index_insp] + phase_hyb_1 = np.append( + phase_insp_[:t1_index_insp] + delta1, phase_hyb_window_ + ) + delta2 = phase_mr_[t2_index_mr - 1] - phase_hyb_1[t2_index_insp - 1] + phase_hyb_2 = np.append( + phase_hyb_1[: t2_index_insp - 1] + delta2, phase_mr_[t2_index_mr - 1 :] + ) + return phase_hyb_2 + + for el, em in modes_to_blend: + phase_hyb_full[(el, em)] = remove_phase_discontinuity( + phase_insp_aligned[(el, em)], + phase_hyb_window[(el, em)], + phase_mr_aligned[(el, em)], + ) + + amp_hyb_full[(el, em)] = np.append( + np.concatenate( + [amp_insp_aligned[(el, em)][:t1_index_insp], amp_hyb_window[(el, em)]] + )[: t2_index_insp - 1], + amp_mr_aligned[(el, em)][t2_index_mr - 1 :], + ) + + hybrid_modes[(el, em)] = amp_hyb_full[(el, em)] * np.exp( + -1j * phase_hyb_full[(el, em)] + ) + + return ( + hybrid_modes, + t1_index_insp, + t1_index_mr, + t2_index_insp, + t2_index_mr, + frq_insp, + frq_mr, + frq_insp_aligned, + frq_hyb_window, + inspiral_modes_aligned, + merger_ringdown_modes_aligned, + sample_indices_insp, + sample_indices_mr, + amp_insp_aligned, + amp_hyb_window, + amp_mr_aligned, + amp_hyb_full, + phase_insp, + phase_insp_aligned, + phase_hyb_window, + phase_mr_aligned, + phase_hyb_full, + phase_correction, + ) diff --git a/build/lib/esigmapy/generator.py b/build/lib/esigmapy/generator.py new file mode 100644 index 0000000..81e326e --- /dev/null +++ b/build/lib/esigmapy/generator.py @@ -0,0 +1,1210 @@ +# Copyright (C) 2020 Prayush Kumar, Akash Maurya, Kaushik Paul +# +"""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 + +ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 +ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 + + +def eccentricity_at_extremum_frequency( + 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 = ls.SimInspiralENIGMADynamics( + mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, False + ) + t, x, e, l, phi, phidot, r, rdot = retval[:8] + t.data.data *= lal.MTSUN_SI + + if verbose: + print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") + + omega = pt.TimeSeries(phidot.data.data, delta_t=t.data.data[1] - t.data.data[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.data.data - time_at_sensitive_freq).argmin() + e0 = e.data.data[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.data.data, x.data.data**1.5, "--", label="x ** {3/2}") + ax1.plot(t.data.data, phidot.data.data, 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.data.data, e.data.data, 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( + mass1, + mass2, + spin1z, + spin2z, + e0, + l0, + f_lower, + sample_rate, + f_reference, + show_figures=False, + verbose=False, +): + """ """ + itime = time.perf_counter() + retval = ls.SimInspiralENIGMADynamics( + mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, False + ) + t, x, e, l, phi, phidot, r, rdot = retval[:8] + t.data.data *= 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.data.data - x_reference).argmin() + e0 = e.data.data[idx_e0] + + if show_figures: + import matplotlib.pyplot as plt + + fig, (ax) = plt.subplots(1, 1, figsize=(10, 5), sharex=True) + ax.plot(t.data.data, x.data.data**1.5, "--", label="x ** {3/2}") + ax.plot(t.data.data, phidot.data.data, lw=0.5, label="omega") + ax.axhline(x_reference**1.5, color="c", lw=1) + ax.axvline(t.data.data[idx_e0], color="r", lw=1) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Orbital angular velocity") + + ax2 = plt.twinx(ax) + ax2.plot(t.data.data, e.data.data, 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( + 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.data.data[-1] + mean_anomaly = l.data.data[-1] + f_start = f_lower + elif f_ref < f_lower: + itime = time.perf_counter() + f_start = f_ref + + retval = ls.SimInspiralENIGMADynamics( + mass1, + mass2, + spin1z, + spin2z, + eccentricity, + f_start, + mean_anomaly, + 1e-12, + 1 / delta_t, + False, + ) + + if f_ref < f_lower: + ref_idx = np.searchsorted( + (retval[1].data.data ** 1.5) / ((mass1 + mass2) * lal.MTSUN_SI * np.pi), + f_lower, + ) + new_len = len(retval[0].data.data) - ref_idx + for ii in range(8): + lal.ResizeREAL8TimeSeries(retval[ii], ref_idx, new_len) + + t, x, e, l, phi, phidot, r, rdot = retval[:8] + t.data.data *= ( + 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)] = ls.SimInspiralENIGMAModeFromDynamics( + el, + em, + t.data, + x.data, + phi.data, + phidot.data, + r.data, + rdot.data, + mass1, + mass2, + spin1z, + spin2z, + distance, + ) + + if return_pycbc_timeseries: + modes = { + k: pt.TimeSeries( + modes[k].data.data, + delta_t=delta_t, + epoch=-delta_t * (len(modes[k].data.data) - 1), + ) + for k in modes + } + else: + modes = {k: np.asarray(modes[k].data.data) 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}.data.data, delta_t=delta_t, epoch=-delta_t * len({name}.data.data))" + ) + return orbital_var_dict, modes + + for name in return_orbital_params: + exec(f"orbital_var_dict['{name}'] = {name}.data.data") + return (t.data.data - t.data.data[-1]), orbital_var_dict, modes + + if return_pycbc_timeseries: + return modes + return (t.data.data - t.data.data[-1]), modes + + +def get_inspiral_esigma_waveform( + 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( + 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_window_start(freq_, delta_t_, delta_phi_, direction="forward"): + """Integrates frequency backward/forward from the start/end until + `delta_phi_` radians of orbital phase has elapsed. + + Args: + freq_ (numpy.array): Frequency time series, uniformly sampled + delta_t_ (float64): Step size in time + delta_phi_ (float64): Phase shift in radians to integrate across + direction (str, optional): Direction of integration in time. + Defaults to "forward". + + Returns: + int: Index in frequency array where `delta_phi` is reached + """ + from scipy import integrate + + if direction == "backward": + for idx in range(len(freq_) - 2, 0, -1): + # this could be optimized to not repeat integration + if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): + return idx + elif direction == "forward": + for idx in range(1, len(freq_)): + # this could be optimized to not repeat integration + if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( + delta_phi_ + ): + return idx + + +def _get_transition_frequency_window( + orbital_phase, + orbital_freq, + delta_t, + f_mr_transition, # orbital frequency + num_hyb_orbits, + keep_f_mr_transition_at_center, + blend_using_avg_orbital_frequency, + failsafe, + verbose=False, +): + """ + This function first finds where the `orbital_freq` crosses the transition + frequency `f_mr_transition`, and finds the point in time that is + `num_hyb_orbits` orbits before that time. This marks the start of the + hybridization window, and the orbital frequency at that time is returned + + Parameters: + ----------- + orbital_phase -- Orbital phase evolution + orbital_freq -- Orbital frequency evolution + delta_t -- Waveform's time grid-spacing (s) + f_mr_transition -- Inspiral to merger transition frequency + (Hz). This should be the same (mode/orbital) + frequency as passed for `orbital_freq`. + num_hyb_orbits -- number of orbital cycles to blend over. + 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). + blend_using_avg_orbital_frequency -- If True, the orbit averaged + frequency during the inspiral is used to + blend modes, instead of the modes' + frequency. + failsafe -- If True, we make reasonable choices for the + user, if the inputs to this method lead + into exceptions. + verbose -- Verbosity flag + + Returns: + -------- + f_window_mr_transition -- Hybridization frequency window (in Hz). + """ + transition_idx = ( + len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) + ) # index at which orbital_freq becomes just smaller than transition orbital + # frequency, towards the end of waveform + + if verbose > 4: + print( + f"""Transition frequency found at index {transition_idx} + in the orbital frequency array of length {len(orbital_freq)}""" + ) + + if blend_using_avg_orbital_frequency: + # We integrate `orbital_freq` to obtain `orbital_phase` + from scipy import integrate + + orbital_phase = integrate.cumulative_trapezoid( + orbital_freq, dx=delta_t, initial=0 + ) + if len(orbital_phase) != len(orbital_freq): + raise RuntimeError( + f"""Something went wrong while integrating orbital frequency. +They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" + ) + + if keep_f_mr_transition_at_center: + # Orbital cycle based hybridization that keeps f_mr_transition at + # the hybridization frequency window's midpoint + if blend_using_avg_orbital_frequency: + window_start_idx = _get_window_start( + orbital_freq[: transition_idx + 1], + delta_t, + num_hyb_orbits * np.pi, + direction="backward", + ) + window_end_idx = transition_idx + _get_window_start( + orbital_freq[transition_idx:], + delta_t, + num_hyb_orbits * np.pi, + direction="forward", + ) + # FAILSAFE mode behavior + if window_start_idx is None: + if failsafe: + window_start_idx = 0 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform before the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or increase the +inspiral-to-merger transition frequency. `window_start_idx` is None.""" + ) + if window_end_idx is None: + if failsafe: + window_end_idx = len(orbital_freq) - 1 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform after the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or decrease the +inspiral-to-merger transition frequency. `window_end_idx` is None.""" + ) + else: + # phase is always a monotonically increasing function, + # hence using the more efficient np.searchsorted method + window_start_idx = -1 + np.searchsorted( + orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi + ) + window_end_idx = np.searchsorted( + orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi + ) + f_window_mr_transition = 2 * np.min( + [ + orbital_freq[window_end_idx] - f_mr_transition, + f_mr_transition - orbital_freq[window_start_idx], + ] + ) + elif not keep_f_mr_transition_at_center: + # Orbital cycle based hybridization that keeps f_mr_transition at + # the hybridization frequency window's right end + if blend_using_avg_orbital_frequency: + window_start_idx = _get_window_start( + orbital_freq[: transition_idx + 1], + delta_t, + 2 * num_hyb_orbits * np.pi, + direction="backward", + ) + if window_start_idx is None: + if failsafe: + window_start_idx = 0 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform before the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or increase the +inspiral-to-merger transition frequency. `window_start_idx` is None.""" + ) + else: + # Orbital cycle based hybridization that keeps f_mr_transition + # at the hybridization frequency window's end + window_start_idx = ( + np.searchsorted( + orbital_phase, + orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, + ) + - 1 + ) + f_window_mr_transition = ( + orbital_freq[transition_idx] - orbital_freq[window_start_idx] + ) + + if verbose > 4: + print( + f"""The transition is to happen in a window from + frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, + between indices: [{window_start_idx}, {transition_idx}]""" + ) + else: + raise RuntimeError( + """We should never reach here. Did you set a non-bool value for the + flag `keep_f_mr_transition_at_center`?""" + ) + + if verbose > 4: + print(f"""f_window_mr_transition = {f_window_mr_transition}""") + + return f_window_mr_transition + + +def get_imr_esigma_modes( + 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( + 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. Its 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( + 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( + 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/build/lib/esigmapy/legacy.py b/build/lib/esigmapy/legacy.py new file mode 100644 index 0000000..c69e167 --- /dev/null +++ b/build/lib/esigmapy/legacy.py @@ -0,0 +1,125 @@ +# Copyright (C) 2023 Prayush Kumar +# +"""Legacy code to calibrate transition/attachment of inspiral and merger-ringdown +""" + + +class FitMOmegaIMRAttachmentNonSpinning: + called_once = False + + def __init__(self): + self.called_once = False + return + + @classmethod + def fit_quadratic_poly(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_quadratic_poly") + cls.called_once = True + assert len(coeffs) == 2, "{} coeffs passed!".format(len(coeffs)) + a1, a2 = coeffs + return (1.0 / 6**1.5) * (1.0 + a1 * eta + a2 * eta**2) + + @classmethod + def fit_cubic_poly(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_cubic_poly") + cls.called_once = True + assert len(coeffs) == 3, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3 = coeffs + return (1.0 / 6**1.5) * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) + + @classmethod + def fit_ratio_poly_44(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_poly_44") + cls.called_once = True + assert len(coeffs) == 6, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3, b1, b2, b3 = coeffs + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) + / (1.0 + b1 * eta + b2 * eta**2 + b3 * eta**3) + ) + + @classmethod + def fit_ratio_sqrt_poly_44(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_sqrt_poly_44") + cls.called_once = True + assert len(coeffs) == 6, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3, b1, b2, b3 = coeffs + s_eta = eta**0.5 + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * s_eta + a2 * s_eta**2 + a3 * s_eta**3) + / (1.0 + b1 * s_eta + b2 * s_eta**2 + b3 * s_eta**3) + ) + + @classmethod + def fit_ratio_sqrt_hyb1_poly_44(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_sqrt_hyb1_poly_44") + cls.called_once = True + assert len(coeffs) == 6, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3, b1, b2, b3 = coeffs + s_eta = eta**0.5 + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) + / (1.0 + b1 * eta + b2 * eta**2 + b3 * eta**3) + ) + + @classmethod + def fit_ratio_poly_43(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_poly_43") + cls.called_once = True + assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3, b1, b2 = coeffs + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) + / (1.0 + b1 * eta + b2 * eta**2) + ) + + @classmethod + def fit_ratio_sqrt_poly_43(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_sqrt_poly_43") + cls.called_once = True + assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3, b1, b2 = coeffs + s_eta = eta**0.5 + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * s_eta + a2 * s_eta**2 + a3 * s_eta**3) + / (1.0 + b1 * s_eta + b2 * s_eta**2) + ) + + @classmethod + def fit_ratio_sqrt_hyb1_poly_43(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_sqrt_hyb1_poly_43") + cls.called_once = True + assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) + a1, a2, a3, b1, b2 = coeffs + s_eta = eta**0.5 + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * eta * s_eta + a2 * eta**2 * s_eta + a3 * eta**3 * s_eta) + / (1.0 + b1 * eta + b2 * eta**2) + ) + + @classmethod + def fit_ratio_poly_34(cls, eta, coeffs): + if not cls.called_once: + logging.info("Using fit_ratio_poly_34") + cls.called_once = True + assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) + a1, a2, b1, b2, b3 = coeffs + return ( + (1.0 / 6**1.5) + * (1.0 + a1 * eta + a2 * eta**2) + / (1.0 + b1 * eta + b2 * eta**2 + b3 * eta**3) + ) diff --git a/build/lib/esigmapy/python_codes/__init__.py b/build/lib/esigmapy/python_codes/__init__.py new file mode 100644 index 0000000..203562b --- /dev/null +++ b/build/lib/esigmapy/python_codes/__init__.py @@ -0,0 +1 @@ +# __init__.py \ No newline at end of file diff --git a/build/lib/esigmapy/python_codes/esigma_go_terms.py b/build/lib/esigmapy/python_codes/esigma_go_terms.py new file mode 100644 index 0000000..e0057f2 --- /dev/null +++ b/build/lib/esigmapy/python_codes/esigma_go_terms.py @@ -0,0 +1,3343 @@ +""" +esigma_go_terms.py +==================== +Python translation of ESIGMA_GOTerms.c +""" + +import numpy as np +from dataclasses import dataclass + +# Mapping M_PI2 to pi^2 (standard in PN GW theory for this coefficient) +M_PI = np.pi +M_PI2 = M_PI ** 2 + +def Complex(real: float, imag: float) -> complex: + """Helper function to mimic the C-style Complex constructor.""" + return complex(real, imag) +def rect(r: float, phi: float) -> complex: + """Convert polar coordinates to rectangular form.""" + return r * np.exp(1j * phi) + +@dataclass +class KeplerVars: + # Basic orbital elements + pn_order: int + eta: float + Mtot: float + x: float + e: float + l: float + r: float + rDOT: float + PhiDOT: float + S1z: float + S2z: float + + # Powers of x + x2: float = 0.0; x2p5: float = 0.0; x3: float = 0.0; x3p5: float = 0.0 + x4: float = 0.0; x4p5: float = 0.0; x5: float = 0.0; x6: float = 0.0 + x7: float = 0.0; x8: float = 0.0; x9: float = 0.0 + + # Powers of e + e2: float = 0.0; e3: float = 0.0; e4: float = 0.0; e5: float = 0.0 + e6: float = 0.0; e7: float = 0.0; e8: float = 0.0; e9: float = 0.0 + + # Powers of r + r2: float = 0.0; r3: float = 0.0; r4: float = 0.0; r5: float = 0.0 + r6: float = 0.0; r7: float = 0.0; r8: float = 0.0; r9: float = 0.0 + + # Powers of rDOT + rDOT2: float = 0.0; rDOT3: float = 0.0; rDOT4: float = 0.0; rDOT5: float = 0.0 + rDOT6: float = 0.0; rDOT7: float = 0.0; rDOT8: float = 0.0; rDOT9: float = 0.0 + + # Powers of PhiDOT + PhiDOT2: float = 0.0; PhiDOT3: float = 0.0; PhiDOT4: float = 0.0; PhiDOT5: float = 0.0 + PhiDOT6: float = 0.0; PhiDOT7: float = 0.0; PhiDOT8: float = 0.0; PhiDOT9: float = 0.0 + + # Powers of S1z + S1z2: float = 0.0; S1z3: float = 0.0; S1z4: float = 0.0; S1z5: float = 0.0 + S1z6: float = 0.0; S1z7: float = 0.0; S1z8: float = 0.0; S1z9: float = 0.0 + S1z10: float = 0.0; S1z11: float = 0.0; S1z12: float = 0.0; S1z13: float = 0.0 + S1z14: float = 0.0; S1z15: float = 0.0; S1z16: float = 0.0 + + # Powers of S2z + S2z2: float = 0.0; S2z3: float = 0.0; S2z4: float = 0.0 + S2z5: float = 0.0; S2z6: float = 0.0 + + # Powers of eta + eta2: float = 0.0; eta3: float = 0.0; eta4: float = 0.0 + eta5: float = 0.0; eta6: float = 0.0 + + # Powers of Mtot + Mtot2: float = 0.0; Mtot3: float = 0.0; Mtot4: float = 0.0 + Mtot5: float = 0.0; Mtot6: float = 0.0 + + def compute_powers(self): + """Compute all power fields from the base values.""" + self.x2 = self.x**2; self.x2p5 = self.x**2.5; self.x3 = self.x**3 + self.x3p5 = self.x**3.5; self.x4 = self.x**4; self.x4p5 = self.x**4.5 + self.x5 = self.x**5; self.x6 = self.x**6; self.x7 = self.x**7 + self.x8 = self.x**8; self.x9 = self.x**9 + + self.e2 = self.e**2; self.e3 = self.e**3; self.e4 = self.e**4; self.e5 = self.e**5 + self.e6 = self.e**6; self.e7 = self.e**7; self.e8 = self.e**8; self.e9 = self.e**9 + + self.r2 = self.r**2; self.r3 = self.r**3; self.r4 = self.r**4; self.r5 = self.r**5 + self.r6 = self.r**6; self.r7 = self.r**7; self.r8 = self.r**8; self.r9 = self.r**9 + + self.rDOT2 = self.rDOT**2; self.rDOT3 = self.rDOT**3; self.rDOT4 = self.rDOT**4 + self.rDOT5 = self.rDOT**5; self.rDOT6 = self.rDOT**6; self.rDOT7 = self.rDOT**7 + self.rDOT8 = self.rDOT**8; self.rDOT9 = self.rDOT**9 + + self.PhiDOT2 = self.PhiDOT**2; self.PhiDOT3 = self.PhiDOT**3; self.PhiDOT4 = self.PhiDOT**4 + self.PhiDOT5 = self.PhiDOT**5; self.PhiDOT6 = self.PhiDOT**6; self.PhiDOT7 = self.PhiDOT**7 + self.PhiDOT8 = self.PhiDOT**8; self.PhiDOT9 = self.PhiDOT**9 + + self.S1z2 = self.S1z**2; self.S1z3 = self.S1z**3; self.S1z4 = self.S1z**4 + self.S1z5 = self.S1z**5; self.S1z6 = self.S1z**6; self.S1z7 = self.S1z**7 + self.S1z8 = self.S1z**8; self.S1z9 = self.S1z**9; self.S1z10 = self.S1z**10 + self.S1z11 = self.S1z**11; self.S1z12 = self.S1z**12; self.S1z13 = self.S1z**13 + self.S1z14 = self.S1z**14; self.S1z15 = self.S1z**15; self.S1z16 = self.S1z**16 + + self.S2z2 = self.S2z**2; self.S2z3 = self.S2z**3; self.S2z4 = self.S2z**4 + self.S2z5 = self.S2z**5; self.S2z6 = self.S2z**6 + + self.eta2 = self.eta**2; self.eta3 = self.eta**3; self.eta4 = self.eta**4 + self.eta5 = self.eta**5; self.eta6 = self.eta**6 + + self.Mtot2 = self.Mtot**2; self.Mtot3 = self.Mtot**3; self.Mtot4 = self.Mtot**4 + self.Mtot5 = self.Mtot**5; self.Mtot6 = self.Mtot**6 + +# All the hGO_l_m functions contain the 3PN non-spinning general orbit (GO) terms +# from Mishra et al. arXiv:1501.07096 and 3.5PN quasi-circular spinning +# corrections as given in Henry et al, arXiv:2209.00374v2 + +# The (2,2) mode has newly computed 4PN non-spinning quasi-circular piece from +# arXiv:2304.11185 + +# H22 + +def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, + PhiDOT: float, vpnorder: int, S1z: float, S2z: float, + x: float, params: KeplerVars) -> complex: + + # Note: In Python, `params` should be an object (like a dataclass or SimpleNamespace) + # so that attributes can be accessed via dot notation (e.g., params.PhiDOT2). + + # 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 * Nu) + + 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 + + + + if vpnorder == 0: + return (mass / r + params.PhiDOT2 * params.r2 + + Complex(0, 2) * PhiDOT * r * rDOT - params.rDOT2) + + elif vpnorder == 2: + return ((21 * params.Mtot2 * (-10 + Nu) - + 27 * (-1 + 3 * Nu) * params.r2 * combination_b * combination_a3 + + mass * r * + ((11 + 156 * Nu) * params.PhiDOT2 * params.r2 + + Complex(0, 10) * (5 + 27 * Nu) * PhiDOT * r * rDOT - + 3 * (15 + 32 * Nu) * params.rDOT2)) / + (42. * params.r2)) + + elif vpnorder == 3: + return ((params.Mtot2 * (Complex(0, -1) * rDOT * + ((3 + 3 * delta - 8 * Nu) * S1z + + (3 - 3 * delta - 8 * Nu) * S2z) + + PhiDOT * r * + ((-3 - 3 * delta + 5 * Nu) * S1z + + (-3 + 3 * delta + 5 * Nu) * S2z))) / + (3. * params.r2)) + # (<--This is the general orbit term) + # (This is the quasi-circular limit of the general orbit term-->) + # - ((-4 * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x2p5) / 3.) + + # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - Nu * (S1z + S2z)) * params.x2p5) / 3.) + # (<--This is Quentins quasi-circular term) + + elif vpnorder == 4: + return ((6 * params.Mtot3 * (3028 + 1267 * Nu + 158 * params.eta2) + + 9 * (83 - 589 * Nu + 1111 * params.eta2) * params.r3 * + combination_b2 * combination_a4 + + params.Mtot2 * r * + ((-11891 - 36575 * Nu + 13133 * params.eta2) * params.PhiDOT2 * + params.r2 + + Complex(0, 8) * (-773 - 3767 * Nu + 2852 * params.eta2) * + PhiDOT * r * rDOT - + 6 * (-619 + 2789 * Nu + 934 * params.eta2) * params.rDOT2) - + 3 * mass * params.r2 * + (2 * (-835 - 19 * Nu + 2995 * params.eta2) * params.PhiDOT4 * + params.r4 + + Complex(0, 6) * (-433 - 721 * Nu + 1703 * params.eta2) * + params.PhiDOT3 * params.r3 * rDOT + + 6 * (-33 + 1014 * Nu + 232 * params.eta2) * params.PhiDOT2 * + params.r2 * params.rDOT2 + + Complex(0, 4) * (-863 + 1462 * Nu + 2954 * params.eta2) * + PhiDOT * r * params.rDOT3 - + 3 * (-557 + 664 * Nu + 1712 * params.eta2) * params.rDOT4)) / + (1512. * params.r3) + + (3 * params.Mtot3 * + (S1z * (4 * Nu * S2z + (1 + delta - 2 * Nu) * S1z * kappa1) - + (-1 + delta + 2 * Nu) * params.S2z2 * kappa2)) / + (4. * params.r3)) + # This is where circular limit terms added and subtracted + # - ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + + # ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + + elif vpnorder == 5: + return ((params.Mtot2 * Nu * + (2 * mass * (Complex(0, -702) * PhiDOT * r + rDOT) + + 3 * r * + (Complex(0, -316) * params.PhiDOT3 * params.r3 - + 847 * params.PhiDOT2 * params.r2 * rDOT + + Complex(0, 184) * PhiDOT * r * params.rDOT2 - + 122 * params.rDOT3))) / + (105. * params.r3) + # Henry et al. QC spin terms + # + ((2*(56*delta*Nu*(-S1z + S2z) + 101*Nu*(S1z + S2z) + 132*params.eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) + + + # Henry et al. ecc spin terms + ((params.Mtot2 * + (mass * + ((238 + delta * (238 - 141 * Nu) + Nu * (-181 + 474 * Nu)) * + PhiDOT * r * S1z + + Complex(0, 8) * + (55 + delta * (55 - 19 * Nu) + + 2 * Nu * (-50 + 43 * Nu)) * + rDOT * S1z + + (238 + delta * (-238 + 141 * Nu) + Nu * (-181 + 474 * Nu)) * + PhiDOT * r * S2z + + Complex(0, 8) * + (55 + delta * (-55 + 19 * Nu) + + 2 * Nu * (-50 + 43 * Nu)) * + rDOT * S2z) - + r * (PhiDOT * r * params.rDOT2 * + (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * Nu + + 188 * params.eta2) * + S1z) + + (18 * (-1 + delta) + 5 * (63 + 55 * delta) * Nu - + 188 * params.eta2) * + S2z) - + Complex(0, 2) * params.rDOT3 * + ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * Nu - + 4 * params.eta2) * + S1z + + (-27 + 27 * delta + 30 * Nu - 42 * delta * Nu - + 4 * params.eta2) * + S2z) + + Complex(0, 2) * params.PhiDOT2 * params.r2 * rDOT * + ((51 + 88 * Nu * (-3 + 5 * Nu) + + delta * (51 + 62 * Nu)) * + S1z + + (51 + 88 * Nu * (-3 + 5 * Nu) - + delta * (51 + 62 * Nu)) * + S2z) + + params.PhiDOT3 * params.r3 * + ((120 * (1 + delta) + (-483 + 83 * delta) * Nu + + 234 * params.eta2) * + S1z + + (120 + 3 * Nu * (-161 + 78 * Nu) - + delta * (120 + 83 * Nu)) * + S2z)))) / + (84. * params.r3))) + + elif vpnorder == 6: + return ((4 * params.Mtot4 * + (-8203424 + 2180250 * params.eta2 + 592600 * params.eta3 + + 15 * Nu * (-5503804 + 142065 * M_PI2)) - + 2700 * (-507 + 6101 * Nu - 25050 * params.eta2 + 34525 * params.eta3) * + params.r4 * combination_b3 * combination_a5 + + params.Mtot3 * r * + (params.PhiDOT2 * + (337510808 - 198882000 * params.eta2 + + 56294600 * params.eta3 + + Nu * (183074880 - 6392925 * M_PI2)) * + params.r2 + + Complex(0, 110) * PhiDOT * + (-5498800 - 785120 * params.eta2 + 909200 * params.eta3 + + 3 * Nu * (-1849216 + 38745 * M_PI2)) * + r * rDOT + + 2 * + (51172744 - 94929000 * params.eta2 - 5092400 * params.eta3 + + 45 * Nu * (2794864 + 142065 * M_PI2)) * + params.rDOT2) - + 20 * params.Mtot2 * params.r2 * + ((-986439 + 1873255 * Nu - 9961400 * params.eta2 + + 6704345 * params.eta3) * + params.PhiDOT4 * params.r4 + + Complex(0, 4) * + (-273687 - 978610 * Nu - 4599055 * params.eta2 + + 2783005 * params.eta3) * + params.PhiDOT3 * params.r3 * rDOT + + (-181719 + 19395325 * Nu + 8237980 * params.eta2 + + 2612735 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT2 + + Complex(0, 8) * + (-234312 + 1541140 * Nu + 1230325 * params.eta2 + + 1828625 * params.eta3) * + PhiDOT * r * params.rDOT3 - + 3 * + (-370268 + 1085140 * Nu + 2004715 * params.eta2 + + 1810425 * params.eta3) * + params.rDOT4) + + 300 * mass * params.r3 * + (4 * + (12203 - 36427 * Nu - 27334 * params.eta2 + + 149187 * params.eta3) * + params.PhiDOT6 * params.r6 + + Complex(0, 2) * + (44093 - 68279 * Nu - 295346 * params.eta2 + + 541693 * params.eta3) * + params.PhiDOT5 * params.r5 * rDOT + + 2 * + (27432 - 202474 * Nu + 247505 * params.eta2 + + 394771 * params.eta3) * + params.PhiDOT4 * params.r4 * params.rDOT2 + + Complex(0, 2) * + (97069 - 383990 * Nu - 8741 * params.eta2 + + 1264800 * params.eta3) * + params.PhiDOT3 * params.r3 * params.rDOT3 + + (-42811 + 53992 * Nu + 309136 * params.eta2 - + 470840 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT4 + + Complex(0, 2) * + (51699 - 252256 * Nu + 131150 * params.eta2 + + 681160 * params.eta3) * + PhiDOT * r * params.rDOT5 - + 3 * + (16743 - 75104 * Nu + 26920 * params.eta2 + + 207200 * params.eta3) * + params.rDOT6)) / + (3.3264e6 * params.r4) + # Henry et al. QC spin terms + # + (((4*(1 + delta)*(-7 + 9*kappa1) - 7*(9 + 17*delta)*Nu - 9*(15 + 7*delta)*kappa1*Nu + 12*(7 - 17*kappa1)*params.eta2)*params.S1z2 + 2*S1z*(Complex(0,-42)*(1 + delta - 2*Nu) - 84*(1 + delta - Nu)*M_PI + Nu*(-271 + 288*Nu)*S2z) + S2z*(12*(7 - 17*kappa2)*params.eta2*S2z + 4*(-1 + delta)*(Complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + Nu*(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 * + (params.Mtot3 * + (2 * mass * + (S1z * (Complex(0, 14) * (1 + delta - 2 * Nu) + + 42 * (1 + delta - 2 * Nu) * Nu * S1z + + kappa1 * + (438 + delta * (438 + 103 * Nu) + + Nu * (-773 + 108 * Nu)) * + S1z) + + 2 * + (Complex(0, -7) * (-1 + delta + 2 * Nu) + + (995 - 192 * Nu) * Nu * S1z) * + S2z - + (42 * Nu * (-1 + delta + 2 * Nu) + + kappa2 * (-438 + (773 - 108 * Nu) * Nu + + delta * (438 + 103 * Nu))) * + params.S2z2) + + r * (params.rDOT2 * + (Complex(0, 56) * (1 + delta - 2 * Nu) * S1z + + (-56 * (1 + delta - 2 * Nu) * Nu + + kappa1 * (291 * (1 + delta) - + 2 * (445 + 154 * delta) * Nu + + 24 * params.eta2)) * + params.S1z2 + + 4 * Nu * (-3 + 44 * Nu) * S1z * S2z + + S2z * (Complex(0, -56) * (-1 + delta + 2 * Nu) + + 56 * Nu * (-1 + delta + 2 * Nu) * S2z + + kappa2 * + (291 - 890 * Nu + 24 * params.eta2 + + delta * (-291 + 308 * Nu)) * + S2z)) + + params.PhiDOT2 * params.r2 * + (Complex(0, 196) * (1 + delta - 2 * Nu) * S1z + + (56 * Nu * (7 + 7 * delta + Nu) + + kappa1 * (-153 * (1 + delta) - + 2 * (62 + 215 * delta) * Nu + + 804 * params.eta2)) * + params.S1z2 + + 8 * (60 - 187 * Nu) * Nu * S1z * S2z + + S2z * (Complex(0, -196) * (-1 + delta + 2 * Nu) + + 56 * Nu * (7 - 7 * delta + Nu) * S2z + + kappa2 * + (-153 + 4 * Nu * (-31 + 201 * Nu) + + delta * (153 + 430 * Nu)) * + S2z)) + + 2 * PhiDOT * r * rDOT * + (Complex(0, -1) * + (117 * (1 + delta) * kappa1 - + 434 * (1 + delta) * Nu + + (-23 + 211 * delta) * kappa1 * Nu + + 2 * (14 - 195 * kappa1) * params.eta2) * + params.S1z2 + + 4 * S1z * + (35 * (1 + delta - 2 * Nu) + + Complex(0, 1) * (9 - 209 * Nu) * Nu * S2z) + + S2z * (-140 * (-1 + delta + 2 * Nu) + + Complex(0, 1) * + (-14 * Nu * (-31 + 31 * delta + 2 * Nu) + + kappa2 * (117 * (-1 + delta) + + (23 + 211 * delta) * Nu + + 390 * params.eta2)) * + S2z))))) / + params.r4)) + # + Henry et al. QC spinning hereditary terms + # (((-8 * M_PI * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x4) / 3.)) + + elif vpnorder == 7: + return ( + # Henry et al QC spin terms + # ((3318*params.eta3*(S1z + S2z) + Nu*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*params.S1z3 - 1008*params.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*params.S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*params.S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*params.eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*params.S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. + # + + # Henry et al. ecc+spin terms + ((params.Mtot2 * + (-3 * mass * r * + (Complex(0, -16) * params.rDOT3 * + (Complex(0, 12) * Nu * (-16703 + 4427 * Nu) + + 35 * Nu * + (4578 + Nu * (-4288 + 765 * Nu) + + delta * (3748 + 802 * Nu)) * + S1z + + 35 * + (delta * (942 - 2 * Nu * (1874 + 401 * Nu)) + + Nu * (4578 + Nu * (-4288 + 765 * Nu))) * + S2z - + 32970 * (S1z + delta * S1z + S2z)) + + 4 * PhiDOT * r * params.rDOT2 * + (-338520 * params.eta3 * (S1z + S2z) + + 48930 * (S1z + delta * S1z + S2z - delta * S2z) + + Nu * (Complex(0, 3420696) - + 35 * (14154 + 21167 * delta) * S1z - + 495390 * S2z + 740845 * delta * S2z) + + params.eta2 * (Complex(0, -612336) + + 245 * (3566 - 1565 * delta) * S1z + + 245 * (3566 + 1565 * delta) * S2z)) + + params.PhiDOT3 * params.r3 * + (2515380 * params.eta3 * (S1z + S2z) - + 5 * Nu * + (Complex(0, 1859936) + + 7 * (82329 + 37061 * delta) * S1z + + 7 * (82329 - 37061 * delta) * S2z) - + 128100 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * params.eta2 * + (Complex(0, 381348) + + 35 * (-18505 + 1777 * delta) * S1z - + 35 * (18505 + 1777 * delta) * S2z)) + + Complex(0, 8) * params.PhiDOT2 * params.r2 * rDOT * + (779100 * params.eta3 * (S1z + S2z) + + 5 * Nu * + (Complex(0, 828806) + + 7 * (4839 + 5971 * delta) * S1z + + 7 * (4839 - 5971 * delta) * S2z) - + 62475 * (S1z + delta * S1z + S2z - delta * S2z) + + params.eta2 * (Complex(0, -976002) + + 35 * (-29599 + 3109 * delta) * S1z - + 35 * (29599 + 3109 * delta) * S2z))) + + 3 * params.r2 * + (Complex(0, 4) * params.PhiDOT2 * params.r2 * params.rDOT3 * + (Complex(0, 2) * (65451 - 350563 * Nu) * Nu + + 105 * Nu * + (6020 + delta * (3114 + 411 * Nu) + + Nu * (-4513 + 9136 * Nu)) * + S1z + + 105 * + (-3 * delta * (-408 + Nu * (1038 + 137 * Nu)) + + Nu * (6020 + Nu * (-4513 + 9136 * Nu))) * + S2z - + 128520 * (S1z + delta * S1z + S2z)) + + PhiDOT * r * params.rDOT4 * + (Complex(0, -128) * Nu * (2487 + 18334 * Nu) - + 105 * Nu * + (8689 + 8 * Nu * (-687 + 1402 * Nu) + + delta * (2143 + 8212 * Nu)) * + S1z + + 105 * + (Nu * (-8689 + 8 * (687 - 1402 * Nu) * Nu) + + delta * (-1470 + Nu * (2143 + 8212 * Nu))) * + S2z + + 154350 * (S1z + delta * S1z + S2z)) - + Complex(0, 6) * params.rDOT5 * + (-11760 * params.eta3 * (S1z + S2z) + + 4 * params.eta2 * + (Complex(0, 57338) + + 35 * (569 + 301 * delta) * S1z + + 35 * (569 - 301 * delta) * S2z) - + 16 * Nu * + (Complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + + 35 * (72 - 71 * delta) * S2z) + + 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + + params.PhiDOT5 * params.r5 * + (2263380 * params.eta3 * (S1z + S2z) + + 3 * Nu * + (Complex(0, 653432) + + 35 * (13283 + 7839 * delta) * S1z + + 35 * (13283 - 7839 * delta) * S2z) - + 219240 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * params.eta2 * + (Complex(0, -291268) + + 105 * (-7669 + 165 * delta) * S1z - + 105 * (7669 + 165 * delta) * S2z)) + + Complex(0, 14) * params.PhiDOT4 * params.r4 * rDOT * + (385170 * params.eta3 * (S1z + S2z) + + 15 * Nu * + (Complex(0, 16914) + (8633 + 2267 * delta) * S1z + + 8633 * S2z - 2267 * delta * S2z) - + 18630 * (S1z + delta * S1z + S2z - delta * S2z) + + 2 * params.eta2 * + (Complex(0, -67904) + + 15 * (-13932 + 679 * delta) * S1z - + 15 * (13932 + 679 * delta) * S2z)) + + 6 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (-6720 * params.eta3 * (S1z + S2z) + + 5 * Nu * + (Complex(0, -241704) + + 7 * (4377 + 3083 * delta) * S1z + + 7 * (4377 - 3083 * delta) * S2z) - + 36960 * (S1z + delta * S1z + S2z - delta * S2z) + + params.eta2 * (Complex(0, -364384) + + 35 * (5059 - 4753 * delta) * S1z + + 35 * (5059 + 4753 * delta) * S2z))) + + 14 * params.Mtot2 * + (Complex(0, 30) * rDOT * + (15280 * params.eta3 * (S1z + S2z) - + 4 * params.eta2 * + (Complex(0, -111) + 3562 * S1z + 682 * delta * S1z + + 945 * kappa1 * params.S1z3 + + (3562 - 682 * delta + + 945 * (-2 + kappa1) * params.S1z2) * + S2z + + 945 * (-2 + kappa2) * S1z * params.S2z2 + + 945 * kappa2 * params.S2z3) + + Nu * (Complex(0, -27520) + + 378 * + (5 * (1 + delta) * kappa1 + + 3 * (3 + delta) * lambda1) * + params.S1z3 + + (29749 - 13605 * delta) * S2z - + 1512 * (1 + delta) * kappa1 * params.S1z2 * S2z - + 378 * + (5 * (-1 + delta) * kappa2 + + 3 * (-3 + delta) * lambda2) * + params.S2z3 + + S1z * (29749 + 13605 * delta + + 1512 * (-1 + delta) * kappa2 * + params.S2z2)) + + 2 * (-8009 * (1 + delta) * S1z - + 567 * (1 + delta) * lambda1 * params.S1z3 + + (-1 + delta) * S2z * + (8009 + 567 * lambda2 * params.S2z2))) + + PhiDOT * r * + (285840 * params.eta3 * (S1z + S2z) - + 30 * params.eta2 * + (Complex(0, 11504) + 1890 * kappa1 * params.S1z3 + + 23090 * S2z - 1823 * delta * S2z + + 1890 * (-2 + kappa1) * params.S1z2 * S2z + + 1890 * kappa2 * params.S2z3 + + S1z * (23090 + 1823 * delta + + 1890 * (-2 + kappa2) * params.S2z2)) + + 30 * (689 * (1 + delta) * S1z - + 1134 * (1 + delta) * lambda1 * params.S1z3 + + (-1 + delta) * S2z * + (-689 + 1134 * lambda2 * params.S2z2)) + + 2 * Nu * + (Complex(0, 415432) - 66840 * S2z + + 15 * (8 * (-557 + 1342 * delta) * S1z + + 189 * + (5 * (1 + delta) * kappa1 + + 6 * (3 + delta) * lambda1) * + params.S1z3 - + 10736 * delta * S2z - + 2457 * (1 + delta) * kappa1 * params.S1z2 * + S2z + + 2457 * (-1 + delta) * kappa2 * S1z * + params.S2z2 - + 189 * + (5 * (-1 + delta) * kappa2 + + 6 * (-3 + delta) * lambda2) * + params.S2z3)))))) / + (317520. * params.r4)) + # + Henry et al. QC spinning hereditary terms + # (2 * M_PI * (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x4p5) + ) + + else: + return complex(0, 0) + +# hQC_l_m() functions contain only the non-spinning hereditary terms at +# particular PN order. + +def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, + params: KeplerVars) -> complex: + + EulerGamma = 0.5772156649015329 + x0 = 0.4375079683656479 + b0 = 2 * 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 * Nu) + + # keeping only the hereditary terms + # if vpnorder == 0: + # return (2 * x) + + # elif vpnorder == 2: + # return ((-5.095238095238095 + (55 * Nu) / 21.) * params.x2) + + if vpnorder == 3: + # return (4 * M_PI * params.x2p5) + return (complex(0, 0.6666666666666666) * params.x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) + + # elif vpnorder == 4: + # return ((-2.874338624338624 - (1069 * Nu) / 108. + (2047 * params.eta2) / 756.) * params.x3) + + elif vpnorder == 5: + # return ((complex(0,-48)*Nu - (214*M_PI)/21. + (68*Nu*M_PI)/21.)*params.x3p5) + # return ((2 * (-107 + 34 * Nu) * M_PI * params.x3p5) / 21.) # This is old implementation + return (complex(0, 0.015873015873015872) * (-107 + 34 * Nu) * params.x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) + + elif vpnorder == 6: + # return ((params.x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * Nu) * M_PI) - 13696 * np.log(16 * x))) / 1680.) # This is the old implementation. + + return (params.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) * Nu * S1z + complex(0, 5.333333333333333) * EulerGamma * Nu * S1z - + (8 * M_PI * S1z) / 3. + (8 * Nu * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - + complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * Nu * S2z + + complex(0, 5.333333333333333) * EulerGamma * Nu * S2z - (8 * M_PI * S2z) / 3. + (8 * Nu * M_PI * S2z) / 3. + + (912 * np.log(2)) / 35. - 64 * EulerGamma * np.log(2) + complex(0, 32) * M_PI * np.log(2) - + complex(0, 10.666666666666666) * S1z * np.log(2) + complex(0, 10.666666666666666) * Nu * S1z * np.log(2) - + complex(0, 10.666666666666666) * S2z * np.log(2) + complex(0, 10.666666666666666) * Nu * S2z * np.log(2) - + 64 * np.log(2) * np.log(2) + (88 * np.log(b0)) / 3. - 32 * EulerGamma * np.log(b0) + + complex(0, 16) * M_PI * np.log(b0) - complex(0, 5.333333333333333) * S1z * np.log(b0) + + complex(0, 5.333333333333333) * Nu * S1z * np.log(b0) - complex(0, 5.333333333333333) * S2z * np.log(b0) + + complex(0, 5.333333333333333) * Nu * S2z * np.log(b0) - 64 * np.log(2) * np.log(b0) - + 16 * np.log(b0) * np.log(b0) - (1712 * np.log(r0)) / 105. + (684 * np.log(x)) / 35. - + 48 * EulerGamma * np.log(x) + complex(0, 24) * M_PI * np.log(x) - complex(0, 8) * S1z * np.log(x) + + complex(0, 8) * Nu * S1z * np.log(x) - complex(0, 8) * S2z * np.log(x) + complex(0, 8) * Nu * S2z * np.log(x) - + 96 * np.log(2) * np.log(x) - 48 * np.log(b0) * np.log(x) - 36 * np.log(x) * np.log(x) - + complex(0, 0.4444444444444444) * delta * (S1z - S2z) * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x)))) + + elif vpnorder == 7: + # return (((-2173 - 4990 * Nu + 1120 * params.eta2) * M_PI * params.x4p5) / 378.) # This is the old implementation. + return (complex(0, 0.0004409171075837742) * params.x4p5 * (23903 - 26076 * EulerGamma + 57452 * Nu - + 59880 * EulerGamma * Nu - 18728 * params.eta2 + 13440 * EulerGamma * params.eta2 + + complex(0, 13038) * M_PI + complex(0, 29940) * Nu * M_PI - complex(0, 6720) * params.eta2 * M_PI - + 8316 * kappa1 * params.S1z2 - 8316 * delta * kappa1 * params.S1z2 + 9072 * EulerGamma * kappa1 * params.S1z2 + + 9072 * delta * EulerGamma * kappa1 * params.S1z2 + 16632 * kappa1 * Nu * params.S1z2 - + 18144 * EulerGamma * kappa1 * Nu * params.S1z2 - complex(0, 4536) * kappa1 * M_PI * params.S1z2 - + complex(0, 4536) * delta * kappa1 * M_PI * params.S1z2 + complex(0, 9072) * kappa1 * Nu * M_PI * params.S1z2 - + 33264 * Nu * S1z * S2z + 36288 * EulerGamma * Nu * S1z * S2z - complex(0, 18144) * Nu * M_PI * S1z * S2z - + 8316 * kappa2 * params.S2z2 + 8316 * delta * kappa2 * params.S2z2 + 9072 * EulerGamma * kappa2 * params.S2z2 - + 9072 * delta * EulerGamma * kappa2 * params.S2z2 + 16632 * kappa2 * Nu * params.S2z2 - + 18144 * EulerGamma * kappa2 * Nu * params.S2z2 - complex(0, 4536) * kappa2 * M_PI * params.S2z2 + + complex(0, 4536) * delta * kappa2 * M_PI * params.S2z2 + complex(0, 9072) * kappa2 * Nu * M_PI * params.S2z2 - + 52152 * np.log(2) - 119760 * Nu * np.log(2) + 26880 * params.eta2 * np.log(2) + + 18144 * kappa1 * params.S1z2 * np.log(2) + 18144 * delta * kappa1 * params.S1z2 * np.log(2) - + 36288 * kappa1 * Nu * params.S1z2 * np.log(2) + 72576 * Nu * S1z * S2z * np.log(2) + + 18144 * kappa2 * params.S2z2 * np.log(2) - 18144 * delta * kappa2 * params.S2z2 * np.log(2) - + 36288 * kappa2 * Nu * params.S2z2 * np.log(2) + 12 * (-2173 - 4990 * Nu + 1120 * params.eta2 + + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(b0) + 18 * (-2173 - 4990 * Nu + + 1120 * params.eta2 + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(x))) + + # 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 * (params.x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * Nu) + + 12 * (628830397253 + 1854914893791 * Nu + 421984442880 * np.log(2)) + 415134720 * EulerGamma * (6099 - 40277 * Nu + complex(0, 70) * (107 - 13 * Nu) * M_PI + 280 * (-107 + 13 * Nu) * np.log(2)) + + 35 * (-28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) + + 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2 - 955450349568 * Nu * np.log(2) + + 3321077760 * (-107 + 13 * Nu) * np.log(2) * np.log(2) - complex(0, 5930496) * M_PI * (6099 - 74773 * Nu + 280 * (-107 + 13 * Nu) * np.log(2))) - 5700491596800 * np.log(mass) + + 6966444925440 * np.log(x) + 69189120 * (420 * (-107 + 13 * Nu) * np.log(b0) * np.log(b0) + + 420 * (-107 + 13 * Nu) * np.log(mass) * np.log(mass) - 14 * np.log(mass) * (-11803 * Nu + + 60 * EulerGamma * (-107 + 13 * Nu) + complex(0, 30) * (107 - 13 * Nu) * M_PI + + 120 * (-107 + 13 * Nu) * np.log(2) + 90 * (-107 + 13 * Nu) * np.log(x)) + 14 * np.log(b0) * (5885 - 6420 * EulerGamma - 11803 * Nu + 780 * EulerGamma * Nu + complex(0, 3210) * M_PI - + complex(0, 390) * Nu * M_PI + 120 * (-107 + 13 * Nu) * np.log(2) + (6420 - 780 * Nu) * np.log(mass) + + 90 * (-107 + 13 * Nu) * np.log(x)) + 5 * np.log(x) * (-74149 * Nu + 252 * EulerGamma * (-107 + 13 * Nu) - + complex(0, 126) * (-107 + 13 * Nu) * M_PI + 504 * (-107 + 13 * Nu) * np.log(2) + + 189 * (-107 + 13 * Nu) * np.log(x)) + 84672 * Nu * np.log(x0))))) + + # return ((params.x5 * (276756480 * EulerGamma * (11449 + 19105 * Nu) - 12 * (846557506853 + 1008017482431 * Nu) + 35 * (28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) - complex(0, 3953664) * (11449 + 109657 * Nu) * M_PI - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2) + 138378240 * (11449 + 19105 * Nu) * np.log(16 * x))) / 7.62810048e10) + + else: + return complex(0, 0) + +def hl_2_m_2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: KeplerVars) -> complex: + + if vpnorder < 0 or vpnorder > 8: + raise ValueError("Error in hl_2_m_2: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms + waveform_modes = ( + hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) + ) + + # cpolar(r, theta) in C is equivalent to rect(r, theta) in Python + phase_factor = rect(1.0, -2 * Phi) + + return amplitude * waveform_modes * phase_factor + +def hl_2_m_min2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: KeplerVars) -> complex: + + if vpnorder < 0 or vpnorder > 8: + raise ValueError("Error in hl_2_m_min2: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Sum the GO and QC terms, then apply the complex conjugate for the m = -2 mode + waveform_modes = ( + hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) + ).conjugate() + + # Calculate the phase factor with positive 2 * Phi + phase_factor = rect(1.0, 2 * Phi) + + return amplitude * waveform_modes * phase_factor + +# H21 + +def hGO_2_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + r0 = 2 * mass / np.exp(0.5) + + if vpnorder == 1: + return 0.6666666666666666j * delta * mass * PhiDOT + + elif vpnorder == 2: + return ( + (-0.5j * params.Mtot2 * + ((1 + delta) * S1z + (-1 + delta) * S2z)) + / params.r2 + ) + + elif vpnorder == 3: + return ( + (0.023809523809523808j * delta * mass * PhiDOT * + (4 * mass * (-9 + 11 * Nu) + + r * ((19 - 24 * Nu) * params.PhiDOT2 * params.r2 + + 2j * (83 + 2 * Nu) * PhiDOT * r * rDOT + + 2 * (-33 + 10 * Nu) * params.rDOT2))) + / r + ) + + elif vpnorder == 4: + return ( + (0.011904761904761904j * params.Mtot2 * + (2 * mass * + ((77 + 59 * Nu + 11 * delta * (7 + Nu)) * S1z + + (-77 - 59 * Nu + 11 * delta * (7 + Nu)) * S2z) + + r * (-2j * PhiDOT * r * rDOT * + (147 * (1 + delta) * S1z + (-83 + 13 * delta) * Nu * S1z + + 147 * (-1 + delta) * S2z + (83 + 13 * delta) * Nu * S2z) + + params.rDOT2 * + ((105 * (1 + delta) - 4 * (13 + 15 * delta) * Nu) * S1z + + (-105 + 15 * delta * (7 - 4 * Nu) + 52 * Nu) * S2z) + + 4 * params.PhiDOT2 * params.r2 * + ((-21 - 21 * delta + 66 * Nu + 4 * delta * Nu) * S1z + + (21 - 21 * delta - 66 * Nu + 4 * delta * Nu) * S2z)))) + / params.r3 + ) + + elif vpnorder == 5: + term1 = ( + (0.0013227513227513227j * delta * mass * PhiDOT * + (10 * params.Mtot2 * (31 - 205 * Nu + 111 * params.eta2) - + 2 * mass * r * + ((-197 + 5 * Nu + 660 * params.eta2) * params.PhiDOT2 * params.r2 + + 1j * (-3167 - 5278 * Nu + 201 * params.eta2) * PhiDOT * r * rDOT + + 8 * (202 + 587 * Nu - 177 * params.eta2) * params.rDOT2) + + 3 * params.r2 * + ((152 - 692 * Nu + 333 * params.eta2) * params.PhiDOT4 * params.r4 + + 2j * (308 - 1607 * Nu + 111 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT - + 3 * (75 - 560 * Nu + 68 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 - + 2j * (-265 + 526 * Nu + 18 * params.eta2) * PhiDOT * r * params.rDOT3 + + (-241 + 550 * Nu - 264 * params.eta2) * params.rDOT4))) + / params.r2 + ) + + # Henry et al. ecc spin terms + term2 = ( + (params.Mtot3 * + (-4 * rDOT * + (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + + kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) - + 1j * PhiDOT * r * + ((-((1 + delta) * (9 + kappa1)) + + 2 * (9 + (4 + 3 * delta) * kappa1) * Nu) * params.S1z2 - + 12 * delta * Nu * S1z * S2z + + (9 + kappa2 - 2 * (9 + 4 * kappa2) * Nu + + delta * (-9 - kappa2 + 6 * kappa2 * Nu)) * params.S2z2))) + / (6.0 * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + (delta * params.Mtot2 * Nu * PhiDOT * + (mass * (195 * PhiDOT * r - 946j * rDOT) + + 9 * r * + (270 * params.PhiDOT3 * params.r3 - + 483j * params.PhiDOT2 * params.r2 * rDOT - + 580 * PhiDOT * r * params.rDOT2 + + 42j * params.rDOT3))) + / (315.0 * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.00033068783068783067j * params.Mtot2 * + (3 * params.r2 * + (4j * PhiDOT * r * params.rDOT3 * + ((-315 * (1 + delta) + 2 * (251 + 463 * delta) * Nu + + 4 * (15 + delta) * params.eta2) * S1z + + (315 - 315 * delta - 502 * Nu + 926 * delta * Nu + + 4 * (-15 + delta) * params.eta2) * S2z) + + 12 * params.PhiDOT2 * params.r2 * params.rDOT2 * + ((189 * (1 + delta) - 2 * (521 + 293 * delta) * Nu + + 7 * (55 + 23 * delta) * params.eta2) * S1z + + (189 * (-1 + delta) + 2 * (521 - 293 * delta) * Nu + + 7 * (-55 + 23 * delta) * params.eta2) * S2z) + + params.rDOT4 * + ((567 * (1 + delta) - 16 * (77 + 64 * delta) * Nu + + 8 * (177 + 173 * delta) * params.eta2) * S1z + + (567 * (-1 + delta) + 16 * (77 - 64 * delta) * Nu + + 8 * (-177 + 173 * delta) * params.eta2) * S2z) - + 4j * params.PhiDOT3 * params.r3 * rDOT * + ((936 * (1 + delta) - 5 * (979 + 215 * delta) * Nu + + 2 * (1353 + 293 * delta) * params.eta2) * S1z + + (936 * (-1 + delta) + 5 * (979 - 215 * delta) * Nu + + 2 * (-1353 + 293 * delta) * params.eta2) * S2z) + + 4 * params.PhiDOT4 * params.r4 * + ((-252 * (1 + delta) + (1315 + 857 * delta) * Nu + + 4 * (-285 + 43 * delta) * params.eta2) * S1z + + (252 + 5 * Nu * (-263 + 228 * Nu) + + delta * (-252 + Nu * (857 + 172 * Nu))) * S2z)) - + 2 * mass * r * + (-1j * PhiDOT * r * rDOT * + ((2043 * (1 + delta) + (37 + 2597 * delta) * Nu + + (10635 + 139 * delta) * params.eta2) * S1z + + (2043 * (-1 + delta) + (-37 + 2597 * delta) * Nu + + (-10635 + 139 * delta) * params.eta2) * S2z) + + params.PhiDOT2 * params.r2 * + ((-765 - Nu * (667 + 7773 * Nu) + + delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S1z + + (765 + Nu * (667 + 7773 * Nu) + + delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S2z) + + 4 * params.rDOT2 * + ((-234 * (1 + delta) - 4 * (560 + 901 * delta) * Nu + + (483 + 1111 * delta) * params.eta2) * S1z + + (234 + 7 * (320 - 69 * Nu) * Nu + + delta * (-234 + Nu * (-3604 + 1111 * Nu))) * S2z)) + + 2 * params.Mtot2 * + (1134 * kappa1 * (-1 - delta + (3 + delta) * Nu) * params.S1z3 + + 1134 * (1 + delta) * (-2 + kappa1) * Nu * params.S1z2 * S2z + + S1z * + (-5661 - 5661 * delta - 17156 * Nu - 9172 * delta * Nu + + 231 * params.eta2 + 775 * delta * params.eta2 + + 1134 * (-1 + delta) * (-2 + kappa2) * Nu * params.S2z2) + + S2z * (5661 - 5661 * delta + 17156 * Nu - 9172 * delta * Nu - + 231 * params.eta2 + 775 * delta * params.eta2 + + 1134 * kappa2 * (1 - delta + (-3 + delta) * Nu) * + params.S2z2))) + ) / params.r4 + + + return term1 + term2 + + elif vpnorder == 7: + + spin_terms = ( + -23760 * params.Mtot3 * + (params.PhiDOT3 * params.r4 * + (-12j * (-35 + 107 * Nu) * S1z + + 5 * (126 - 462 * Nu + 80 * params.eta2 + + kappa1 * (-2 - 79 * Nu + 153 * params.eta2)) * params.S1z2 + + S2z * (-420j + 1284j * Nu + + 10 * (-63 + kappa2) * S2z + + 5 * (462 + 79 * kappa2) * Nu * S2z - + 5 * (80 + 153 * kappa2) * params.eta2 * S2z)) - + 1j * params.PhiDOT2 * params.r3 * rDOT * + (-24j * (-35 + 202 * Nu) * S1z + + 5 * (-14 * Nu * (3 + 58 * Nu) + + kappa1 * (-409 + 1096 * Nu + 6 * params.eta2)) * params.S1z2 + + S2z * (-840j + 2045 * kappa2 * S2z + + 10 * (406 - 3 * kappa2) * params.eta2 * S2z + + Nu * (4848j + 210 * S2z - 5480 * kappa2 * S2z))) - + 4j * r * params.rDOT3 * + (3j * (26 + 57 * Nu) * S1z + + 5 * (4 * params.eta2 + + kappa1 * (-7 + 49 * Nu + 15 * params.eta2)) * params.S1z2 - + S2z * (78j - 35 * kappa2 * S2z + + 5 * (4 + 15 * kappa2) * params.eta2 * S2z + + Nu * (171j + 245 * kappa2 * S2z))) + + 2j * mass * rDOT * + (-4j * (-78 + 769 * Nu) * S1z + + 5 * ((14 - 59 * Nu) * Nu + + kappa1 * (-70 - 77 * Nu + 18 * params.eta2)) * params.S1z2 + + S2z * (-312j + 350 * kappa2 * S2z + + 5 * (59 - 18 * kappa2) * params.eta2 * S2z + + Nu * (3076j - 70 * S2z + 385 * kappa2 * S2z))) + + PhiDOT * params.r2 * params.rDOT2 * + (6j * (-62 + 219 * Nu) * S1z - + 5 * (189 - 756 * Nu + 388 * params.eta2 + + kappa1 * (98 - 266 * Nu + 276 * params.eta2)) * params.S1z2 + + S2z * (372j + 945 * S2z + 490 * kappa2 * S2z + + 20 * (97 + 69 * kappa2) * params.eta2 * S2z - + 2 * Nu * (657j + 35 * (54 + 19 * kappa2) * S2z))) + + mass * PhiDOT * r * + (8j * (-61 + 480 * Nu) * S1z + + 5 * (-392 + 448 * Nu + 474 * params.eta2 + + kappa1 * (-11 + 150 * Nu + 58 * params.eta2)) * params.S1z2 - + S2z * (-488j - 5 * (392 + 11 * kappa2) * S2z + + 10 * (237 + 29 * kappa2) * params.eta2 * S2z + + 10 * Nu * (384j + (224 + 75 * kappa2) * S2z)))) + ) + + orbital_terms = ( + delta * mass * + (-240 * mass * PhiDOT * params.r3 * + ((197936 - 139360 * Nu - 367105 * params.eta2 + + 253245 * params.eta3) * params.PhiDOT4 * params.r4 + + 1j * (279236 - 483940 * Nu - 2817805 * params.eta2 + + 459180 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT - + 6 * (38627 + 89295 * Nu - 492740 * params.eta2 + + 75975 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 - + 1j * (-731008 + 2287930 * Nu + 981060 * params.eta2 + + 10275 * params.eta3) * PhiDOT * r * params.rDOT3 + + (-327667 + 436705 * Nu + 659790 * params.eta2 - + 438255 * params.eta3) * params.rDOT4) + + 900 * PhiDOT * params.r4 * + (2 * (-2594 + 27609 * Nu - 74032 * params.eta2 + + 25974 * params.eta3) * params.PhiDOT6 * params.r6 + + 4j * (-5730 + 58833 * Nu - 137842 * params.eta2 + + 17123 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + + 2 * (-114 - 41622 * Nu + 147569 * params.eta2 + + 4196 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + + 4j * (-9554 + 70788 * Nu - 156227 * params.eta2 + + 5810 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + + (17619 - 138450 * Nu + 322600 * params.eta2 - + 80816 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - + 2j * (8793 - 52230 * Nu + 69340 * params.eta2 + + 2536 * params.eta3) * PhiDOT * r * params.rDOT5 + + 2 * (3957 - 24534 * Nu + 42584 * params.eta2 - + 20800 * params.eta3) * params.rDOT6) - + 2 * params.Mtot3 * + (-23760j * rDOT * + (5 * (Nu * (-14 + 31 * Nu) + 7 * kappa1 * (10 + 31 * Nu)) * params.S1z2 + + 2 * S1z * (-156j + 155 * params.eta2 * S2z + + 2 * Nu * (613j + 390 * S2z)) + + S2z * (-312j + 350 * kappa2 * S2z + + 155 * params.eta2 * S2z + + Nu * (2452j + 35 * (-2 + 31 * kappa2) * S2z))) + + PhiDOT * r * + (8946400 * params.eta3 - + 8 * (6991786 + 724680j * S1z + + 7425 * (392 + 11 * kappa1) * params.S1z2 + + 724680j * S2z + + 7425 * (392 + 11 * kappa2) * params.S2z2) - + 3600 * params.eta2 * + (-628 + 33 * (-19 + 92 * kappa1) * params.S1z2 - + 7326 * S1z * S2z + + 33 * (-19 + 92 * kappa2) * params.S2z2) + + 15 * Nu * + (994455 * M_PI2 + + 8 * (-2249485 + + 7920 * (-21 + 8 * kappa1) * params.S1z2 + + 283536j * S2z + + 7920 * (-21 + 8 * kappa2) * params.S2z2 - + 1584 * S1z * (-179j + 170 * S2z))))) + + 3 * params.Mtot2 * r * + (31680j * params.rDOT3 * + (5 * (4 * params.eta2 + 7 * kappa1 * (-1 + 5 * Nu)) * params.S1z2 + + S1z * (78j + 40 * params.eta2 * S2z + + Nu * (327j + 420 * S2z)) + + S2z * (78j - 35 * kappa2 * S2z + + 20 * params.eta2 * S2z + + Nu * (327j + 175 * kappa2 * S2z))) - + 22 * PhiDOT * r * params.rDOT2 * + (2553200 * params.eta3 - + 24 * (268267 + 5580j * S1z + + 525 * (27 + 14 * kappa1) * params.S1z2 + + 5580j * S2z + + 525 * (27 + 14 * kappa2) * params.S2z2) - + 200 * params.eta2 * + (39445 + 72 * (-4 + 21 * kappa1) * params.S1z2 - + 3600 * S1z * S2z + + 72 * (-4 + 21 * kappa2) * params.S2z2) + + 25 * Nu * + (23247 * M_PI2 + + 8 * (-69259 + 1026j * S1z + + 126 * (27 + 5 * kappa1) * params.S1z2 + + 1026j * S2z + + 126 * (27 + 5 * kappa2) * params.S2z2))) + + params.PhiDOT3 * params.r3 * + (10071200 * params.eta3 + + 96 * (-421183 - 34650j * S1z + + 825 * (-63 + kappa1) * params.S1z2 - + 34650j * S2z + + 825 * (-63 + kappa2) * params.S2z2) - + 400 * params.eta2 * + (64177 + 792 * (-5 + 6 * kappa1) * params.S1z2 - + 17424 * S1z * S2z + + 792 * (-5 + 6 * kappa2) * params.S2z2) + + 15 * Nu * + (426195 * M_PI2 + + 8 * (-509635 + + 330 * (210 + 83 * kappa1) * params.S1z2 + + 29304j * S2z + + 330 * (210 + 83 * kappa2) * params.S2z2 - + 792 * S1z * (-37j + 70 * S2z)))) - + 2j * params.PhiDOT2 * params.r2 * rDOT * + (-8330400 * params.eta3 + + 8 * (-2810116 - 415800j * S1z + + 1012275 * kappa1 * params.S1z2 - + 415800j * S2z + + 1012275 * kappa2 * params.S2z2) + + 4800 * params.eta2 * + (13411 + 33 * (19 + 12 * kappa1) * params.S1z2 + + 462 * S1z * S2z + + 33 * (19 + 12 * kappa2) * params.S2z2) + + 5 * Nu * + (1278585 * M_PI2 - + 8 * (5139685 + + 990 * (-21 + 139 * kappa1) * params.S1z2 - + 313632j * S2z + + 990 * (-21 + 139 * kappa2) * params.S2z2 - + 3564 * S1z * (88j + 185 * S2z)))))) + ) + + log_term = ( + -13559040 * delta * params.Mtot3 * PhiDOT * r * + (2 * mass - 3 * params.PhiDOT2 * params.r3 + + 6j * PhiDOT * params.r2 * rDOT + + 6 * r * params.rDOT2) * + np.log(r / r0) + ) + + return ( + -1.0020843354176688e-7j * + (spin_terms + orbital_terms + log_term) + ) / params.r4 + + else: + return 0.0 + 0.0j + +def hQC_2_m_1( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + + delta: float = np.sqrt(1.0 - 4.0 * Nu) + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + r0: float = b0 + + # Common log terms to simplify the messy 4PN-7PN expressions + log_term_base = (-7.0 + 6.0 * EulerGamma - 3j * M_PI + np.log(64.0) + 6.0 * np.log(b0) + 9.0 * np.log(x)) + + if vpnorder == 4: + return (-2.0 * delta * params.x3 * log_term_base) / 9.0 + + elif vpnorder == 5: + spin_factor = ((1.0 + delta) * S1z + (-1.0 + delta) * S2z) + return (spin_factor * params.x3p5 * log_term_base) / 6.0 + + elif vpnorder == 6: + return -0.007936507936507936 * (delta * (-17.0 + 6.0 * Nu) * params.x4 * log_term_base) + + elif vpnorder == 7: + # Heavily nested 3.5PN (order 7) term + term1 = (params.x4p5 * ( + -98 * S1z + 84 * EulerGamma * S1z + 3017 * Nu * S1z - 2586 * EulerGamma * Nu * S1z - + 42j * M_PI * S1z + 1293j * Nu * M_PI * S1z + 98 * S2z - 84 * EulerGamma * S2z - + 3017 * Nu * S2z + 2586 * EulerGamma * Nu * S2z + 42j * M_PI * S2z - 1293j * Nu * M_PI * S2z + + 84 * S1z * np.log(2) - 2586 * Nu * S1z * np.log(2) - 84 * S2z * np.log(2) + 2586 * Nu * S2z * np.log(2) + + 84 * S1z * np.log(b0) - 2586 * Nu * S1z * np.log(b0) - 84 * S2z * np.log(b0) + 2586 * Nu * S2z * np.log(b0) + + 126 * S1z * np.log(x) - 3879 * Nu * S1z * np.log(x) - 126 * S2z * np.log(x) + 3879 * Nu * S2z * np.log(x) + + 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 * Nu * S1z) / 12.0 - (29 * EulerGamma * Nu * S1z) / 14.0 - + 0.16666666666666666j * M_PI * S1z + 1.0357142857142858j * Nu * M_PI * S1z - + (7 * S2z) / 18.0 + (EulerGamma * S2z) / 3.0 + (29 * Nu * S2z) / 12.0 - (29 * EulerGamma * Nu * S2z) / 14.0 - + 0.16666666666666666j * M_PI * S2z + 1.0357142857142858j * Nu * M_PI * S2z + + 1.7523809523809524j * np.log(2) - 2.6666666666666665j * EulerGamma * np.log(2) - + (4 * M_PI * np.log(2)) / 3.0 + (S1z * np.log(2)) / 3.0 - (29 * Nu * S1z * np.log(2)) / 14.0 + + (S2z * np.log(2)) / 3.0 - (29 * Nu * S2z * np.log(2)) / 14.0 - 1.3333333333333333j * np.log(2)**2 - + 1.3333333333333333j * np.log(b0)**2 - 1.3587301587301588j * np.log(r0) + + (np.log(b0) * (392j - 336j * EulerGamma - 168 * M_PI + 42 * S1z - 261 * Nu * S1z + 42 * S2z - 261 * Nu * S2z - 336j * np.log(2) - 504j * np.log(x))) / 126.0 + + 2.6285714285714286j * np.log(x) - 4j * EulerGamma * np.log(x) - 2 * M_PI * np.log(x) + + (S1z * np.log(x)) / 2.0 - (87 * Nu * S1z * np.log(x)) / 28.0 + (S2z * np.log(x)) / 2.0 - + (87 * Nu * S2z * np.log(x)) / 28.0 - 4j * np.log(2) * np.log(x) - 3j * np.log(x)**2 + ) + )) / 252.0 + return term1 + + else: + return 0.0 + 0.0j + +def hl_2_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_2_m_1: Input PN order parameter should be between [0, 8]." + ) + + # Calculate the pre-factor: (4 * M * eta * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + go_component: complex = hGO_2_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_2_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + phase_factor: complex = np.exp(-1j * Phi) + + # Combine terms + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_2_m_min1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_2_m_min1: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # These are the same functions used for the m=1 mode + go_component: complex = hGO_2_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_2_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # In C: conj(hGO + hQC) + # In Python: use the .conjugate() method or np.conj() + amplitude_term: complex = (go_component + qc_component).conjugate() + + # In C: cpolar(1, 1 * Phi) -> exp(i * Phi) + # Note the sign change from the m=1 mode + phase_factor: complex = np.exp(1j * Phi) + + return pre_factor * amplitude_term * phase_factor + +# H33 + +def hGO_3_m_3( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + r0 = 2 * mass / np.exp(0.5) + + 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_d2 = combination_d ** 2 + combination_d5 = combination_d ** 5 + + combination_e = 1j * PhiDOT * r + rDOT + combination_e3 = combination_e ** 3 + + if vpnorder == 1: + return ( + (np.sqrt(0.11904761904761904) * delta * + (2 * r * combination_a3 + + mass * (-7j * PhiDOT * r + 4 * rDOT))) + / (2.0 * r) + ) + + elif vpnorder == 3: + return ( + (np.sqrt(0.11904761904761904) * delta * + (6 * (-5 + 19 * Nu) * params.r2 * combination_b4 * + (1j * PhiDOT * r + rDOT) + + 2 * params.Mtot2 * + (-3j * (-101 + 43 * Nu) * PhiDOT * r + + (-109 + 86 * Nu) * rDOT) + + 3 * mass * r * + (-12j * (1 + 4 * Nu) * params.PhiDOT3 * params.r3 + + 6 * (14 + 31 * Nu) * params.PhiDOT2 * params.r2 * rDOT + + 3j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 - + 4 * (8 + 17 * Nu) * params.rDOT3))) + / (36.0 * params.r2) + ) + + elif vpnorder == 4: + return ( + (-0.125j * np.sqrt(0.11904761904761904) * params.Mtot2 * + (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) + + r * (2 * params.rDOT2 * + (6 * (1 + delta) * S1z - 5 * (5 + 3 * delta) * Nu * S1z + + (-6 + delta * (6 - 15 * Nu) + 25 * Nu) * S2z) + + params.PhiDOT2 * params.r2 * + (-24 * (1 + delta) * S1z + (119 + 33 * delta) * Nu * S1z + + (24 - 119 * Nu + 3 * delta * (-8 + 11 * Nu)) * S2z) + + 2j * PhiDOT * r * rDOT * + (-18 * (1 + delta) * S1z + (77 + 39 * delta) * Nu * S1z + + (18 - 77 * Nu + 3 * delta * (-6 + 13 * Nu)) * S2z)))) + / params.r3 + ) + + elif vpnorder == 5: + term1 = ( + delta * + (30 * (183 - 1579 * Nu + 3387 * params.eta2) * params.r3 * + combination_c2 * combination_d5 + + 10 * params.Mtot3 * + (-1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r + + 4 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + + 2 * params.Mtot2 * r * + (-11j * (-5353 - 13493 * Nu + 4671 * params.eta2) * params.PhiDOT3 * params.r3 + + (-75243 - 142713 * Nu + 192821 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT + + 220j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 - + 10 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + + 3 * mass * params.r2 * + (2j * (-7633 + 9137 * Nu + 28911 * params.eta2) * params.PhiDOT5 * params.r5 - + 4 * (-8149 + 1576 * Nu + 43533 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT - + 2j * (-9297 - 19517 * Nu + 64839 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - + 32 * (-1288 + 3667 * Nu + 4056 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 - + 5j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 + + 20 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) + / (1584.0 * np.sqrt(210) * params.r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.125j * np.sqrt(1.0714285714285714) * params.Mtot3 * + (7 * PhiDOT * r + 2j * rDOT) * + (kappa1 * (-1 - delta + 2 * (2 + delta) * Nu) * params.S1z2 + + S2z * (-4 * delta * Nu * S1z + + kappa2 * (1 - delta + 2 * (-2 + delta) * Nu) * S2z)) + / params.r3 + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + -(delta * params.Mtot2 * Nu * + (668 * params.Mtot2 + + 2 * mass * r * + (4081 * params.PhiDOT2 * params.r2 + + 297j * PhiDOT * r * rDOT - 452 * params.rDOT2) + + 5 * params.r2 * + (1329 * params.PhiDOT4 * params.r4 - + 2926j * params.PhiDOT3 * params.r3 * rDOT - + 384 * params.PhiDOT2 * params.r2 * params.rDOT2 - + 408j * PhiDOT * r * params.rDOT3 + + 200 * params.rDOT4))) + / (36.0 * np.sqrt(210) * params.r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + -0.006944444444444444j * params.Mtot2 * + (10 * params.Mtot2 * + ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + + 8 * (12 + 47 * delta) * params.eta2) * S1z + + (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + + 8 * (-12 + 47 * delta) * params.eta2) * S2z) + + 2 * mass * r * + (2 * params.PhiDOT2 * params.r2 * + ((1320 * (1 + delta) - 2 * (4469 + 211 * delta) * Nu + + (8709 + 2777 * delta) * params.eta2) * S1z + + (1320 * (-1 + delta) + 8938 * Nu - 422 * delta * Nu + + (-8709 + 2777 * delta) * params.eta2) * S2z) + + 3j * PhiDOT * r * rDOT * + ((2000 * (1 + delta) - (9147 + 3173 * delta) * Nu + + (8911 + 5273 * delta) * params.eta2) * S1z + + (2000 * (-1 + delta) + (9147 - 3173 * delta) * Nu + + (-8911 + 5273 * delta) * params.eta2) * S2z) + + 10 * params.rDOT2 * + ((-105 * (1 + delta) + (541 + 77 * delta) * Nu - + 2 * (462 + 247 * delta) * params.eta2) * S1z + + (105 + Nu * (-541 + 924 * Nu) + + delta * (-105 + (77 - 494 * Nu) * Nu)) * S2z)) - + 3 * params.r2 * + (-3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + ((480 * (1 + delta) - (1711 + 1889 * delta) * Nu + + 2 * (-1161 + 757 * delta) * params.eta2) * S1z + + (480 * (-1 + delta) + (1711 - 1889 * delta) * Nu + + 2 * (1161 + 757 * delta) * params.eta2) * S2z) + + 2 * params.PhiDOT4 * params.r4 * + ((350 * (1 + delta) - 4 * (404 + 461 * delta) * Nu + + (883 + 769 * delta) * params.eta2) * S1z + + (350 * (-1 + delta) + 4 * (404 - 461 * delta) * Nu + + (-883 + 769 * delta) * params.eta2) * S2z) + + 2j * params.PhiDOT3 * params.r3 * rDOT * + ((660 * (1 + delta) - (4061 + 2899 * delta) * Nu + + (2643 + 4789 * delta) * params.eta2) * S1z + + (660 * (-1 + delta) + (4061 - 2899 * delta) * Nu + + (-2643 + 4789 * delta) * params.eta2) * S2z) + + 10 * params.rDOT4 * + ((-30 * (1 + delta) + (187 + 101 * delta) * Nu - + 2 * (159 + 61 * delta) * params.eta2) * S1z + + (30 + Nu * (-187 + 318 * Nu) + + delta * (-30 + (101 - 122 * Nu) * Nu)) * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + ((90 + Nu * (-1321 + 5118 * Nu) + + delta * (90 + Nu * (-319 + 714 * Nu))) * S1z + + (-90 + (1321 - 5118 * Nu) * Nu + + delta * (90 + Nu * (-319 + 714 * Nu))) * S2z))) + / (np.sqrt(210) * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + M_PI2 = M_PI ** 2 + + spin_terms = ( + 504504 * params.Mtot3 * + (2 * mass * + (rDOT * + (S1z * (108 - 498 * Nu + + 5j * (-24 - 5 * kappa1 + 3 * (76 + kappa1) * Nu + + 4 * (-111 + 13 * kappa1) * params.eta2) * S1z) + + 6 * (-18 + 83 * Nu) * S2z - + 5j * (-24 - 5 * kappa2 + 3 * (76 + kappa2) * Nu + + 4 * (-111 + 13 * kappa2) * params.eta2) * params.S2z2) + + PhiDOT * r * + (S1z * (-3j * (-99 + 581 * Nu) + + 5 * (-24 + 399 * kappa1 + 48 * (7 - 19 * Nu) * Nu + + kappa1 * Nu * (-1629 + 188 * Nu)) * S1z) + + 3j * (-99 + 581 * Nu) * S2z - + 5 * (-24 + 399 * kappa2 + 48 * (7 - 19 * Nu) * Nu + + kappa2 * Nu * (-1629 + 188 * Nu)) * params.S2z2)) + + r * (3 * PhiDOT * r * params.rDOT2 * + (S1z * (216j + 545 * kappa1 * S1z + + 40 * (45 + 8 * kappa1) * params.eta2 * S1z - + 30 * Nu * (50j + 20 * S1z + 73 * kappa1 * S1z)) + + 12j * (-18 + 125 * Nu) * S2z - + 5 * (109 * kappa2 - 6 * (20 + 73 * kappa2) * Nu + + 8 * (45 + 8 * kappa2) * params.eta2) * params.S2z2) + + 2 * params.rDOT3 * + (S1z * (-54 + 145j * kappa1 * S1z - + 30j * Nu * (11j + 2 * Nu * S1z + + kappa1 * (17 + 8 * Nu) * S1z)) + + 6 * (9 - 55 * Nu) * S2z + + 5j * (12 * params.eta2 + + kappa2 * (-29 + 6 * Nu * (17 + 8 * Nu))) * params.S2z2) + + 6 * params.PhiDOT2 * params.r2 * rDOT * + (S1z * (297 - 465 * Nu + + 5j * (6 * (20 - 87 * Nu) * Nu + + kappa1 * (-50 + 3 * Nu * (47 + 76 * Nu))) * S1z) + + 3 * (-99 + 155 * Nu) * S2z - + 5j * (6 * (20 - 87 * Nu) * Nu + + kappa2 * (-50 + 3 * Nu * (47 + 76 * Nu))) * params.S2z2) + + params.PhiDOT3 * params.r3 * + (3j * (531 - 1295 * Nu) * S1z + + 10 * (-33 * kappa1 + 6 * (30 + 13 * kappa1) * Nu + + 4 * (-96 + 67 * kappa1) * params.eta2) * params.S1z2 + + S2z * (-1593j + 330 * kappa2 * S2z + + 5 * Nu * (777j - + 4 * (90 + 39 * kappa2 - 192 * Nu + + 134 * kappa2 * Nu) * S2z))))) + ) + + orbital_terms = ( + delta * + (-17640 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * + params.r4 * combination_b6 * combination_e3 + + 168 * params.Mtot2 * params.r2 * + (1j * (-7508635 + 7 * Nu * (-1318438 + Nu * (-10231834 + 9667755 * Nu))) * + params.PhiDOT5 * params.r5 + + 7 * (1235591 + Nu * (884445 + (23935218 - 26913443 * Nu) * Nu)) * + params.PhiDOT4 * params.r4 * rDOT - + 1j * (8961149 + 7 * Nu * (-31755709 + Nu * (-11134798 + 22187331 * Nu))) * + params.PhiDOT3 * params.r3 * params.rDOT2 - + (-36806435 + 7 * Nu * (33178545 + Nu * (24565078 + 22873537 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT3 - + 5j * (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * + PhiDOT * r * params.rDOT4 + + 5 * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * + params.rDOT5) + + 1764 * mass * params.r3 * + (-2j * (239087 + Nu * (-1206515 + Nu * (422631 + 3979375 * Nu))) * + params.PhiDOT7 * params.r7 + + 2 * (621284 + Nu * (-2279907 + 2 * Nu * (-1180187 + 5876531 * Nu))) * + params.PhiDOT6 * params.r6 * rDOT + + 2j * (39270 + Nu * (1235486 - 5319747 * Nu + 4406349 * params.eta2)) * + params.PhiDOT5 * params.r5 * params.rDOT2 + + 8 * (349111 + 4 * Nu * (-519370 + 33 * Nu * (10939 + 42635 * Nu))) * + params.PhiDOT4 * params.r4 * params.rDOT3 + + 2j * (1212607 + 3 * Nu * (-2012698 - 67827 * Nu + 7955628 * params.eta2)) * + params.PhiDOT3 * params.r3 * params.rDOT4 + + 4 * (201135 + 2 * Nu * (-773107 + Nu * (1214819 + 1157652 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT5 + + 5j * (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * + PhiDOT * r * params.rDOT6 - + 40 * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * + params.rDOT7) + + 2 * params.Mtot4 * + (4 * rDOT * + (269279500 * params.eta3 + + 2 * (-174108226 + + 63063 * S1z * (108j + 5 * (24 + 5 * kappa1) * S1z) + + 63063 * S2z * (108j + 5 * (24 + 5 * kappa2) * S2z)) - + 21 * Nu * + (103100846 + 1846845 * M_PI2 + 1693692j * S2z - + 6006 * (S1z * (-282j + 5 * (-180 + 7 * kappa1) * S1z) - + 980 * S1z * S2z + + 5 * (-180 + 7 * kappa2) * params.S2z2)) - + 2940 * params.eta2 * + (-122855 + + 4719 * ((-6 + 7 * kappa1) * params.S1z2 - + 26 * S1z * S2z + + (-6 + 7 * kappa2) * params.S2z2))) + + 1j * PhiDOT * r * + (-1176172480 * params.eta3 + + 8 * (-74084729 + + 189189 * S1z * (99j + 5 * (-8 + 133 * kappa1) * S1z) + + 189189 * S2z * (99j + 5 * (-8 + 133 * kappa2) * S2z)) + + 35280 * params.eta2 * + (56255 + + 429 * ((-22 + 65 * kappa1) * params.S1z2 - + 174 * S1z * S2z + + (-22 + 65 * kappa2) * params.S2z2)) - + 147 * Nu * + (-65012788 + 4485195 * M_PI2 + 3943368j * S2z + + 10296 * (S1z * (383j + 5 * (-96 + 277 * kappa1) * S1z) - + 3220 * S1z * S2z + + 5 * (-96 + 277 * kappa2) * params.S2z2)))) + + params.Mtot3 * r * + (-12j * PhiDOT * r * params.rDOT2 * + (-1035895280 * params.eta3 - + 2 * (-547993687 + + 63063 * S1z * (216j + 545 * kappa1 * S1z) + + 63063 * S2z * (216j + 545 * kappa2 * S2z)) + + 77 * Nu * + (42451610 + 1511055 * M_PI2 + 1749384j * S2z + + 6552 * (S1z * (267j + 25 * (6 + 11 * kappa1) * S1z) - + 5 * S1z * S2z + + 25 * (6 + 11 * kappa2) * params.S2z2)) + + 490 * params.eta2 * + (-5802767 + + 5148 * ((-6 + 23 * kappa1) * params.S1z2 - + 58 * S1z * S2z + + (-6 + 23 * kappa2) * params.S2z2))) + + 4 * params.rDOT3 * + (-1359334480 * params.eta3 - + 4 * (-150254558 + + 63063 * S1z * (54j + 145 * kappa1 * S1z) + + 63063 * S2z * (54j + 145 * kappa2 * S2z)) + + 231 * Nu * + (8490448 + 503685 * M_PI2 + 242424j * S2z + + 2184 * (S1z * (111j + 110 * kappa1 * S1z) + + 70 * S1z * S2z + + 110 * kappa2 * params.S2z2)) + + 11760 * params.eta2 * + (-312980 + + 429 * ((3 + 25 * kappa1) * params.S1z2 - + 44 * S1z * S2z + + (3 + 25 * kappa2) * params.S2z2))) + + 6 * params.PhiDOT2 * params.r2 * rDOT * + (2368900688 * params.eta3 + + 8 * (-812986529 + + 63063 * S1z * (297j + 250 * kappa1 * S1z) + + 63063 * S2z * (297j + 250 * kappa2 * S2z)) - + 1176 * params.eta2 * + (2423171 + + 4290 * ((-3 + 41 * kappa1) * params.S1z2 - + 88 * S1z * S2z + + (-3 + 41 * kappa2) * params.S2z2)) + + 539 * Nu * + (-24139772 + 647595 * M_PI2 + 120744j * S2z - + 936 * (S1z * (-129j + 600 * S1z + 205 * kappa1 * S1z) - + 460 * S1z * S2z + + 5 * (120 + 41 * kappa2) * params.S2z2))) + + 1j * params.PhiDOT3 * params.r3 * + (-4538040136 * params.eta3 - + 88 * (259018351 + + 17199 * S1z * (-531j + 110 * kappa1 * S1z) + + 17199 * S2z * (-531j + 110 * kappa2 * S2z)) + + 2352 * params.eta2 * + (7332973 + + 12870 * ((5 + 23 * kappa1) * params.S1z2 - + 36 * S1z * S2z + + (5 + 23 * kappa2) * params.S2z2)) + + 21 * Nu * + (49864815 * M_PI2 + + 8 * (-88128538 - 2099097j * S2z + + 9009 * (S1z * (-233j + 40 * (15 + kappa1) * S1z) + + 360 * S1z * S2z + + 40 * (15 + kappa2) * params.S2z2)))))) + ) + + log_term = ( + 74954880 * delta * params.Mtot3 * + (22j * mass * PhiDOT * r + + 59j * params.PhiDOT3 * params.r4 + 8 * mass * rDOT + + 66 * params.PhiDOT2 * params.r3 * rDOT + + 24j * PhiDOT * params.r2 * params.rDOT2 - + 4 * r * params.rDOT3) * + np.log(r / r0) + ) + + return ( + 1j * (spin_terms + orbital_terms + log_term) + / (2.4216192e7 * np.sqrt(210) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_3_m_3( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + + delta: float = np.sqrt(1.0 - 4.0 * Nu) + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + r0: float = b0 + + if vpnorder == 4: + return ( + (3.0 * np.sqrt(0.04285714285714286) * delta * params.x3 * ( + -97.0 + 60.0 * EulerGamma - 30.0j * M_PI + + 60.0 * np.log(2.0) + 60.0 * np.log(3.0) + + 60.0 * np.log(b0) + 90.0 * np.log(x) + )) / 4.0 + ) + + elif vpnorder == 6: + numerator_6 = (delta * params.x4 * ( + 188568.0 - 116640.0 * EulerGamma - 70537.0 * Nu + 43740.0 * EulerGamma * Nu + + 58320.0j * M_PI - 21870.0j * Nu * M_PI - + 116640.0 * np.log(2.0) + 43740.0 * Nu * np.log(2.0) - + 116640.0 * np.log(3.0) + 43740.0 * Nu * np.log(3.0) + + 14580.0 * (-8.0 + 3.0 * Nu) * np.log(b0) + + 21870.0 * (-8.0 + 3.0 * Nu) * np.log(x) + )) + 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 * np.log(b0) * ( + -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * M_PI + + 5.0 * (-4.0 - 4.0 * delta + 19.0 * Nu + 5.0 * delta * Nu) * S1z + + 20.0 * S2z - 20.0 * delta * S2z - 95.0 * Nu * S2z + 25.0 * delta * Nu * S2z + + 120.0j * delta * np.log(2.0) + 120.0j * delta * np.log(3.0) + + 180.0j * delta * np.log(x) + ) + + numerator_7 = (params.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 * Nu * S1z - 643223.0 * delta * Nu * S1z + 1508220.0 * EulerGamma * Nu * S1z + + 396900.0 * delta * EulerGamma * Nu * S1z + 158760.0j * M_PI * S1z + 158760.0j * delta * M_PI * S1z - + 754110.0j * Nu * M_PI * S1z - 198450.0j * delta * Nu * M_PI * S1z - + 513324.0 * S2z + 513324.0 * delta * S2z + 317520.0 * EulerGamma * S2z - + 317520.0 * delta * EulerGamma * S2z + 2439857.0 * Nu * S2z - 643223.0 * delta * Nu * S2z - + 1508220.0 * EulerGamma * Nu * S2z + 396900.0 * delta * EulerGamma * Nu * S2z - + 158760.0j * M_PI * S2z + 158760.0j * delta * M_PI * S2z + + 754110.0j * Nu * M_PI * S2z - 198450.0j * delta * Nu * M_PI * S2z - + 2490264.0j * delta * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + + 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - + 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * Nu * S1z * np.log(2.0) + + 396900.0 * delta * Nu * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - + 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * Nu * S2z * np.log(2.0) + + 396900.0 * delta * Nu * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - + 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + + 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - + 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + + 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - + 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * Nu * S2z * np.log(3.0) + + 396900.0 * delta * Nu * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + + 952560.0j * delta * (np.log(3.0)**2) + 952560.0j * delta * (np.log(b0)**2) + + 589680.0j * delta * np.log(r0) - 3735396.0j * delta * np.log(x) + + 2857680.0j * delta * EulerGamma * np.log(x) + 1428840.0 * delta * M_PI * np.log(x) - + 476280.0 * S1z * np.log(x) - 476280.0 * delta * S1z * np.log(x) + + 2262330.0 * Nu * S1z * np.log(x) + 595350.0 * delta * Nu * S1z * np.log(x) + + 476280.0 * S2z * np.log(x) - 476280.0 * delta * S2z * np.log(x) - + 2262330.0 * Nu * S2z * np.log(x) + 595350.0 * delta * Nu * S2z * np.log(x) + + 2857680.0j * delta * np.log(2.0) * np.log(x) + + 2857680.0j * delta * np.log(3.0) * np.log(x) + + 2143260.0j * delta * (np.log(x)**2) + + log_b0_term + )) + return numerator_7 / (2352.0 * np.sqrt(210.0)) + + else: + return 0.0 + 0.0j + +def hl_3_m_3( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R + # Note: This specific pre-factor is identical across several modes in the C source + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + go_component: complex = hGO_3_m_3( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_3( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) + # The higher 'm' index means the phase evolves 3 times faster than the orbit + phase_factor: complex = np.exp(-3j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_3_m_min3( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R + # Note: This specific pre-factor is identical across several modes in the C source + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # We assume hGO_3_m_3 and hQC_3_m_3 are defined elsewhere in your package + go_component: complex = hGO_3_m_3( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_3( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + # In C: conj(hGO + hQC) + # In Python: use the .conjugate() method or np.conj() + amplitude_term: complex = (go_component + qc_component).conjugate() + + # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) + # The higher 'm' index means the phase evolves 3 times faster than the orbit + phase_factor: complex = np.exp(3j * Phi) + + return pre_factor * amplitude_term * phase_factor + +# H32 + +def hGO_3_m_2( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + + if vpnorder == 2: + return ( + -(np.sqrt(0.7142857142857143) * mass * (-1 + 3 * Nu) * PhiDOT * + (4 * PhiDOT * r + 1j * rDOT)) + / 6.0 + ) + + elif vpnorder == 3: + return ( + (np.sqrt(0.7142857142857143) * params.Mtot2 * Nu * + (4 * PhiDOT * r + 1j * rDOT) * (S1z + S2z)) + / (3.0 * params.r2) + ) + + elif vpnorder == 4: + return ( + -(mass * PhiDOT * + (2 * mass * + ((167 - 925 * Nu + 1615 * params.eta2) * PhiDOT * r + + 5j * (-82 + 239 * Nu + 55 * params.eta2) * rDOT) - + 3 * r * + (2 * (-13 - 25 * Nu + 355 * params.eta2) * params.PhiDOT3 * params.r3 - + 60j * (-8 + 25 * Nu + params.eta2) * params.PhiDOT2 * params.r2 * rDOT + + 12 * (-23 + 70 * Nu + 10 * params.eta2) * PhiDOT * r * params.rDOT2 + + 5j * (-13 + 38 * Nu + 10 * params.eta2) * params.rDOT3))) + / (108.0 * np.sqrt(35) * r) + ) + + elif vpnorder == 5: + term1 = ( + (params.Mtot2 * Nu * PhiDOT * + (7j * mass + + r * (49j * params.PhiDOT2 * params.r2 + + 90 * PhiDOT * r * rDOT - 6j * params.rDOT2))) + / (4.0 * np.sqrt(35) * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + (np.sqrt(0.7142857142857143) * params.Mtot2 * + (2j * mass * rDOT * + ((-12 + Nu * (97 + 4 * Nu) + delta * (-12 + 5 * Nu)) * S1z + + (-12 + delta * (12 - 5 * Nu) + Nu * (97 + 4 * Nu)) * S2z) + + 4 * mass * PhiDOT * r * + (-((12 + delta * (12 - 23 * Nu) + Nu * (53 + 8 * Nu)) * S1z) - + (12 + Nu * (53 + 8 * Nu) + delta * (-12 + 23 * Nu)) * S2z) - + 3 * r * + (16 * params.eta2 * PhiDOT * r * + (4 * params.PhiDOT2 * params.r2 - + 2j * PhiDOT * r * rDOT + params.rDOT2) * + (S1z + S2z) + + 30j * params.PhiDOT2 * params.r2 * rDOT * + (S1z + delta * S1z + S2z - delta * S2z) + + Nu * (-4 * params.PhiDOT3 * params.r3 * + ((5 + 17 * delta) * S1z + (5 - 17 * delta) * S2z) - + 1j * params.PhiDOT2 * params.r2 * rDOT * + ((189 + 17 * delta) * S1z + (189 - 17 * delta) * S2z) + + 20 * PhiDOT * r * params.rDOT2 * + (-((-3 + delta) * S1z) + (3 + delta) * S2z) - + 4j * params.rDOT3 * + ((-4 + delta) * S1z - (4 + delta) * S2z))))) + / (72.0 * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + -(mass * PhiDOT * + (4 * params.Mtot2 * + (2 * (5377 + 6438 * Nu - 79866 * params.eta2 + 37348 * params.eta3) * + PhiDOT * r - + 5j * (-4115 + 18399 * Nu - 20276 * params.eta2 + 7 * params.eta3) * + rDOT) - + 4 * mass * r * + ((4599 - 15737 * Nu + 36259 * params.eta2 + 108563 * params.eta3) * + params.PhiDOT3 * params.r3 - + 1j * (-34053 + 59698 * Nu + 192949 * params.eta2 + 16193 * params.eta3) * + params.PhiDOT2 * params.r2 * rDOT + + (-59058 + 77983 * Nu + 322468 * params.eta2 - 4264 * params.eta3) * + PhiDOT * r * params.rDOT2 + + 5j * (-3387 + 8518 * Nu + 8968 * params.eta2 + 884 * params.eta3) * + params.rDOT3) + + 3 * params.r2 * + (4 * (-710 + 3892 * Nu - 10655 * params.eta2 + 24000 * params.eta3) * + params.PhiDOT5 * params.r5 + + 11j * (-1484 + 11693 * Nu - 25006 * params.eta2 + 428 * params.eta3) * + params.PhiDOT4 * params.r4 * rDOT + + 4 * (4161 - 25618 * Nu + 29489 * params.eta2 + 22078 * params.eta3) * + params.PhiDOT3 * params.r3 * params.rDOT2 + + 44j * (-151 + 1067 * Nu - 2419 * params.eta2 + 57 * params.eta3) * + params.PhiDOT2 * params.r2 * params.rDOT3 + + 4 * (2041 - 11680 * Nu + 19334 * params.eta2 + 3368 * params.eta3) * + PhiDOT * r * params.rDOT4 + + 5j * (477 - 2624 * Nu + 3862 * params.eta2 + 1160 * params.eta3) * + params.rDOT5))) + / (4752.0 * np.sqrt(35) * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + (np.sqrt(0.7142857142857143) * params.Mtot3 * + (2 * mass * + (2j * (1 + delta - 2 * Nu) * S1z + + Nu * ((1 + delta) * (-6 + kappa1) + 12 * Nu) * params.S1z2 + + 8 * Nu * (-1 + 3 * Nu) * S1z * S2z + + S2z * (-2j * (-1 + delta + 2 * Nu) + + Nu * (-6 - delta * (-6 + kappa2) + kappa2 + 12 * Nu) * S2z)) + + r * (4 * params.rDOT2 * + (2j * (1 + delta - 2 * Nu) * S1z + + Nu * ((1 + delta) * (-2 + kappa1) + 4 * Nu) * params.S1z2 + + 8 * params.eta2 * S1z * S2z + + S2z * (-2j * (-1 + delta + 2 * Nu) + + Nu * (-2 - delta * (-2 + kappa2) + kappa2 + 4 * Nu) * S2z)) + + 2 * params.PhiDOT2 * params.r2 * + (14j * (1 + delta - 2 * Nu) * S1z + + (6 * (1 + delta) * kappa1 - + (26 + 23 * kappa1 + delta * (26 + 11 * kappa1)) * Nu + + 4 * (1 + 9 * kappa1) * params.eta2) * params.S1z2 - + 64 * params.eta2 * S1z * S2z + + S2z * (-14j * (-1 + delta + 2 * Nu) + + 2 * Nu * (-13 + 13 * delta + 2 * Nu) * S2z + + kappa2 * (6 + delta * (-6 + 11 * Nu) + + Nu * (-23 + 36 * Nu)) * S2z)) + + PhiDOT * r * rDOT * + (40 * (1 + delta - 2 * Nu) * S1z - + 1j * (8 * Nu * (-2 - 2 * delta + Nu) + + kappa1 * (3 + delta * (3 + 11 * Nu) + + Nu * (5 + 18 * Nu))) * params.S1z2 + + 20j * (-3 + Nu) * Nu * S1z * S2z + + S2z * (-40 * (-1 + delta + 2 * Nu) - + 1j * (8 * Nu * (-2 + 2 * delta + Nu) + + kappa2 * (3 - delta * (3 + 11 * Nu) + + Nu * (5 + 18 * Nu))) * S2z))))) + / (24.0 * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + return ( + -0.000014029180695847363 * + (params.Mtot2 * + (3 * params.r2 * + (-120 * params.eta3 * + (3565 * params.PhiDOT5 * params.r5 + + 2321j * params.PhiDOT4 * params.r4 * rDOT + + 8244 * params.PhiDOT3 * params.r3 * params.rDOT2 - + 869j * params.PhiDOT2 * params.r2 * params.rDOT3 - + 56 * PhiDOT * r * params.rDOT4 - + 120j * params.rDOT5) * + (S1z + S2z) + + 2475 * params.PhiDOT2 * params.r2 * + (6 * params.PhiDOT3 * params.r3 + + 77j * params.PhiDOT2 * params.r2 * rDOT - + 72 * PhiDOT * r * params.rDOT2 + + 6j * params.rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) - + 3 * Nu * + (22j * params.PhiDOT4 * params.r4 * rDOT * + (36322j + 5 * (2993 + 3893 * delta) * S1z + + 5 * (2993 - 3893 * delta) * S2z) - + 25j * params.rDOT5 * + ((1053 + 443 * delta) * S1z + (1053 - 443 * delta) * S2z) + + 44j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (-5444j + 5 * (1424 + 849 * delta) * S1z + + 7120 * S2z - 4245 * delta * S2z) - + 20 * PhiDOT * r * params.rDOT4 * + (-1782j + (5963 + 2969 * delta) * S1z + + 5963 * S2z - 2969 * delta * S2z) + + 4 * params.PhiDOT5 * params.r5 * + (-86889j + 10 * (2063 + 225 * delta) * S1z + + 20630 * S2z - 2250 * delta * S2z) + + 4 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (234861j + 40 * (-1824 + 97 * delta) * S1z - + 40 * (1824 + 97 * delta) * S2z)) + + 2 * params.eta2 * + (params.PhiDOT5 * params.r5 * + (-1549757j + 300 * (1448 + 1311 * delta) * S1z + + 300 * (1448 - 1311 * delta) * S2z) + + 11j * params.PhiDOT4 * params.r4 * rDOT * + (329548j + 15 * (4113 + 1411 * delta) * S1z + + 61695 * S2z - 21165 * delta * S2z) + + 22j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (-23971j + 15 * (3829 + 243 * delta) * S1z + + 57435 * S2z - 3645 * delta * S2z) + + 150j * params.rDOT5 * + ((-503 + 92 * delta) * S1z - (503 + 92 * delta) * S2z) + + 10 * PhiDOT * r * params.rDOT4 * + (4565j + 6 * (-6327 + 991 * delta) * S1z - + 6 * (6327 + 991 * delta) * S2z) + + 21 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (161403j + 10 * (-1897 + 1471 * delta) * S1z - + 10 * (1897 + 1471 * delta) * S2z))) - + 6 * mass * r * + (60 * params.eta3 * + (2417 * params.PhiDOT3 * params.r3 + + 7258j * params.PhiDOT2 * params.r2 * rDOT - + 4381 * PhiDOT * r * params.rDOT2 + + 480j * params.rDOT3) * + (S1z + S2z) - + 165 * + (1161 * params.PhiDOT3 * params.r3 - + 536j * params.PhiDOT2 * params.r2 * rDOT - + 2412 * PhiDOT * r * params.rDOT2 - + 270j * params.rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) + + 2 * Nu * + (1j * params.PhiDOT2 * params.r2 * rDOT * + (1015784j + 5 * (30849 + 88721 * delta) * S1z + + 5 * (30849 - 88721 * delta) * S2z) + + params.PhiDOT3 * params.r3 * + (-173371j + 5 * (61569 + 10789 * delta) * S1z + + 307845 * S2z - 53945 * delta * S2z) - + 5 * PhiDOT * r * params.rDOT2 * + (-115368j + (177417 + 52307 * delta) * S1z + + 177417 * S2z - 52307 * delta * S2z) + + 100j * params.rDOT3 * + ((-1545 + 181 * delta) * S1z - (1545 + 181 * delta) * S2z)) + + params.eta2 * + (20 * PhiDOT * r * params.rDOT2 * + (-11187j - 48074 * S1z + 11057 * delta * S1z - + 48074 * S2z - 11057 * delta * S2z) + + 725j * params.rDOT3 * + (-73 * S1z + 31 * delta * S1z - 73 * S2z - 31 * delta * S2z) + + params.PhiDOT3 * params.r3 * + (603141j - 543040 * S1z + 404620 * delta * S1z - + 20 * (27152 + 20231 * delta) * S2z) + + 1j * params.PhiDOT2 * params.r2 * rDOT * + (-1798104j - 648485 * S1z + 105755 * delta * S1z - + 5 * (129697 + 21151 * delta) * S2z))) + + 10 * params.Mtot2 * + (24 * params.eta3 * + (6981 * PhiDOT * r + 1600j * rDOT) * + (S1z + S2z) - + 66 * (2027 * PhiDOT * r + 380j * rDOT) * + (S1z + delta * S1z + S2z - delta * S2z) + + 30j * Nu * rDOT * + (297 * (1 + delta) * kappa1 * params.S1z3 + + 297 * (1 + delta) * kappa1 * params.S1z2 * S2z + + S1z * (17261 - 1641 * delta - + 297 * (-1 + delta) * kappa2 * params.S2z2) + + S2z * (17261 + 1641 * delta - + 297 * (-1 + delta) * kappa2 * params.S2z2)) + + 8 * Nu * PhiDOT * r * + (-7315j - + 4455 * (1 + delta) * kappa1 * params.S1z3 + + 3 * (3881 - 7757 * delta) * S2z - + 4455 * (1 + delta) * kappa1 * params.S1z2 * S2z + + 4455 * (-1 + delta) * kappa2 * params.S2z3 + + 3 * S1z * + (3881 + 7757 * delta + + 1485 * (-1 + delta) * kappa2 * params.S2z2)) + + 3 * params.eta2 * + (-5j * rDOT * + (S1z * (18793 + 223 * delta + 1188 * kappa1 * params.S1z2) + + (18793 - 223 * delta + 1188 * (-2 + kappa1) * params.S1z2) * S2z + + 1188 * (-2 + kappa2) * S1z * params.S2z2 + + 1188 * kappa2 * params.S2z3) + + 4 * PhiDOT * r * + (-4939j - 23359 * S1z - 5563 * delta * S1z + + 5940 * kappa1 * params.S1z3 + + (-23359 + 5563 * delta + + 5940 * (-2 + kappa1) * params.S1z2) * S2z + + 5940 * (-2 + kappa2) * S1z * params.S2z2 + + 5940 * kappa2 * params.S2z3))))) + / (np.sqrt(35) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_3_m_2( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> 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 + b0: float = 2.0 * mass / np.exp(0.5) + EulerGamma: float = 0.5772156649015329 + + # Pre-calculating the shared log term using np.log + common_log_term: complex = ( + -10.0 + 6.0 * EulerGamma - 3.0j * np.pi + + np.log(4096.0) + 6.0 * np.log(b0) + 9.0 * np.log(x) + ) + + if vpnorder == 5: + return ( + -0.4444444444444444j * np.sqrt(0.7142857142857143) * (-1.0 + 3.0 * Nu) * params.x3p5 * common_log_term + ) + + elif vpnorder == 6: + return ( + 0.8888888888888888j * np.sqrt(0.7142857142857143) * Nu * (S1z + S2z) * params.x4 * common_log_term + ) + + elif vpnorder == 7: + numerator = ( + -0.024691358024691357j * (193.0 - 680.0 * Nu + 230.0 * params.eta2) * params.x4p5 * common_log_term + ) + return numerator / np.sqrt(35.0) + + else: + return 0.0 + 0.0j + +def hl_3_m_2( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package + go_component: complex = hGO_3_m_2( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_2( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) + phase_factor: complex = np.exp(-2j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_3_m_min2( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package + go_component: complex = hGO_3_m_2( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_2( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + amplitude_term: complex = (go_component + qc_component).conjugate() + + # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) + phase_factor: complex = np.exp(2j * Phi) + + return pre_factor * amplitude_term * phase_factor + +# H31 + +def hGO_3_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + r0 = 2 * mass / np.exp(0.5) + + 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 + + if vpnorder == 1: + return ( + delta * (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 * Nu) * params.r2 * combination_b2 * combination_a3 + + 2 * params.Mtot2 * + (1j * (-101 + 43 * Nu) * PhiDOT * r + + (109 - 86 * Nu) * rDOT) + + 3 * mass * r * + (-4j * (-9 + 14 * Nu) * params.PhiDOT3 * params.r3 + + 6 * (2 + 9 * Nu) * params.PhiDOT2 * params.r2 * rDOT - + 1j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 + + 4 * (8 + 17 * Nu) * params.rDOT3)) + / (36.0 * np.sqrt(14) * params.r2) + ) + + elif vpnorder == 4: + return ( + 0.041666666666666664j * params.Mtot2 * + (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) - + r * (2 * params.rDOT2 * + (-6 * (1 + delta) * S1z + 5 * (5 + 3 * delta) * Nu * S1z + + 6 * S2z - 6 * delta * S2z + + 5 * (-5 + 3 * delta) * Nu * S2z) + + params.PhiDOT2 * params.r2 * + ((24 + 24 * delta - 87 * Nu + 31 * delta * Nu) * S1z + + (-24 + 24 * delta + 87 * Nu + 31 * delta * Nu) * S2z) + + 2j * PhiDOT * r * rDOT * + ((6 + 6 * delta - 31 * Nu + 35 * delta * Nu) * S1z + + (-6 + 6 * delta + 31 * Nu + 35 * delta * Nu) * S2z))) + / (np.sqrt(14) * params.r3) + ) + + elif vpnorder == 5: + term1 = ( + delta * + (-18j * (183 - 1579 * Nu + 3387 * params.eta2) * params.r3 * + combination_b3 * combination_a4 + + 2 * params.Mtot3 * + (1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r - + 12 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + + 2 * params.Mtot2 * r * + (-1j * (-8641 - 59189 * Nu + 31959 * params.eta2) * params.PhiDOT3 * params.r3 + + (-32635 - 29345 * Nu + 29541 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT - + 44j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 + + 6 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + + 3 * mass * params.r2 * + (2j * (-2479 - 4505 * Nu + 16785 * params.eta2) * params.PhiDOT5 * params.r5 + + 4 * (817 + 1220 * Nu - 7449 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT + + 6j * (-1679 + 1469 * Nu + 12233 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - + 32 * (-460 + 421 * Nu + 2514 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 + + 1j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 - + 12 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) + / (4752.0 * np.sqrt(14) * params.r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + params.Mtot3 * + (kappa1 * + (-1j * (-13 - 13 * delta + 68 * Nu + 42 * delta * Nu) * PhiDOT * r - + 34 * (1 + delta) * rDOT + 4 * (26 + 9 * delta) * Nu * rDOT) * + params.S1z2 + + S2z * (12 * delta * Nu * (7j * PhiDOT * r - 6 * rDOT) * S1z + + kappa2 * + (-1j * (13 - 13 * delta - 68 * Nu + 42 * delta * Nu) * PhiDOT * r + + 2 * (17 - 17 * delta - 52 * Nu + 18 * delta * Nu) * rDOT) * + S2z)) + / (24.0 * np.sqrt(14) * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + delta * params.Mtot2 * Nu * + (668 * params.Mtot2 - + 2 * mass * r * + (727 * params.PhiDOT2 * params.r2 - + 99j * PhiDOT * r * rDOT + 452 * params.rDOT2) + + params.r2 * + (-499 * params.PhiDOT4 * params.r4 + + 1534j * params.PhiDOT3 * params.r3 * rDOT + + 3072 * params.PhiDOT2 * params.r2 * params.rDOT2 - + 680j * PhiDOT * r * params.rDOT3 + + 1000 * params.rDOT4)) + / (180.0 * np.sqrt(14) * params.r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.0023148148148148147j * params.Mtot2 * + (2 * params.Mtot2 * + ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + + 8 * (12 + 47 * delta) * params.eta2) * S1z + + (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + + 8 * (-12 + 47 * delta) * params.eta2) * S2z) + + 3 * params.r2 * + (2 * params.rDOT4 * + ((30 + Nu * (-187 + 318 * Nu) + + delta * (30 + Nu * (-101 + 122 * Nu))) * S1z + + (-30 + (187 - 318 * Nu) * Nu + + delta * (30 + Nu * (-101 + 122 * Nu))) * S2z) + + 2 * params.PhiDOT4 * params.r4 * + ((90 - Nu * (28 + 579 * Nu) + + delta * (90 + Nu * (-800 + 551 * Nu))) * S1z + + (-90 + Nu * (28 + 579 * Nu) + + delta * (90 + Nu * (-800 + 551 * Nu))) * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + ((186 - Nu * (745 + 354 * Nu) + + delta * (186 + Nu * (-191 + 554 * Nu))) * S1z + + (-186 + Nu * (745 + 354 * Nu) + + delta * (186 + Nu * (-191 + 554 * Nu))) * S2z) + + 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + ((32 + Nu * (-451 + 230 * Nu) + + delta * (32 + Nu * (691 + 626 * Nu))) * S1z + + (-32 + (451 - 230 * Nu) * Nu + + delta * (32 + Nu * (691 + 626 * Nu))) * S2z) + + 2j * params.PhiDOT3 * params.r3 * rDOT * + ((-12 + Nu * (-341 + 315 * Nu) + + delta * (-12 + Nu * (-91 + 1213 * Nu))) * S1z + + (12 + (341 - 315 * Nu) * Nu + + delta * (-12 + Nu * (-91 + 1213 * Nu))) * S2z)) - + 2 * mass * r * + (2 * params.PhiDOT2 * params.r2 * + ((-312 * (1 + delta) + 2 * (827 - 923 * delta) * Nu + + 5 * (-201 + 131 * delta) * params.eta2) * S1z + + (-312 * (-1 + delta) - 2 * (827 + 923 * delta) * Nu + + 5 * (201 + 131 * delta) * params.eta2) * S2z) + + 2 * params.rDOT2 * + ((105 + Nu * (-541 + 924 * Nu) + + delta * (105 + Nu * (-77 + 494 * Nu))) * S1z + + (-105 + (541 - 924 * Nu) * Nu + + delta * (105 + Nu * (-77 + 494 * Nu))) * S2z) + + 1j * PhiDOT * r * rDOT * + ((1104 - 7 * Nu * (439 + 597 * Nu) + + delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S1z + + (-1104 + 7 * Nu * (439 + 597 * Nu) + + delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S2z))) + / (np.sqrt(14) * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + M_PI2 = np.pi ** 2 + + spin_terms = ( + 1513512 * params.Mtot3 * + (2 * mass * + (PhiDOT * r * + (1j * (-97 + 631 * Nu) * S1z + + 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + + 3 * kappa1 * (-39 + Nu * (149 + 4 * Nu))) * params.S1z2 + + S2z * (97j - 631j * Nu - + 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + + 3 * kappa2 * (-39 + Nu * (149 + 4 * Nu))) * S2z)) + + rDOT * + (2 * (-18 + 83 * Nu) * S1z - + 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + + kappa1 * (155 + Nu * (-373 + 164 * Nu))) * params.S1z2 + + S2z * (36 - 166 * Nu + + 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + + kappa2 * (155 + Nu * (-373 + 164 * Nu))) * S2z))) + + r * (2 * params.rDOT3 * + (S1z * (18 - 110 * Nu - + 5j * (69 * kappa1 - 214 * kappa1 * Nu + + 4 * (5 + 4 * kappa1) * params.eta2) * S1z) + + 2 * (-9 + 55 * Nu) * S2z + + 5j * (69 * kappa2 - 214 * kappa2 * Nu + + 4 * (5 + 4 * kappa2) * params.eta2) * params.S2z2) + + params.PhiDOT3 * params.r3 * + (S1z * (255j - 1403j * Nu + + 10 * (28 * (3 - 8 * Nu) * Nu + + kappa1 * (51 + 2 * Nu * (-91 + 118 * Nu))) * S1z) + + 1j * (-255 + 1403 * Nu) * S2z - + 10 * (28 * (3 - 8 * Nu) * Nu + + kappa2 * (51 + 2 * Nu * (-91 + 118 * Nu))) * params.S2z2) + + 2 * params.PhiDOT2 * params.r2 * rDOT * + (S1z * (255 - 1079 * Nu + + 5j * (2 * (60 - 361 * Nu) * Nu + + kappa1 * (6 + Nu * (-47 + 164 * Nu))) * S1z) + + (-255 + 1079 * Nu) * S2z - + 5j * (2 * (60 - 361 * Nu) * Nu + + kappa2 * (6 + Nu * (-47 + 164 * Nu))) * params.S2z2) + + PhiDOT * r * params.rDOT2 * + (4j * (-114 + 781 * Nu) * S1z + + 5 * (-213 * kappa1 - 72 * Nu + 278 * kappa1 * Nu + + 8 * (7 + 44 * kappa1) * params.eta2) * params.S1z2 + + S2z * (456j + 1065 * kappa2 * S2z + + 2 * Nu * (-1562j - 5 * (-36 + 139 * kappa2 + + 4 * (7 + 44 * kappa2) * Nu) * S2z))))) + ) + + orbital_terms = ( + delta * + (52920 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * + params.r4 * combination_b4 * combination_a5 + + 840 * params.Mtot2 * params.r2 * + ((-2555489 + 7 * Nu * (820078 + Nu * (-6623390 + 4948497 * Nu))) * + params.PhiDOT5 * params.r5 + + 1j * (3537631 + 7 * Nu * (-2817653 + Nu * (-7052042 + 4017147 * Nu))) * + params.PhiDOT4 * params.r4 * rDOT + + 3 * (-1428997 + 7 * Nu * (-1230747 + Nu * (-237418 + 4061717 * Nu))) * + params.PhiDOT3 * params.r3 * params.rDOT2 + + 1j * (-5153011 + 7 * Nu * (-2375327 + 9 * Nu * (218846 + 1640185 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT3 + + (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * + PhiDOT * r * params.rDOT4 + + 3j * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * + params.rDOT5) - + 8820 * mass * params.r3 * + (2 * (111737 + Nu * (-366573 + Nu * (-618923 + 2278593 * Nu))) * + params.PhiDOT7 * params.r7 + + 2j * (101844 + Nu * (-273675 - 871630 * Nu + 2069774 * params.eta2)) * + params.PhiDOT6 * params.r6 * rDOT + + 2 * (341322 + Nu * (-1429938 + Nu * (-1206083 + 7690681 * Nu))) * + params.PhiDOT5 * params.r5 * params.rDOT2 + + 8j * (90241 + 2 * Nu * (-206022 + Nu * (-62113 + 1003558 * Nu))) * + params.PhiDOT4 * params.r4 * params.rDOT3 + + 2 * (410547 + Nu * (-2269686 + Nu * (762091 + 8400052 * Nu))) * + params.PhiDOT3 * params.r3 * params.rDOT4 + + 4j * (217935 + 2 * Nu * (-573699 + 5 * Nu * (18671 + 445748 * Nu))) * + params.PhiDOT2 * params.r2 * params.rDOT5 + + (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * + PhiDOT * r * params.rDOT6 + + 24j * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * + params.rDOT7) + + 2 * params.Mtot4 * + (-4178597424j * rDOT + + 84j * rDOT * + (38468500 * params.eta3 + + 648648j * (S1z + S2z) - + 90090 * ((-24 + 155 * kappa1) * params.S1z2 + + (-24 + 155 * kappa2) * params.S2z2) - + 420 * params.eta2 * + (-122855 + + 3003 * ((2 + 11 * kappa1) * params.S1z2 - + 18 * S1z * S2z + + (2 + 11 * kappa2) * params.S2z2)) + + 3 * Nu * + (-103100846 - 1846845 * M_PI2 - + 564564j * S2z + + 6006 * (S1z * (-94j + 5 * (-52 + 63 * kappa1) * S1z) - + 20 * S1z * S2z + + 5 * (-52 + 63 * kappa2) * params.S2z2))) + + PhiDOT * r * + (1176172480 * params.eta3 + + 8 * (74084729 - + 189189 * S1z * (97j + 5 * (-8 + 117 * kappa1) * S1z) - + 189189 * S2z * (97j + 5 * (-8 + 117 * kappa2) * S2z)) - + 176400 * params.eta2 * + (11251 + + 429 * ((2 + 13 * kappa1) * params.S1z2 - + 22 * S1z * S2z + + (2 + 13 * kappa2) * params.S2z2)) + + 147 * Nu * + (-65012788 + 4485195 * M_PI2 + + 4499352j * S2z + + 10296 * (S1z * (437j + 15 * (-32 + 71 * kappa1) * S1z) - + 3860 * S1z * S2z + + 15 * (-32 + 71 * kappa2) * params.S2z2)))) - + 3 * params.Mtot3 * r * + (-4j * params.rDOT3 * + (601018232 - 1359334480 * params.eta3 - + 756756 * S1z * (6j + 115 * kappa1 * S1z) - + 756756 * S2z * (6j + 115 * kappa2 * S2z) + + 231 * Nu * + (8490448 + 503685 * M_PI2 + + 80808j * S2z + + 2184 * (S1z * (37j + 190 * kappa1 * S1z) + + 70 * S1z * S2z + + 190 * kappa2 * params.S2z2)) + + 58800 * params.eta2 * + (-62596 + + 429 * ((-1 + 5 * kappa1) * params.S1z2 - + 12 * S1z * S2z + + (-1 + 5 * kappa2) * params.S2z2))) - + 14j * params.PhiDOT2 * params.r2 * rDOT * + (-229522160 * params.eta3 + + 8 * (48303859 + + 135135 * S1z * (-17j + 2 * kappa1 * S1z) + + 135135 * S2z * (-17j + 2 * kappa2 * S2z)) + + 2520 * params.eta2 * + (100913 + + 286 * ((-31 + 5 * kappa1) * params.S1z2 - + 72 * S1z * S2z + + (-31 + 5 * kappa2) * params.S2z2)) + + 7 * Nu * + (125038052 + 2374515 * M_PI2 + + 5858424j * S2z - + 10296 * (S1z * (-569j + 25 * (-24 + 7 * kappa1) * S1z) + + 700 * S1z * S2z + + 25 * (-24 + 7 * kappa2) * params.S2z2))) + + 4 * PhiDOT * r * params.rDOT2 * + (-1095987374 + 1035895280 * params.eta3 + + 378378 * S1z * (152j + 355 * kappa1 * S1z) + + 378378 * S2z * (152j + 355 * kappa2 * S2z) - + 490 * params.eta2 * + (-5802767 + + 5148 * ((2 + 23 * kappa1) * params.S1z2 - + 42 * S1z * S2z + + (2 + 23 * kappa2) * params.S2z2)) - + 77 * Nu * + (42451610 + 1511055 * M_PI2 + + 3623256j * S2z - + 6552 * (S1z * (-553j + 5 * (18 + 37 * kappa1) * S1z) + + 965 * S1z * S2z + + 5 * (18 + 37 * kappa2) * params.S2z2))) + + 7 * params.PhiDOT3 * params.r3 * + (512893080 * params.eta3 - + 136 * (-2089567 + + 135135 * S1z * (1j + 2 * kappa1 * S1z) + + 135135 * S2z * (1j + 2 * kappa2 * S2z)) - + 560 * params.eta2 * + (2457671 + + 2574 * ((11 + 53 * kappa1) * params.S1z2 - + 84 * S1z * S2z + + (11 + 53 * kappa2) * params.S2z2)) + + 3 * Nu * + (16621605 * M_PI2 + + 8 * (27468722 + + 2681679j * S2z + + 3003 * (S1z * (893j - 840 * S1z + 800 * kappa1 * S1z) - + 3160 * S1z * S2z + + 40 * (-21 + 20 * kappa2) * params.S2z2)))))) + ) + + log_term = ( + 74954880 * delta * params.Mtot3 * + (mass * (-22j * PhiDOT * r - 24 * rDOT) + + 3 * r * + (7j * params.PhiDOT3 * params.r3 + + 14 * params.PhiDOT2 * params.r2 * rDOT - + 8j * PhiDOT * r * params.rDOT2 + + 4 * params.rDOT3)) * + np.log(r / r0) + ) + + return ( + 1j * (spin_terms + orbital_terms + log_term) + / (3.6324288e8 * np.sqrt(14) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_3_m_1( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + + delta: float = np.sqrt(1.0 - 4.0 * Nu) + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + r0: float = b0 + + if vpnorder == 4: + return ( + -0.005555555555555556 * ( + delta * params.x3 * ( + -97.0 + 60.0 * EulerGamma - 30.0j * np.pi + + 60.0 * np.log(2.0) + 60.0 * np.log(b0) + 90.0 * np.log(x) + ) + ) / np.sqrt(14.0) + ) + + elif vpnorder == 6: + return ( + (delta * params.x4 * ( + -1552.0 + 960.0 * EulerGamma - 6487.0 * Nu + 420.0 * EulerGamma * Nu - + 480.0j * np.pi - 210.0j * Nu * np.pi + 960.0 * np.log(2.0) + + 420.0 * Nu * np.log(2.0) + 60.0 * (16.0 + 7.0 * Nu) * np.log(b0) + + 90.0 * (16.0 + 7.0 * Nu) * np.log(x) + )) / (1080.0 * np.sqrt(14.0)) + ) + + elif vpnorder == 7: + numerator_7 = ( + -9.44822373393802e-6 * ( + params.x4p5 * ( + 53132.0j * delta - 92232.0j * delta * EulerGamma + 35280.0j * delta * EulerGamma**2 - + 46116.0 * delta * np.pi + 35280.0 * delta * EulerGamma * np.pi - 2940.0j * delta * np.pi**2 + + 57036.0 * S1z + 57036.0 * delta * S1z - 35280.0 * EulerGamma * S1z - 35280.0 * delta * EulerGamma * S1z - + 114513.0 * Nu * S1z - 143031.0 * delta * Nu * S1z + 97020.0 * EulerGamma * Nu * S1z + + 114660.0 * delta * EulerGamma * Nu * S1z + 17640.0j * np.pi * S1z + 17640.0j * delta * np.pi * S1z - + 48510.0j * Nu * np.pi * S1z - 57330.0j * delta * Nu * np.pi * S1z - 57036.0 * S2z + + 57036.0 * delta * S2z + 35280.0 * EulerGamma * S2z - 35280.0 * delta * EulerGamma * S2z + + 114513.0 * Nu * S2z - 143031.0 * delta * Nu * S2z - 97020.0 * EulerGamma * Nu * S2z + + 114660.0 * delta * EulerGamma * Nu * S2z - 17640.0j * np.pi * S2z + 17640.0j * delta * np.pi * S2z + + 48510.0j * Nu * np.pi * S2z - 57330.0j * delta * Nu * np.pi * S2z - 92232.0j * delta * np.log(2.0) + + 70560.0j * delta * EulerGamma * np.log(2.0) + 35280.0 * delta * np.pi * np.log(2.0) - + 35280.0 * S1z * np.log(2.0) - 35280.0 * delta * S1z * np.log(2.0) + 97020.0 * Nu * S1z * np.log(2.0) + + 114660.0 * delta * Nu * S1z * np.log(2.0) + 35280.0 * S2z * np.log(2.0) - 35280.0 * delta * S2z * np.log(2.0) - + 97020.0 * Nu * S2z * np.log(2.0) + 114660.0 * delta * Nu * S2z * np.log(2.0) + + 35280.0j * delta * np.log(2.0)**2 - 2490264.0j * delta * np.log(3.0) + + 1905120.0j * delta * EulerGamma * np.log(3.0) + 952560.0 * delta * np.pi * np.log(3.0) - + 317520.0 * S1z * np.log(3.0) - 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + + 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - 317520.0 * delta * S2z * np.log(3.0) - + 1508220.0 * Nu * S2z * np.log(3.0) + 396900.0 * delta * Nu * S2z * np.log(3.0) + + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + 952560.0j * delta * np.log(3.0)**2 + + 35280.0j * delta * np.log(b0)**2 + 21840.0j * delta * np.log(r0) - 138348.0j * delta * np.log(x) + + 105840.0j * delta * EulerGamma * np.log(x) + 52920.0 * delta * np.pi * np.log(x) - 52920.0 * S1z * np.log(x) - + 52920.0 * delta * S1z * np.log(x) + 145530.0 * Nu * S1z * np.log(x) + 171990.0 * delta * Nu * S1z * np.log(x) + + 52920.0 * S2z * np.log(x) - 52920.0 * delta * S2z * np.log(x) - 145530.0 * Nu * S2z * np.log(x) + + 171990.0 * delta * Nu * S2z * np.log(x) + 105840.0j * delta * np.log(2.0) * np.log(x) + + 79380.0j * delta * np.log(x)**2 + + 588.0 * np.log(b0) * ( + -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * np.pi + + 15.0 * (-4.0 - 4.0 * delta + 11.0 * Nu + 13.0 * delta * Nu) * S1z + + 60.0 * S2z - 60.0 * delta * S2z - 165.0 * Nu * S2z + 195.0 * delta * Nu * S2z + + 120.0j * delta * np.log(2.0) + 180.0j * delta * np.log(x) + ) + ) + ) + ) + return numerator_7 / np.sqrt(14.0) + + else: + return 0.0 + 0.0j + +def hl_3_m_1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + # Maintaining the logic of the C-source error check + raise ValueError( + "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module + go_component: complex = hGO_3_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -1 * Phi) -> exp(-i * Phi) + phase_factor: complex = np.exp(-1j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_3_m_min1( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + # Maintaining the logic of the C-source error check + raise ValueError( + "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module + go_component: complex = hGO_3_m_1( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params + ) + qc_component: complex = hQC_3_m_1( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + amplitude_factor = (go_component + qc_component).conjugate() + + # cpolar(1, 1 * Phi) -> exp(i * Phi) + phase_factor: complex = np.exp(1j * Phi) + + return pre_factor * amplitude_factor * phase_factor + +# H44 + +def hGO_4_m_4( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt((1 - 4 * Nu) + 0j) + 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 + + if vpnorder == 2: + return ( + np.sqrt(0.7142857142857143) * (-1 + 3 * Nu) * + (7 * params.Mtot2 + 6 * params.r2 * combination_a4 + + 3 * mass * r * + (17 * params.PhiDOT2 * params.r2 + + 18j * PhiDOT * r * rDOT - 6 * params.rDOT2)) + / (36.0 * params.r2) + ) + + elif vpnorder == 4: + return ( + (40 * params.Mtot3 * (314 - 987 * Nu + 195 * params.eta2) - + 60 * (23 - 159 * Nu + 291 * params.eta2) * params.r3 * + combination_b * combination_a5 + + params.Mtot2 * r * + ((53143 - 199660 * Nu + 127500 * params.eta2) * params.PhiDOT2 * params.r2 + + 24j * (967 - 4615 * Nu + 5935 * params.eta2) * PhiDOT * r * rDOT - + 10 * (290 - 2033 * Nu + 4365 * params.eta2) * params.rDOT2) - + 3 * mass * params.r2 * + ((613 - 920 * Nu + 6420 * params.eta2) * params.PhiDOT4 * params.r4 - + 8j * (-976 + 1745 * Nu + 3150 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + + 2 * (-6141 + 8980 * Nu + 31500 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 4j * (-1853 + 1730 * Nu + 13230 * params.eta2) * PhiDOT * r * params.rDOT3 - + 20 * (-83 + 30 * Nu + 762 * params.eta2) * params.rDOT4)) + / (1584.0 * np.sqrt(35) * params.r3) + ) + + elif vpnorder == 5: + term1 = ( + params.Mtot2 * Nu * + (6 * mass * (-43j * PhiDOT * r + 9 * rDOT) + + r * (-734j * params.PhiDOT3 * params.r3 + + 129 * params.PhiDOT2 * params.r2 * rDOT + + 156j * PhiDOT * r * params.rDOT2 - + 26 * params.rDOT3)) + / (24.0 * np.sqrt(35) * params.r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + params.Mtot2 * + (-3j * params.PhiDOT2 * params.r3 * rDOT * + ((-250 + 1221 * Nu - 1512 * params.eta2 + + delta * (-250 + 849 * Nu)) * S1z + + (-250 + delta * (250 - 849 * Nu) + 1221 * Nu - + 1512 * params.eta2) * S2z) - + 2 * mass * PhiDOT * r * + ((-130 + 757 * Nu - 1224 * params.eta2 + + delta * (-130 + 513 * Nu)) * S1z + + (-130 + delta * (130 - 513 * Nu) + 757 * Nu - + 1224 * params.eta2) * S2z) - + 2j * mass * rDOT * + ((-100 + 577 * Nu - 864 * params.eta2 + + delta * (-100 + 333 * Nu)) * S1z + + (-100 + delta * (100 - 333 * Nu) + 577 * Nu - + 864 * params.eta2) * S2z) - + 6 * params.PhiDOT3 * params.r4 * + ((-65 + 263 * Nu - 291 * params.eta2 + + delta * (-65 + 282 * Nu)) * S1z + + (-65 + delta * (65 - 282 * Nu) + 263 * Nu - + 291 * params.eta2) * S2z) + + 12 * PhiDOT * params.r2 * params.rDOT2 * + ((-40 + 201 * Nu - 252 * params.eta2 + + delta * (-40 + 129 * Nu)) * S1z + + (-40 + delta * (40 - 129 * Nu) + 201 * Nu - + 252 * params.eta2) * S2z) + + 6j * r * params.rDOT3 * + ((-20 + 107 * Nu - 144 * params.eta2 + + delta * (-20 + 63 * Nu)) * S1z + + (-20 + delta * (20 - 63 * Nu) + 107 * Nu - + 144 * params.eta2) * S2z)) + / (72.0 * np.sqrt(35) * params.r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + (10 * params.Mtot4 * + (-4477296 + 12734393 * Nu - 6895 * params.eta2 + 1043805 * params.eta3) + + 3150 * (-367 + 4337 * Nu - 17462 * params.eta2 + 23577 * params.eta3) * + params.r4 * combination_b2 * combination_a6 + + 2 * params.Mtot3 * r * + ((-36967579 + 245501977 * Nu - 459916170 * params.eta2 + + 150200680 * params.eta3) * params.PhiDOT2 * params.r2 + + 4j * (7571073 - 10780154 * Nu - 56898800 * params.eta2 + + 43665510 * params.eta3) * PhiDOT * r * rDOT - + 10 * (1283609 - 5800627 * Nu + 3725295 * params.eta2 + + 4771935 * params.eta3) * params.rDOT2) - + params.Mtot2 * params.r2 * + ((-28258134 + 3245207 * Nu + 144051250 * params.eta2 + + 136991820 * params.eta3) * params.PhiDOT4 * params.r4 - + 24j * (2371982 - 7733376 * Nu - 7948185 * params.eta2 + + 9074870 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + + 7 * (6557973 - 50558069 * Nu + 59901380 * params.eta2 + + 104752320 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 168j * (52044 - 1084807 * Nu + 1849450 * params.eta2 + + 4171730 * params.eta3) * PhiDOT * r * params.rDOT3 - + 35 * (1083 - 1246819 * Nu + 2524240 * params.eta2 + + 5995845 * params.eta3) * params.rDOT4) - + 105 * mass * params.r3 * + ((116396 - 551405 * Nu + 560658 * params.eta2 + + 293036 * params.eta3) * params.PhiDOT6 * params.r6 + + 2j * (158192 - 670661 * Nu + 177718 * params.eta2 + + 2163976 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + + (-393665 + 1322392 * Nu + 1589680 * params.eta2 - + 8622660 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 - + 8j * (-23048 + 209397 * Nu - 487057 * params.eta2 + + 260396 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 - + (630647 - 3391000 * Nu + 2501958 * params.eta2 + + 7664096 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - + 2j * (218975 - 1037408 * Nu + 148970 * params.eta2 + + 3699480 * params.eta3) * PhiDOT * r * params.rDOT5 + + 10 * (10233 - 44864 * Nu - 13050 * params.eta2 + + 203280 * params.eta3) * params.rDOT6)) + / (1.44144e6 * np.sqrt(35) * params.r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + np.sqrt(0.7142857142857143) * params.Mtot3 * (-1 + 3 * Nu) * + (12 * mass + + r * (53 * params.PhiDOT2 * params.r2 + + 26j * PhiDOT * r * rDOT - 8 * params.rDOT2)) * + (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + + S2z * (4 * Nu * S1z + kappa2 * S2z - delta * kappa2 * S2z - + 2 * kappa2 * Nu * S2z)) + / (48.0 * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + # Henry et al. ecc+spin terms + return ( + params.Mtot2 * + (14 * params.Mtot2 * + (120 * params.eta3 * + (24635 * PhiDOT * r + 18657j * rDOT) * + (S1z + S2z) - + 60 * (10039 * PhiDOT * r + 7706j * rDOT) * + (S1z + delta * S1z + S2z - delta * S2z) + + 5 * Nu * + (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 * params.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 * params.r2 * + (1680 * params.eta3 * + (2833 * params.PhiDOT5 * params.r5 + + 18796j * params.PhiDOT4 * params.r4 * rDOT - + 13185 * params.PhiDOT3 * params.r3 * params.rDOT2 + + 1186j * params.PhiDOT2 * params.r2 * params.rDOT3 - + 5863 * PhiDOT * r * params.rDOT4 - + 2100j * params.rDOT5) * + (S1z + S2z) - + 1050 * + (454 * params.PhiDOT5 * params.r5 + + 1195j * params.PhiDOT4 * params.r4 * rDOT - + 1950 * params.PhiDOT3 * params.r3 * params.rDOT2 - + 442j * params.PhiDOT2 * params.r2 * params.rDOT3 - + 384 * PhiDOT * r * params.rDOT4 - + 184j * params.rDOT5) * + (S1z + delta * S1z + S2z - delta * S2z) - + 6 * params.eta2 * + (2 * params.PhiDOT5 * params.r5 * + (-2459811j + 35 * (26517 + 10223 * delta) * S1z + + (928095 - 357805 * delta) * S2z) - + 10j * params.rDOT5 * + (35291j + 28 * (2183 + 1155 * delta) * S1z + + (61124 - 32340 * delta) * S2z) + + 1j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (917901j + 1120 * (-191 + 1616 * delta) * S1z - + 1120 * (191 + 1616 * delta) * S2z) + + 5 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (85426j + 7 * (-148363 + 4365 * delta) * S1z - + 7 * (148363 + 4365 * delta) * S2z) - + 4 * PhiDOT * r * params.rDOT4 * + (280067j + 70 * (5844 + 4817 * delta) * S1z - + 70 * (-5844 + 4817 * delta) * S2z) + + 1j * params.PhiDOT4 * params.r4 * rDOT * + (10375501j + 70 * (65831 + 22871 * delta) * S1z - + 70 * (-65831 + 22871 * delta) * S2z)) + + Nu * (-40j * params.rDOT5 * + (12203j + 42 * (843 + 656 * delta) * S1z - + 42 * (-843 + 656 * delta) * S2z) + + 4j * params.PhiDOT2 * params.r2 * params.rDOT3 * + (-266071j + 210 * (-2279 + 1010 * delta) * S1z - + 210 * (2279 + 1010 * delta) * S2z) - + 16 * PhiDOT * r * params.rDOT4 * + (58753j + 105 * (2030 + 1947 * delta) * S1z - + 105 * (-2030 + 1947 * delta) * S2z) + + params.PhiDOT5 * params.r5 * + (-8997592j + 105 * (39959 + 15835 * delta) * S1z - + 105 * (-39959 + 15835 * delta) * S2z) - + 10 * params.PhiDOT3 * params.r3 * params.rDOT2 * + (254228j + 21 * (65293 + 34551 * delta) * S1z - + 21 * (-65293 + 34551 * delta) * S2z) + + 2j * params.PhiDOT4 * params.r4 * rDOT * + (12351083j + 105 * (43193 + 37913 * delta) * S1z - + 105 * (-43193 + 37913 * delta) * S2z))) + + mass * r * + (2520 * params.eta3 * + (8036 * params.PhiDOT3 * params.r3 + + 41814j * params.PhiDOT2 * params.r2 * rDOT - + 30537 * PhiDOT * r * params.rDOT2 - + 9064j * params.rDOT3) * + (S1z + S2z) - + 210 * + (11849 * params.PhiDOT3 * params.r3 + + 31868j * params.PhiDOT2 * params.r2 * rDOT + + 3572 * PhiDOT * r * params.rDOT2 + + 1508j * params.rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) - + 12 * params.eta2 * + (1j * params.PhiDOT2 * params.r2 * rDOT * + (-1150397j + 35 * (154765 + 157449 * delta) * S1z + + 5416775 * S2z - 5510715 * delta * S2z) - + PhiDOT * r * params.rDOT2 * + (2306552j + 35 * (4931 + 110733 * delta) * S1z + + 172585 * S2z - 3875655 * delta * S2z) + + params.PhiDOT3 * params.r3 * + (12461121j + 35 * (18331 + 87381 * delta) * S1z + + 641585 * S2z - 3058335 * delta * S2z) - + 25j * params.rDOT3 * + (36676j + 35 * (137 + 1053 * delta) * S1z + + 4795 * S2z - 36855 * delta * S2z)) + + Nu * (-200j * params.rDOT3 * + (-2501j + 42 * (-308 + 51 * delta) * S1z - + 42 * (308 + 51 * delta) * S2z) + + 2 * PhiDOT * r * params.rDOT2 * + (-1951984j - 105 * (-37907 + 14661 * delta) * S1z + + 105 * (37907 + 14661 * delta) * S2z) + + 8j * params.PhiDOT2 * params.r2 * rDOT * + (-10005028j + 105 * (40991 + 31689 * delta) * S1z - + 105 * (-40991 + 31689 * delta) * S2z) + + params.PhiDOT3 * params.r3 * + (88418488j + 105 * (57793 + 266391 * delta) * S1z - + 105 * (-57793 + 266391 * delta) * S2z)))) + / (332640.0 * np.sqrt(35) * params.r4) + ) + + else: + return 0.0 + 0.0j + +def hQC_4_m_4( + mass: float, + Nu: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: KeplerVars + ) -> complex: + EulerGamma: float = 0.5772156649015329 + b0: float = 2.0 * mass / np.exp(0.5) + + if vpnorder == 5: + # Pre-factor: Complex(0, 0.07407407407407407) / sqrt(35) + return ( + (0.07407407407407407j * params.x3p5 * ( + 1888.0 - 960.0 * EulerGamma - 5661.0 * Nu + 2880.0 * EulerGamma * Nu + + 480.0j * np.pi - 1440.0j * Nu * np.pi - 2880.0 * np.log(2.0) + + 8640.0 * Nu * np.log(2.0) + 960.0 * (-1.0 + 3.0 * Nu) * np.log(b0) + + 1440.0 * (-1.0 + 3.0 * Nu) * np.log(x) + )) / np.sqrt(35.0) + ) + + elif vpnorder == 7: + # Pre-factor: Complex(0, 1.0020843354176687e-6) / sqrt(35) + return ( + (1.0020843354176687e-6j * params.x4p5 * ( + -752360448.0 + 382556160.0 * EulerGamma + 2620364605.0 * Nu - + 1333248000.0 * EulerGamma * Nu - 898312500.0 * params.eta2 + + 458035200.0 * EulerGamma * params.eta2 - 191278080.0j * np.pi + + 666624000.0j * Nu * np.pi - 229017600.0j * params.eta2 * np.pi + + 1147668480.0 * np.log(2.0) - 3999744000.0 * Nu * np.log(2.0) + + 1374105600.0 * params.eta2 * np.log(2.0) + + 215040.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(b0) + + 322560.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(x) + )) / np.sqrt(35.0) + ) + + else: + return 0.0 + 0.0j + +def hl_4_m_4( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure + go_component: complex = hGO_4_m_4( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params + ) + qc_component: complex = hQC_4_m_4( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + # cpolar(1, -4 * Phi) -> exp(-i * 4 * Phi) + phase_factor: complex = np.exp(-4j * Phi) + + return pre_factor * (go_component + qc_component) * phase_factor + +def hl_4_m_min4( + mass: float, + Nu: float, + r: float, + rDOT: float, + Phi: float, + PhiDOT: float, + R: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars + ) -> complex: + + if vpnorder > 8: + raise ValueError( + "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." + ) + + # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R + pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R + + # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components + # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure + go_component: complex = hGO_4_m_4( + mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params + ) + qc_component: complex = hQC_4_m_4( + mass, Nu, vpnorder, x, S1z, S2z, params + ) + + amplitude_factor = (go_component + qc_component).conjugate() + + # cpolar(1, 4 * Phi) -> exp(i * 4 * Phi) + phase_factor: complex = np.exp(4j * Phi) + + return pre_factor * amplitude_factor * phase_factor + +# H43 + +def hGO_4_m_3( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + + if vpnorder == 3: + return ( + 0.16666666666666666j * delta * mass * (-1 + 2 * Nu) * PhiDOT * + (4 * mass + + r * (23 * params.PhiDOT2 * params.r2 + + 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) + / (np.sqrt(70) * r) + ) + + elif vpnorder == 4: + return ( + -0.041666666666666664j * np.sqrt(0.35714285714285715) * + params.Mtot2 * Nu * + (4 * mass + + r * (23 * params.PhiDOT2 * params.r2 + + 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) * + ((-1 + delta) * S1z + S2z + delta * S2z) + / params.r3 + ) + + elif vpnorder == 5: + return ( + 0.0012626262626262627j * delta * mass * PhiDOT * + (2 * params.Mtot2 * (972 - 2293 * Nu + 1398 * params.eta2) + + 2 * mass * r * + ((1788 - 9077 * Nu + 13416 * params.eta2) * params.PhiDOT2 * params.r2 + + 3j * (-2796 + 5299 * Nu + 1622 * params.eta2) * PhiDOT * r * rDOT - + 2 * (-1200 + 2545 * Nu + 162 * params.eta2) * params.rDOT2) - + 3 * params.r2 * + ((-524 - 489 * Nu + 6392 * params.eta2) * params.PhiDOT4 * params.r4 + + 4j * (796 - 1864 * Nu + 133 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + + 42 * (-51 + 94 * Nu + 56 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 4j * (-229 + 366 * Nu + 358 * params.eta2) * PhiDOT * r * params.rDOT3 - + 4 * (-43 + 62 * Nu + 80 * params.eta2) * params.rDOT4)) + / (np.sqrt(70) * params.r2) + ) + + elif vpnorder == 6: + term1 = ( + delta * params.Mtot2 * Nu * PhiDOT * + (6 * mass * (181 * PhiDOT * r - 89j * rDOT) + + r * (4847 * params.PhiDOT3 * params.r3 - + 7338j * params.PhiDOT2 * params.r2 * rDOT - + 408 * PhiDOT * r * params.rDOT2 + + 112j * params.rDOT3)) + / (180.0 * np.sqrt(70) * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + -0.0006313131313131314j * params.Mtot2 * + (2 * params.Mtot2 * + ((-440 + 6801 * Nu - 1428 * params.eta2 + + delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S1z + + (440 - 6801 * Nu + 1428 * params.eta2 + + delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S2z) - + 2 * mass * r * + (-3j * PhiDOT * r * rDOT * + (delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S1z - + 5 * (264 - 311 * Nu + 823 * params.eta2) * S1z + + delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S2z + + 5 * (264 - 311 * Nu + 823 * params.eta2) * S2z) - + 2 * params.rDOT2 * + ((220 + 1659 * Nu - 1512 * params.eta2 + + delta * (220 - 3067 * Nu + 240 * params.eta2)) * S1z + + (-220 - 1659 * Nu + 1512 * params.eta2 + + delta * (220 - 3067 * Nu + 240 * params.eta2)) * S2z) + + 2 * params.PhiDOT2 * params.r2 * + ((1826 - 19530 * Nu + 20145 * params.eta2 + + delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S1z + + (-1826 + 19530 * Nu - 20145 * params.eta2 + + delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S2z)) - + 3 * params.r2 * + (3080j * params.PhiDOT3 * params.r3 * rDOT * + ((1 + delta) * S1z + (-1 + delta) * S2z) + + 2 * params.eta2 * + (129 * params.PhiDOT2 * params.r2 * params.rDOT2 * + (41 * S1z + 23 * delta * S1z - 41 * S2z + 23 * delta * S2z) - + 2 * params.rDOT4 * + (149 * S1z + 75 * delta * S1z - 149 * S2z + 75 * delta * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + (925 * S1z + 491 * delta * S1z - 925 * S2z + 491 * delta * S2z) - + 1j * params.PhiDOT3 * params.r3 * rDOT * + (-2105 * S1z + 753 * delta * S1z + 2105 * S2z + 753 * delta * S2z) + + params.PhiDOT4 * params.r4 * + (11617 * S1z + 4847 * delta * S1z - 11617 * S2z + 4847 * delta * S2z)) + + Nu * (16 * params.PhiDOT4 * params.r4 * + (-413 * S1z + 127 * delta * S1z + 413 * S2z + 127 * delta * S2z) - + 2 * params.rDOT4 * + (-301 * S1z + 213 * delta * S1z + 301 * S2z + 213 * delta * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + (-1625 * S1z + 1009 * delta * S1z + 1625 * S2z + 1009 * delta * S2z) + + 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + (-2587 * S1z + 1267 * delta * S1z + 2587 * S2z + 1267 * delta * S2z) - + 2j * params.PhiDOT3 * params.r3 * rDOT * + (3635 * S1z + 7981 * delta * S1z - 3635 * S2z + 7981 * delta * S2z)))) + / (np.sqrt(70) * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + # Henry et al. ecc+spin terms + spin_terms = ( + 5005 * params.Mtot2 * + (-24 * PhiDOT * params.r2 * params.rDOT2 * + (1j * (-11 + 48 * Nu) * S1z + + 6 * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + + S2z * (11j - 48j * Nu - + 6 * (-5 + 4 * kappa2) * params.eta2 * S2z)) + + 4 * r * params.rDOT3 * + ((-11 + 93 * Nu) * S1z - + 6j * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + + S2z * (11 - 93 * Nu + + 6j * (-5 + 4 * kappa2) * params.eta2 * S2z)) + + 4 * mass * rDOT * + ((22 - 111 * Nu) * S1z + + 6j * (15 + 8 * kappa1) * params.eta2 * params.S1z2 + + S2z * (-22 + 111 * Nu - + 6j * (15 + 8 * kappa2) * params.eta2 * S2z)) + + 6j * params.PhiDOT2 * params.r3 * rDOT * + (1j * (-121 + 963 * Nu) * S1z + + 6 * (-55 * params.eta2 + + kappa1 * (-5 + 30 * Nu + 4 * params.eta2)) * params.S1z2 + + S2z * (121j + 30 * kappa2 * S2z - + 6 * (-55 + 4 * kappa2) * params.eta2 * S2z - + 9 * Nu * (107j + 20 * kappa2 * S2z))) + + 2 * mass * PhiDOT * r * + (-1j * (-121 + 633 * Nu) * S1z + + 6 * (180 * params.eta2 + + kappa1 * (9 - 54 * Nu + 28 * params.eta2)) * params.S1z2 - + S2z * (121j + 54 * kappa2 * S2z + + 24 * (45 + 7 * kappa2) * params.eta2 * S2z - + 3 * Nu * (211j + 108 * kappa2 * S2z))) + + params.PhiDOT3 * params.r4 * + (-1j * (-649 + 4767 * Nu) * S1z + + 6 * (820 * params.eta2 + + kappa1 * (75 - 450 * Nu + 364 * params.eta2)) * params.S1z2 - + S2z * (649j + 450 * kappa2 * S2z + + 24 * (205 + 91 * kappa2) * params.eta2 * S2z - + 3 * Nu * (1589j + 900 * kappa2 * S2z)))) + ) + + orbital_terms = ( + delta * + (2 * mass * PhiDOT * params.r3 * + (10 * (234744 - 1010534 * Nu + 1024443 * params.eta2 + + 5451096 * params.eta3) * params.PhiDOT4 * params.r4 - + 3j * (-2426804 + 1512854 * Nu + 4994115 * params.eta2 + + 610960 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + + (-30341028 + 23936528 * Nu + 89326545 * params.eta2 + + 19329660 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 21j * (-668008 + 803028 * Nu + 1908955 * params.eta2 + + 540370 * params.eta3) * PhiDOT * r * params.rDOT3 - + 14 * (-172143 + 155683 * Nu + 680580 * params.eta2 + + 111840 * params.eta3) * params.rDOT4) - + 105 * PhiDOT * params.r4 * + ((-8280 + 24681 * Nu - 151973 * params.eta2 + + 624074 * params.eta3) * params.PhiDOT6 * params.r6 + + 2j * (-32208 + 248485 * Nu - 524074 * params.eta2 + + 24546 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + + 2 * (48924 - 239802 * Nu + 137447 * params.eta2 + + 358156 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + + 4j * (174 + 24488 * Nu - 102039 * params.eta2 + + 44882 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + + 3 * (10455 - 56490 * Nu + 84504 * params.eta2 + + 54016 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 + + 2j * (11175 - 52698 * Nu + 57436 * params.eta2 + + 60808 * params.eta3) * PhiDOT * r * params.rDOT5 - + 6 * (829 - 3726 * Nu + 3480 * params.eta2 + + 4640 * params.eta3) * params.rDOT6) + + params.Mtot2 * r * + (20020 * params.rDOT3 * (S1z + S2z) * + (-11 + 71 * Nu + 30j * params.eta2 * (S1z + S2z)) - + 52 * PhiDOT * r * params.rDOT2 * + (129150 * params.eta3 + + 7 * Nu * (31961 + 8580j * S1z + 8580j * S2z) - + 3j * (32671j + 8470 * S1z + 8470 * S2z) - + 35 * params.eta2 * + (33313 + 1980 * params.S1z2 + 3960 * S1z * S2z + 1980 * params.S2z2)) + + params.PhiDOT3 * params.r3 * + (-10566168 - 70869960 * params.eta3 + + 3248245j * S1z + 2252250 * kappa1 * params.S1z2 + + 3248245j * S2z + 2252250 * kappa2 * params.S2z2 - + 7 * Nu * + (4818166 + 2480335j * S1z + 1287000 * kappa1 * params.S1z2 + + 2480335j * S2z + 1287000 * kappa2 * params.S2z2) + + 3850 * params.eta2 * + (38873 + 78 * (7 + 30 * kappa1) * params.S1z2 - + 3588 * S1z * S2z + + 78 * (7 + 30 * kappa2) * params.S2z2)) - + 2j * params.PhiDOT2 * params.r2 * rDOT * + (6531280 * params.eta3 + + 3 * (5382288 + 605605j * S1z + 150150 * kappa1 * params.S1z2 + + 605605j * S2z + 150150 * kappa2 * params.S2z2) + + 210 * params.eta2 * + (322144 + 2145 * (1 + 4 * kappa1) * params.S1z2 - + 12870 * S1z * S2z + + 2145 * (1 + 4 * kappa2) * params.S2z2) - + 21 * Nu * + (2826484 + 85800 * kappa1 * params.S1z2 + + 515515j * S2z + 85800 * kappa2 * params.S2z2 - + 5005 * S1z * (-103j + 60 * S2z)))) - + 26 * params.Mtot3 * + (770 * rDOT * + (-90j * params.eta2 * params.S1z2 + + S2z * (-22 + 67 * Nu - 90j * params.eta2 * S2z) + + S1z * (-22 + Nu * (67 + 300j * S2z) - 180j * params.eta2 * S2z)) + + PhiDOT * r * + (-38076 + 174720 * params.eta3 - + 46585j * S1z - 20790 * kappa1 * params.S1z2 - + 46585j * S2z - 20790 * kappa2 * params.S2z2 - + 1260 * params.eta2 * + (158 + 33 * (5 + 2 * kappa1) * params.S1z2 + + 198 * S1z * S2z + + 33 * (5 + 2 * kappa2) * params.S2z2) + + 7 * Nu * + (6188 + 11880 * kappa1 * params.S1z2 + + 21505j * S2z + 11880 * kappa2 * params.S2z2 + + 55 * S1z * (391j + 744 * S2z))))) + ) + + return ( + -1.3875013875013875e-6j * mass * + (spin_terms + orbital_terms) + / (np.sqrt(70) * params.r4) + ) + + else: + return Complex(0.0, 0.0) \ No newline at end of file diff --git a/build/lib/esigmapy/python_codes/esigma_go_terms_draft.py b/build/lib/esigmapy/python_codes/esigma_go_terms_draft.py new file mode 100644 index 0000000..9c674c3 --- /dev/null +++ b/build/lib/esigmapy/python_codes/esigma_go_terms_draft.py @@ -0,0 +1,1696 @@ +import numpy as np +from dataclasses import dataclass + +# Storing constants +M_PI = np.pi +M_PI2 = M_PI ** 2 +LOG2 = np.log(2) + +#---------------------------------------------------- +def rect(r: float, phi: float) -> complex: + """Convert polar coordinates to rectangular form.""" + return r * np.exp(1j * phi) +#---------------------------------------------------- + +@dataclass +class CommonVars: + 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): + """Similarly for QC functions""" + def decorator(func): + H_QC_LM_FUNCS[(l, m)] = func + return func + return decorator + +# 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 + np.log(64.0) + 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) + + +# H33 + +# def hGO_3_m_3( +# total_mass: float, +# eta: float, +# r: float, +# rDOT: float, +# PhiDOT: float, +# vpnorder: int, +# S1z: float, +# S2z: float, +# x: float, +# ) -> complex: +# delta = np.sqrt(1 - 4 * eta) +# kappa1 = 1.0 +# kappa2 = 1.0 +# r0 = 2 * total_mass / np.exp(0.5) + +# 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) + +# def hQC_3_m_3( +# 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 + +# 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 * np.log(2.0) + 60.0 * np.log(3.0) + +# 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 * np.log(2.0) + 43740.0 * eta * np.log(2.0) - +# 116640.0 * np.log(3.0) + 43740.0 * eta * np.log(3.0) + +# 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 * np.log(2.0) + 120.0j * delta * np.log(3.0) + +# 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 * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + +# 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - +# 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * eta * S1z * np.log(2.0) + +# 396900.0 * delta * eta * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - +# 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * eta * S2z * np.log(2.0) + +# 396900.0 * delta * eta * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - +# 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + +# 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - +# 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * eta * S1z * np.log(3.0) + +# 396900.0 * delta * eta * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - +# 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * eta * S2z * np.log(3.0) + +# 396900.0 * delta * eta * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + +# 952560.0j * delta * (np.log(3.0)**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 * np.log(2.0) * logx + +# 2857680.0j * delta * np.log(3.0) * logx + +# 2143260.0j * delta * (logx**2) + +# log_b0_term +# )) +# return numerator_7 / (2352.0 * np.sqrt(210.0)) + +# else: +# return complex(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, m)) + QC_func = H_QC_LM_FUNCS.get((l, 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: 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/build/lib/esigmapy/python_codes/esigma_pn_inspiral.py b/build/lib/esigmapy/python_codes/esigma_pn_inspiral.py new file mode 100644 index 0000000..eb58620 --- /dev/null +++ b/build/lib/esigmapy/python_codes/esigma_pn_inspiral.py @@ -0,0 +1,2844 @@ +""" +esigma_pn_inspiral.py +==================== +Python translation of ESIGMA_PNInspiral.c + +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 = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant +LOG2 = 0.693147180559945309417232121458 +LOG3 = 1.09861228866810969139524523692 +LOG4 = 1.38629436111989061883446424292 +LOG5 = 1.60943791243410037460075933323 +REAL8_FAIL_NAN = float('nan') + +# ------ 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 + e2 = e * e + pre = 192.0 * np.sqrt(ef) / (985.0 * e2) + return pre * (np.sqrt(ef) * phi_e(e) - phi_e_tilde(e)) + + +def psi_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 18816.0 / (55691.0 * e2 * np.sqrt(ef)) + pf2 = 16382.0 * np.sqrt(ef) / (55691.0 * e2) + b1 = np.sqrt(ef) * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) + b2 = np.sqrt(ef) * psi_e(e) - psi_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def zed_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 924.0 / (19067.0 * e2 * np.sqrt(ef)) + pf2 = 12243.0 * np.sqrt(ef) / (76268.0 * e2) + b1 = -ef * np.sqrt(ef) * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) + b2 = np.sqrt(ef) * zed_e(e) - zed_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def kappa_e_rad(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + den = 769.0 / 96.0 - 3059665.0 * LOG2 / 700566.0 + 8190315.0 * LOG3 / 1868176.0 + pf = np.sqrt(ef) / e2 + b1 = np.sqrt(ef) * 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)""" + 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) + e0 = 2.0 * eta * 96.0 / 15.0 + return (num / den) if e else e0 + +## --------- 1PN terms ------------ + +def x_dot_1pn(e: float, eta: float) -> float: + """Eq. (A27)""" + 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) + e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) + return (num / den) if e else e0 + +## --------- 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + M = m1 + m2 + if e: + 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 * ef**5 * M**4) + else: + 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) + + +def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: + """Eq. (A28)""" + pre = eta * x**6 * np.sqrt(x) + if e: + 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) -> float: + """Eq. (A29)""" + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + + ef = 1.0 - e2 + eta2 = eta * eta + + # Small-e limit + e0_lim = eta * (68206.0 / 2835.0 + + 27322.0 * eta / 315.0 + + 1888.0 * eta2 / 45.0) + + if e: return e0_lim + else: + 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 + 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**2 + pre = 64.0 * eta / 5.0 + + if e: + 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) + else: + return pre * ( + ((1 + 80*kappa1)*m1*m1*S1z*S1z + + 158*m1*m2*S1z*S2z + + (1 + 80*kappa2)*m2*m2*S2z*S2z) + ) / (16.0 * M2) + +## --------- 2.5PN terms ------------ + +def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: + """See Huerta et al.""" + ef = 1.0 - e*e + ef2 = ef * ef + e2 = e * e + pre = eta * x**7 * np.sqrt(x) + + if 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)""" + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + ef = 1.0 - e2 + ef_sqrt = np.sqrt(ef) + M2 = (m1 + m2)**2 + M4 = M2 * M2 + M6 = M4 * M2 + pre = 64.0 * eta / 5.0 + + if e: + 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 + ) + +## --------- 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 e: + 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.)""" + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + e10 = e8 * e2 + + ef = 1.0 - e2 + 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 + + 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)""" + 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 + M2 = (m1 + m2)**2 + M4 = M2 * M2 + pre = 64.0 * eta / 5.0 + + if e: + 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 + + 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 + + M = m1 + m2 + M2 = M * M + M4 = M2 * M2 + M6 = M4 * 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) + ) + + # --- 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 e 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 e 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 e 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 e else val_e0 + +## --------- 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 e 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 e else val_e0 + + +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 e 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 e 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 not e: + 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 not e: + 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 not e: + return 0.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + M = m1 + m2 + pre = e / (ef**4) * (m1*m2) / (90.0 * M**4) + 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 not e: + return 0.0 + pre = 32.0 * eta * e * x**7 / 5.0 + return pre * (-985.0 * np.pi * phi_e_rad(e) / 48.0) + +## ---------- 2PN ------------- + +def e_dot_2pn(e: float, eta: float) -> float: + e_2_pn = 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) + + if e != 0.0: + 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) + ) + else: + e_2_pn = 0.0 + + 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 not e: + 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 e == 0.0: + 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 not e: + return 0.0 + pre = 32.0 * eta * e * x**4 * x**4 * 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 not e: + 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 e == 0.0: + 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 not e: + 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) + + +import math + +def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: + if e == 0.0: + 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 not e: + 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 not e: + 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 + + 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) * np.sqrt(x) + + 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 * np.sqrt(x) + + rel_sep_3pn(e, u, eta) * x * x + + rel_sep_3pn_SS(e, u, m1, m2, S1z, S2z) * x * x) + + + +# ============ 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) + + +# ================== 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 + xsqx = x * np.sqrt(x) + x5 = x**5 + + # ------------------------- + # 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) * x2 * np.sqrt(x) + + 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) * x3 * np.sqrt(x) + inst += x_dot_3_5_pn(e, eta) * x3 * np.sqrt(x) + inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) + inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) + + 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) + + if radiation_pn_order >= 9: + inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * np.sqrt(x) + + 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 + xsqx = x * np.sqrt(x) + + # ------------------------- + # 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) * x2 * np.sqrt(x) + + if radiation_pn_order >= 6: + inst += e_dot_3pn(e, eta, x) * x3 + inst += e_dot_3_5pn(e, eta) * x3 * np.sqrt(x) + 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 += 0.0 # placeholder for higher order terms, not implemented + + 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 e != 0: + 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/build/lib/esigmapy/python_codes/esigma_pn_main.py b/build/lib/esigmapy/python_codes/esigma_pn_main.py new file mode 100644 index 0000000..652e5cd --- /dev/null +++ b/build/lib/esigmapy/python_codes/esigma_pn_main.py @@ -0,0 +1,501 @@ +import numpy as np +from scipy.integrate import ode +from scipy.interpolate import CubicSpline +import math +from .esigma_pn_inspiral import * +from .esigma_go_terms_draft import * +import lal + +# Constants (LAL equivalents) +LAL_PI = math.pi +LAL_MTSUN_SI = 4.925491025543576e-6 # Solar mass in seconds +RadiationPNOrderDefault = 8 # Default radiation reaction PN order (3PN) + +import os +import numpy as np +# 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 = 3 +LAL_MRSUN_SI = 1.476625061404649e3 # solar mass in metres + + +# # ------------------------------------------------------------------ # +# # Helper: populate powers of orbital variables +# # (mirrors PopulateKeplerParams + kepler_vars struct) +# # ------------------------------------------------------------------ # +# @dataclass +# class KeplerVars: +# eta: float = 0.0 +# eta2: float = 0.0 +# eta3: float = 0.0 +# eta4: float = 0.0 +# eta5: float = 0.0 +# eta6: float = 0.0 + +# Mtot: float = 0.0 +# Mtot2: float = 0.0 +# Mtot3: float = 0.0 +# Mtot4: float = 0.0 +# Mtot5: float = 0.0 +# Mtot6: float = 0.0 + +# S1z: float = 0.0 +# S1z2: float = 0.0 +# S1z3: float = 0.0 +# S1z4: float = 0.0 +# S1z5: float = 0.0 +# S1z6: float = 0.0 +# S1z7: float = 0.0 +# S1z8: float = 0.0 +# S1z9: float = 0.0 +# S1z10: float = 0.0 +# S1z11: float = 0.0 +# S1z12: float = 0.0 +# S1z13: float = 0.0 +# S1z14: float = 0.0 +# S1z15: float = 0.0 +# S1z16: float = 0.0 + +# S2z: float = 0.0 +# S2z2: float = 0.0 +# S2z3: float = 0.0 +# S2z4: float = 0.0 +# S2z5: float = 0.0 +# S2z6: float = 0.0 + + +# def _build_kepler_vars(eta: float, total_mass: float, +# S1z: float, S2z: float) -> KeplerVars: +# """Pre-compute and cache all integer powers used by the mode functions.""" +# kv = KeplerVars() +# kv.eta = eta +# kv.eta2 = eta**2; kv.eta3 = eta**3; kv.eta4 = eta**4 +# kv.eta5 = eta**5; kv.eta6 = eta**6 + +# kv.Mtot = total_mass +# kv.Mtot2 = total_mass**2; kv.Mtot3 = total_mass**3 +# kv.Mtot4 = total_mass**4; kv.Mtot5 = total_mass**5 +# kv.Mtot6 = total_mass**6 + +# kv.S1z = S1z +# for p in range(2, 17): +# setattr(kv, f"S1z{p}", S1z**p) + +# kv.S2z = S2z +# for p in range(2, 7): +# setattr(kv, f"S2z{p}", S2z**p) + +# return kv + + +# ------------------------------------------------------------------ # +# 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 = { + "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] + # ------------------------------------------------------------------ # + u0 = pn_kepler_equation(eta, x_init, e_init, mean_anom_init) + r0 = separation(u0, eta, x_init, e_init, mass1, mass2, S1z, S2z) + y0_dot = eccentric_x_model_odes(0.0, [x_init, e_init, mean_anom_init, 0.0], params) + phi_dot0 = y0_dot[3] + + y0 = np.array([x_init, e_init, mean_anom_init, 0.0]) + + # ------------------------------------------------------------------ # + # ODE integration (RK45 via scipy) + # ------------------------------------------------------------------ # + def rhs(t, y): + return eccentric_x_model_odes(t, y, params) + + solver = ode(rhs).set_integrator( + "dopri5", # RK4(5) Dormand-Prince ≈ gsl rkf45 + rtol=ode_eps, + atol=0.0, + nsteps=10_000, + max_hnil=0, + first_step=2 * LAL_PI / phi_dot0 / 100, + ) + solver.set_initial_value(y0, 0.0) + + MAX_SAMPLES = 2048 * 16384 + + t_list = [0.0] + x_list = [x_init] + e_list = [e_init] + l_list = [mean_anom_init] + phi_list = [0.0] + phi_dot_list = [phi_dot0] + r_list = [r0] + u_list = [u0] + + bad_number = False + + while solver.successful(): + solver.integrate(solver.t + dt) + + y = solver.y + + # NaN / Inf guard + if not np.all(np.isfinite(y)): + bad_number = True + break + + t_list.append(solver.t) + x_list.append(y[0]) + e_list.append(y[1]) + l_list.append(y[2]) + phi_list.append(y[3]) + + yd = eccentric_x_model_odes(solver.t, y, params) + phi_dot_list.append(yd[3]) + + ui = pn_kepler_equation(eta, y[0], y[1], y[2]) + ri = separation(ui, eta, y[0], y[1], mass1, mass2, S1z, S2z) + u_list.append(ui) + r_list.append(ri) + + if len(t_list) >= MAX_SAMPLES: + break + + if y[0] >= x_final: + break + + # Convert to arrays + t_arr = np.array(t_list) + x_arr = np.array(x_list) + e_arr = np.array(e_list) + l_arr = np.array(l_list) + phi_arr = np.array(phi_list) + phi_dot_arr = np.array(phi_dot_list) + r_arr = np.array(r_list) + + final_i = len(t_arr) if not bad_number else len(t_arr) # same either way here + if final_i < 4: + raise RuntimeError("Integration produced fewer than 4 points; cannot interpolate.") + + # ------------------------------------------------------------------ # + # 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_uniform(t_arr, phi_dot_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/build/lib/esigmapy/python_codes/generator_python.py b/build/lib/esigmapy/python_codes/generator_python.py new file mode 100644 index 0000000..761764d --- /dev/null +++ b/build/lib/esigmapy/python_codes/generator_python.py @@ -0,0 +1,1211 @@ +# Copyright (C) 2020 Prayush Kumar, Akash Maurya, Kaushik Paul +# +"""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 * + +ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 +ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 + + +def eccentricity_at_extremum_frequency( + 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.data.data *= lal.MTSUN_SI + + if verbose: + print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") + + omega = pt.TimeSeries(phidot.data.data, delta_t=t.data.data[1] - t.data.data[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.data.data - time_at_sensitive_freq).argmin() + e0 = e.data.data[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.data.data, x.data.data**1.5, "--", label="x ** {3/2}") + ax1.plot(t.data.data, phidot.data.data, 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.data.data, e.data.data, 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( + 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.data.data *= 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.data.data - x_reference).argmin() + e0 = e.data.data[idx_e0] + + if show_figures: + import matplotlib.pyplot as plt + + fig, (ax) = plt.subplots(1, 1, figsize=(10, 5), sharex=True) + ax.plot(t.data.data, x.data.data**1.5, "--", label="x ** {3/2}") + ax.plot(t.data.data, phidot.data.data, lw=0.5, label="omega") + ax.axhline(x_reference**1.5, color="c", lw=1) + ax.axvline(t.data.data[idx_e0], color="r", lw=1) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Orbital angular velocity") + + ax2 = plt.twinx(ax) + ax2.plot(t.data.data, e.data.data, 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( + 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.data.data[-1] + # mean_anomaly = l.data.data[-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: + ref_idx = np.searchsorted( + (retval[1].data.data ** 1.5) / ((mass1 + mass2) * lal.MTSUN_SI * np.pi), + f_lower, + ) + new_len = len(retval[0].data.data) - ref_idx + for ii in range(8): + lal.ResizeREAL8TimeSeries(retval[ii], ref_idx, new_len) + + t, x, e, l, phi, phidot, r, rdot = retval[:8] + t.data.data *= ( + 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.data, + x.data, + phi.data, + phidot.data, + r.data, + rdot.data, + mass1, + mass2, + spin1z, + spin2z, + distance, + ) + + if return_pycbc_timeseries: + modes = { + k: pt.TimeSeries( + modes[k].data.data, + delta_t=delta_t, + epoch=-delta_t * (len(modes[k].data.data) - 1), + ) + for k in modes + } + else: + modes = {k: np.asarray(modes[k].data.data) 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}.data.data, delta_t=delta_t, epoch=-delta_t * len({name}.data.data))" + ) + return orbital_var_dict, modes + + for name in return_orbital_params: + exec(f"orbital_var_dict['{name}'] = {name}.data.data") + return (t.data.data - t.data.data[-1]), orbital_var_dict, modes + + if return_pycbc_timeseries: + return modes + return (t.data.data - t.data.data[-1]), modes + + +def get_inspiral_esigma_waveform( + 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( + 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_window_start(freq_, delta_t_, delta_phi_, direction="forward"): + """Integrates frequency backward/forward from the start/end until + `delta_phi_` radians of orbital phase has elapsed. + + Args: + freq_ (numpy.array): Frequency time series, uniformly sampled + delta_t_ (float64): Step size in time + delta_phi_ (float64): Phase shift in radians to integrate across + direction (str, optional): Direction of integration in time. + Defaults to "forward". + + Returns: + int: Index in frequency array where `delta_phi` is reached + """ + from scipy import integrate + + if direction == "backward": + for idx in range(len(freq_) - 2, 0, -1): + # this could be optimized to not repeat integration + if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): + return idx + elif direction == "forward": + for idx in range(1, len(freq_)): + # this could be optimized to not repeat integration + if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( + delta_phi_ + ): + return idx + + +def _get_transition_frequency_window( + orbital_phase, + orbital_freq, + delta_t, + f_mr_transition, # orbital frequency + num_hyb_orbits, + keep_f_mr_transition_at_center, + blend_using_avg_orbital_frequency, + failsafe, + verbose=False, +): + """ + This function first finds where the `orbital_freq` crosses the transition + frequency `f_mr_transition`, and finds the point in time that is + `num_hyb_orbits` orbits before that time. This marks the start of the + hybridization window, and the orbital frequency at that time is returned + + Parameters: + ----------- + orbital_phase -- Orbital phase evolution + orbital_freq -- Orbital frequency evolution + delta_t -- Waveform's time grid-spacing (s) + f_mr_transition -- Inspiral to merger transition frequency + (Hz). This should be the same (mode/orbital) + frequency as passed for `orbital_freq`. + num_hyb_orbits -- number of orbital cycles to blend over. + 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). + blend_using_avg_orbital_frequency -- If True, the orbit averaged + frequency during the inspiral is used to + blend modes, instead of the modes' + frequency. + failsafe -- If True, we make reasonable choices for the + user, if the inputs to this method lead + into exceptions. + verbose -- Verbosity flag + + Returns: + -------- + f_window_mr_transition -- Hybridization frequency window (in Hz). + """ + transition_idx = ( + len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) + ) # index at which orbital_freq becomes just smaller than transition orbital + # frequency, towards the end of waveform + + if verbose > 4: + print( + f"""Transition frequency found at index {transition_idx} + in the orbital frequency array of length {len(orbital_freq)}""" + ) + + if blend_using_avg_orbital_frequency: + # We integrate `orbital_freq` to obtain `orbital_phase` + from scipy import integrate + + orbital_phase = integrate.cumulative_trapezoid( + orbital_freq, dx=delta_t, initial=0 + ) + if len(orbital_phase) != len(orbital_freq): + raise RuntimeError( + f"""Something went wrong while integrating orbital frequency. +They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" + ) + + if keep_f_mr_transition_at_center: + # Orbital cycle based hybridization that keeps f_mr_transition at + # the hybridization frequency window's midpoint + if blend_using_avg_orbital_frequency: + window_start_idx = _get_window_start( + orbital_freq[: transition_idx + 1], + delta_t, + num_hyb_orbits * np.pi, + direction="backward", + ) + window_end_idx = transition_idx + _get_window_start( + orbital_freq[transition_idx:], + delta_t, + num_hyb_orbits * np.pi, + direction="forward", + ) + # FAILSAFE mode behavior + if window_start_idx is None: + if failsafe: + window_start_idx = 0 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform before the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or increase the +inspiral-to-merger transition frequency. `window_start_idx` is None.""" + ) + if window_end_idx is None: + if failsafe: + window_end_idx = len(orbital_freq) - 1 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform after the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or decrease the +inspiral-to-merger transition frequency. `window_end_idx` is None.""" + ) + else: + # phase is always a monotonically increasing function, + # hence using the more efficient np.searchsorted method + window_start_idx = -1 + np.searchsorted( + orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi + ) + window_end_idx = np.searchsorted( + orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi + ) + f_window_mr_transition = 2 * np.min( + [ + orbital_freq[window_end_idx] - f_mr_transition, + f_mr_transition - orbital_freq[window_start_idx], + ] + ) + elif not keep_f_mr_transition_at_center: + # Orbital cycle based hybridization that keeps f_mr_transition at + # the hybridization frequency window's right end + if blend_using_avg_orbital_frequency: + window_start_idx = _get_window_start( + orbital_freq[: transition_idx + 1], + delta_t, + 2 * num_hyb_orbits * np.pi, + direction="backward", + ) + if window_start_idx is None: + if failsafe: + window_start_idx = 0 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform before the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or increase the +inspiral-to-merger transition frequency. `window_start_idx` is None.""" + ) + else: + # Orbital cycle based hybridization that keeps f_mr_transition + # at the hybridization frequency window's end + window_start_idx = ( + np.searchsorted( + orbital_phase, + orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, + ) + - 1 + ) + f_window_mr_transition = ( + orbital_freq[transition_idx] - orbital_freq[window_start_idx] + ) + + if verbose > 4: + print( + f"""The transition is to happen in a window from + frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, + between indices: [{window_start_idx}, {transition_idx}]""" + ) + else: + raise RuntimeError( + """We should never reach here. Did you set a non-bool value for the + flag `keep_f_mr_transition_at_center`?""" + ) + + if verbose > 4: + print(f"""f_window_mr_transition = {f_window_mr_transition}""") + + return f_window_mr_transition + + +def get_imr_esigma_modes( + 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( + 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. Its 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( + 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( + 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/build/lib/esigmapy/surrogate/__init__.py b/build/lib/esigmapy/surrogate/__init__.py new file mode 100644 index 0000000..90fe36e --- /dev/null +++ b/build/lib/esigmapy/surrogate/__init__.py @@ -0,0 +1,4 @@ +from __future__ import absolute_import + +from .surrogate import * +from .generator import * diff --git a/build/lib/esigmapy/surrogate/generator.py b/build/lib/esigmapy/surrogate/generator.py new file mode 100644 index 0000000..e1c2f7c --- /dev/null +++ b/build/lib/esigmapy/surrogate/generator.py @@ -0,0 +1,820 @@ +# Copyright (C) 2026 Akash Maurya, Prayush Kumar + +"""Functions for generating ESIGMASur 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 esigmapy.utils import f_ISCO_spin +from esigmapy.generator import _get_transition_frequency_window +from .surrogate import _get_surrogate + +ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 +ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 + + +def get_surrogate_object(): + """ + Returns the surrogate object. Useful for advanced users who want to + directly use the base surrogate class' functionalities. + """ + return _get_surrogate() + + +def get_inspiral_esigmasur_modes( + mass1, + mass2, + reference_eccentricity=0.0, + reference_mean_anomaly=0.0, + delta_t=None, + times=None, + t_start=None, + t_end=None, + distance=1.0, + include_conjugate_modes=False, + return_orbital_params=False, + return_pycbc_timeseries=True, + verbose=False, +): + """ + Returns inspiral ESIGMASur GW modes + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + delta_t -- Waveform's time grid-spacing (in s) + reference_eccentricity -- Eccentricity at reference time of surrogate + reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) + distance -- Luminosity distance to the binary (in Mpc) + t_start, t_end -- Start and end times of the waveform + to be generated (in seconds). + Note that the surrogate defines t=0 at the end of + inspiral, so t_start and t_end should be negative + and t_start < t_end <= 0. + Defaults to the full duration of the surrogate. + include_conjugate_modes -- If True, negative "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'] + 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: + orbital_var_names = ["x", "e", "l"] + if return_orbital_params != True: + for name in return_orbital_params: + if name not in orbital_var_names: + raise Exception( + f"{name} is not a valid orbital variable name. Available orbital variable names are: {orbital_var_names}." + ) + + sur = _get_surrogate() + total_mass = mass1 + mass2 + q = mass1 / mass2 + if q < 1: + q = 1 / q + + modes_to_use = [(2, 2)] + + # Calculating the orbital variables + itime = time.perf_counter() + modes = {} + + retval = sur( + M=total_mass, + params=[ + q, + reference_eccentricity, + reference_mean_anomaly, + ], # q, e, l at t=t_ref + delta_t=delta_t, + t_start=t_start, + t_end=t_end, + times=times, + remove_start_phase=True, + return_orbital_variables=return_orbital_params, + ) + # Currently, the surrogate only supports (2, 2) mode, so we directly retrieve that from the returned dictionary. + el, em = modes_to_use[0] + if return_orbital_params: + t, orb_vars, modes[(el, em)] = retval + else: + t, modes[(el, em)] = retval + modes[(el, em)] /= distance + + if include_conjugate_modes: + for el, em in modes_to_use: + modes[(el, -em)] = (-1) ** el * np.conjugate(modes[(el, em)]) + + if return_pycbc_timeseries: + if times is None: + modes = { + k: pt.TimeSeries( + modes[k], + delta_t=delta_t, + epoch=-delta_t * (len(modes[k]) - 1), + ) + for k in modes + } + else: + raise ValueError( + """Cannot return PyCBC TimeSeries when the user provides custom time grid via `times` due to the possibilty of it being a non-uniform time-grid. +Please set `return_pycbc_timeseries=False` if you want to provide custom time grid.""" + ) + 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 = orbital_var_names + + if return_pycbc_timeseries: + # No need to check and raise error here if `times` is provided, + # because the error will be raised while trying to convert modes + # to PyCBC TimeSeries above. + for name in return_orbital_params: + exec( + f"orbital_var_dict['{name}'] = pt.TimeSeries(orb_vars['{name}'], delta_t=delta_t, epoch=-delta_t * (len(orb_vars['{name}'])-1))" + ) + return orbital_var_dict, modes + + for name in return_orbital_params: + exec(f"orbital_var_dict['{name}'] = orb_vars['{name}']") + # return (t - t[-1]), orbital_var_dict, modes + return t, orbital_var_dict, modes + + if return_pycbc_timeseries: + return modes + # return (t - t[-1]), modes + return t, modes + + +def get_inspiral_esigmasur_waveform( + mass1, + mass2, + reference_eccentricity=0.0, + reference_mean_anomaly=0.0, + delta_t=None, + t_start=None, + t_end=None, + times=None, + inclination=0.0, + coa_phase=0.0, + distance=1.0, + return_orbital_params=False, + return_pycbc_timeseries=True, + verbose=False, + **kwargs, +): + """ + Returns inspiral ESIGMASur GW polarizations + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + reference_eccentricity -- Eccentricity at reference time of surrogate + reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) + delta_t -- Waveform's time grid-spacing (in s). + Can be omitted if providing custom time grid via + `times` argument. + t_start, t_end -- Start and end times of the waveform + to be generated (in seconds). + Note that the surrogate defines t=0 at the end of + inspiral, so t_start and t_end should be negative + and t_start < t_end <= 0. + Defaults to the full duration of the surrogate. + times -- Custom time grid (can be non-uniform) on which the + waveform should be generated. Should be a numpy + array of time values in seconds. + If provided, `delta_t`, `t_start` and `t_end` are ignored. + Also set `return_pycbc_timeseries=False` to use this option. + 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) + 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'] + return_pycbc_timeseries -- If True, returns data in the form of PyCBC timeseries. + True by default. Set to False if you want to provide custom time grid via `times` argument, or if you want the output in numpy arrays. + 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_esigmasur_modes( + mass1=mass1, + mass2=mass2, + reference_eccentricity=reference_eccentricity, + reference_mean_anomaly=reference_mean_anomaly, + t_start=t_start, + t_end=t_end, + delta_t=delta_t, + times=times, + distance=distance, + 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: + if times is None: + 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)) + else: + raise ValueError( + """Cannot return PyCBC TimeSeries when the user provides custom time grid via `times` due to the possibilty of it being a non-uniform time-grid. +Please set `return_pycbc_timeseries=False` if you want to provide custom time grid.""" + ) + + 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_esigmasur_mode( + mass1, + mass2, + delta_t, + reference_eccentricity=0.0, + reference_mean_anomaly=0.0, + t_start=None, + distance=1.0, + coa_phase=None, + include_conjugate_modes=False, + f_mr_transition=None, + f_window_mr_transition=None, + num_hyb_orbits=0.25, + 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 ESIGMASur for inspiral and + NRSur7dq4/SEOBNRv4PHM for merger-ringdown + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + delta_t -- Waveform's time grid-spacing (in s) + reference_eccentricity -- Eccentricity at reference time of surrogate + reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) + t_start -- Start time of the waveform to be generated (in seconds). + Note that the surrogate defines t=0 at the end of + inspiral, so t_start should be negative + and t_start < 0. + Defaults to the full duration of the surrogate. + coa_phase -- Coalesence phase of the binary (in rad) + distance -- Luminosity distance to the binary (in Mpc) + 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 + using which alignment is done, i.e. (2,2) mode here. + Defaults to the minimum of the Kerr and + Schwarzschild ISCO frequency equivalent + for the (2,2)-mode. + f_window_mr_transition -- Hybridization frequency window (in Hz). + Should correspond to the mode + using which alignment is done, i.e. (2,2) mode here. + 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_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: + ['e', 'l', 'x']. + 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 + """ + + spin1z = 0.0 + spin2z = 0.0 + blend_using_avg_orbital_frequency = True + modes_to_use = [(2, 2)] + mode_to_align_by = (2, 2) + + if not hasattr(ls, merger_ringdown_approximant): + raise IOError( + """We cannot generate individual modes for {merger_ringdown_approximant}. +Try one of: [NRSur7dq4, SEOBNRv4PHM]""" + ) + if (reference_mean_anomaly is None) and (coa_phase is None): + raise IOError( + f"""Please specify one of the phase angles, either of `reference_mean_anomaly` or `coa_phase`.""" + ) + if blend_aligning_merger_to_inspiral and (reference_mean_anomaly is None): + raise IOError( + f"""If you want to attach ESIGMASur inspiral to merger, by phase shifting merger to inspiral, please specify the phase angle `reference_mean_anomaly`""" + ) + if (not blend_aligning_merger_to_inspiral) and (coa_phase is None): + raise IOError( + f"""If you want to attach ESIGMASur inspiral to merger, by phase shifting inspiral to merger, please specify the phase angle `coa_phase`""" + ) + if reference_mean_anomaly is None: + reference_mean_anomaly = 0 + if coa_phase is None: + coa_phase = 0 + + available_inspiral_orbital_params = ["e", "l", "x"] + 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 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: + 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_esigmasur_modes( + mass1=mass1, + mass2=mass2, + reference_eccentricity=reference_eccentricity, + reference_mean_anomaly=reference_mean_anomaly, + t_start=t_start, + t_end=None, # To generate the full inspiral for attachment to merger + delta_t=delta_t, + distance=distance, + 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 reference eccentricity +{reference_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 reference eccentricity +{reference_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: + NotImplementedError( + "Can't use any prescription other than the orbit averaged frequency one." + ) + + 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( + None, + 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. Its very likely +that you entered a very large reference eccentricity {reference_eccentricity}. The orbital +eccentricity at the end of inspiral was {orbital_eccentricity[-1]} + """ + ) + raise exc + modes_imr_numpy = retval[0] + + # Set t=0 at the end of inspiral surrogate + if mode_to_align_by not in modes_imr_numpy: + mode_to_align_by = list(modes_imr_numpy.keys())[0] + idx_peak = len(modes_inspiral_numpy[mode_to_align_by]) - 1 + 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 retval, orbital_vars_dict, modes_imr + elif return_orbital_params_user: + return orbital_vars_dict, modes_imr + elif return_hybridization_info: + return retval, modes_imr + return modes_imr + + +def get_imr_esigmasur_waveform( + mass1, + mass2, + delta_t, + reference_eccentricity=0.0, + reference_mean_anomaly=0.0, + t_start=None, + distance=1.0, + coa_phase=0.0, + inclination=0.0, + f_mr_transition=None, + f_window_mr_transition=None, + num_hyb_orbits=0.25, + 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 polarizations constructed using hybridized IMR ESIGMASur modes + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + delta_t -- Waveform's time grid-spacing (in s) + reference_eccentricity -- Eccentricity at reference time of surrogate + reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) + t_start -- Start time of the waveform to be generated (in seconds). + Note that the surrogate defines t=0 at the end of + inspiral, so t_start should be negative + and t_start < 0. + Defaults to the full duration of the surrogate. + distance -- Luminosity distance to the binary (in Mpc) + coa_phase -- Coalesence phase of the binary (in rad) + inclination -- Inclination (in rad), defined as the angle + between the orbital angular momentum L and + the line-of-sight + f_mr_transition -- Inspiral to merger transition GW frequency + (Hz). Should correspond to the mode + using which alignment is done, i.e. (2,2) mode here. + Defaults to the minimum of the Kerr and + Schwarzschild ISCO frequency equivalent + for the (2,2)-mode. + f_window_mr_transition -- Hybridization frequency window (in Hz). + Should correspond to the mode + using which alignment is done, i.e. (2,2) mode here. + 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_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: + ['e', 'l', 'x']. + 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_esigmasur_mode( + mass1=mass1, + mass2=mass2, + reference_eccentricity=reference_eccentricity, + reference_mean_anomaly=reference_mean_anomaly, + delta_t=delta_t, + t_start=t_start, + distance=distance, + coa_phase=coa_phase, + 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_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/build/lib/esigmapy/surrogate/surrogate.py b/build/lib/esigmapy/surrogate/surrogate.py new file mode 100644 index 0000000..4f99a2c --- /dev/null +++ b/build/lib/esigmapy/surrogate/surrogate.py @@ -0,0 +1,556 @@ +# Copyright (C) 2026 Akash Maurya + +"""ESIGMASur surrogate base evaluation code""" + +import os + +os.environ["OMP_NUM_THREADS"] = "1" +import numpy as np +from numba import njit +import h5py +import scipy.interpolate as si +import TPI +from lal import SpinWeightedSphericalHarmonic +from pycbc.conversions import eta_from_q +import re + +_amp_correction_factor = np.real( + SpinWeightedSphericalHarmonic(0, 0, -2, 2, 2) +) # The amplitude normalization factor saved in the surrogate files is off by this amount. So we correct for it during surrogate evaluation by using this factor. + + +@njit(fastmath=True) +def mode_from_amp_phase(amp, phase): + return amp * np.exp(-1j * phase) + + +def _unwrap_single_float(val): + if isinstance(val, (float, int, np.floating, np.integer)): + return float(val) + elif isinstance(val, np.ndarray) and val.size == 1: + return float(val.item()) + else: + raise ValueError( + f"Expected a float or a numpy array of size 1, got: {val} ({type(val)})" + ) + + +class Surrogate: + def __init__(self, data_dir): + # The directory where the surrogate data is stored + self.sur_dir = data_dir + # Surrogate data pieces. This will be set in the child classes, as the circular and eccentric surrogates are built of different data pieces. + self.data_piece_names = None + # Normalization factors for the surrogate data pieces, which are used to un-normalize the surrogate data pieces before reconstructing the waveform; read from the surrogate files via self.load_norm_factors(). + self.norm_factor = {} + # The EIM B-matrices for the surrogate data pieces; read from the surrogate files via self.load_eim_B_matrices(). + self.eim_B = {} + # The parametric fits for the surrogate data pieces at EI nodes; read from the surrogate files via self.load_param_space_fits(), defined in child classes. + self.fit = {} + + def get_metadata(self, key): + """ + Returns metadata of the surrogate + + Parameters: + ----------- + key -- Metadata key (string) or list of keys to return. + + Returns: + -------- + Metadata value(s) corresponding to the key(s). + If a single key is provided, returns the corresponding value, + otherwise returns a list of values corresponding to the list of keys. + """ + filename = os.path.join(self.sur_dir, "surrogate_metadata.hdf") + with h5py.File(filename, "r") as f: + if isinstance(key, str): + return f[key][()] + return [f[k][()] for k in key] + + def load_norm_factors(self): + filename_norm_factors = os.path.join(self.sur_dir, f"norm_factors.npz") + norm_factor_dataset = np.load(filename_norm_factors) + for data_piece_name in self.data_piece_names: + self.norm_factor[data_piece_name] = norm_factor_dataset[ + f"norm_factor_{data_piece_name}" + ] + norm_factor_dataset.close() + + def load_eim_B_matrices(self, filename=None, data_piece_names=None): + if filename is None: + filename = "eim_B.npz" + if data_piece_names is None: + data_piece_names = self.data_piece_names + if isinstance(data_piece_names, str): + data_piece_names = [data_piece_names] + filename_eim = os.path.join(self.sur_dir, filename) + eim_B_dataset = np.load(filename_eim) + for data_piece_name in data_piece_names: + self.eim_B[data_piece_name] = eim_B_dataset[f"eim_B_{data_piece_name}"] + eim_B_dataset.close() + + def _set_time_range(self, M, times, t_start, t_end): + mass_scaling_factor = M / self.sur_total_mass + + t_min_sur = self.t_grid_sur[0] * mass_scaling_factor + t_max_sur = self.t_grid_sur[-1] * mass_scaling_factor + + if times is None: + if t_start is None: + t_start = t_min_sur + if t_end is None: + t_end = t_max_sur + else: + t_start = times[0] + t_end = times[-1] + + if t_start < t_min_sur or t_end > t_max_sur: + raise ValueError( + f"""Requested time range [{t_start}s, {t_end}s] is out of the surrogate's time range of [{t_min_sur}s, {t_max_sur}s] for the given total mass of {M:.2f} M_sun. +Please choose a time interval within these bounds.""" + ) + + return t_start, t_end, mass_scaling_factor + + @staticmethod + def _find_conservative_starting_truncation_index(grid, val): + idx = np.searchsorted(grid, val, side="right") - 1 + idx -= 5 # Leaving some buffer to avoid edge effects in spline interpolation + # Checking if we even have data this deep in the starting + if idx < 0: + idx = 0 + return idx + + +class CircularSurrogate(Surrogate): + def __init__(self, data_dir): + super().__init__(data_dir=data_dir) + self.data_piece_names = [ + "amp", + "phase", + "x", + "l", + ] # The surrogate data pieces for the circular surrogate. The "x" and "l" data pieces are used only for reconstructing the orbital variables, and are not needed if one only wants the waveform. + self.load_sur_metadata() + self.load_norm_factors() + self.load_eim_B_matrices( + filename="eim_B.npz", data_piece_names=["amp", "phase"] + ) + self.load_eim_B_matrices( + filename="eim_B-orb_vars.npz", data_piece_names=["x", "l"] + ) + self.load_param_space_fits() + + def load_sur_metadata(self): + self.sur_total_mass, self.t_grid_sur = self.get_metadata(["M", "t_grid_sur"]) + + @staticmethod + def load_interpolant(filename): + # scipy BSplines + tck = np.load(filename) + vec_interpolant = si.BSpline(tck["t"], tck["c"], tck["k"], extrapolate=False) + return vec_interpolant + + def load_param_space_fits(self): + for data_piece_name in self.data_piece_names: + filename_interp = os.path.join(self.sur_dir, f"{data_piece_name}_fits.npz") + self.fit[data_piece_name] = self.load_interpolant(filename_interp) + + def __call__( + self, + M, + q, + reference_mean_anomaly=0.0, # This is only used for returning orbital variables + delta_t=None, + t_start=None, + t_end=None, + times=None, + remove_initial_phase=False, + return_amp_phase_only=False, + return_orbital_variables=False, + ): + + if delta_t is None and times is None: + raise ValueError("Either delta_t or times must be provided.") + + t_grid_sur = self.t_grid_sur + + t_start, t_end, mass_scaling_factor = self._set_time_range( + M=M, times=times, t_start=t_start, t_end=t_end + ) + + start_idx = self._find_conservative_starting_truncation_index( + grid=t_grid_sur, val=t_start / mass_scaling_factor + ) + t_grid_sur = t_grid_sur[start_idx:] * mass_scaling_factor + + if times is None: + num_samples = int((t_end - t_start) / delta_t) + 1 + new_t_grid = t_start + np.arange(num_samples) * delta_t + else: + new_t_grid = times + + q = _unwrap_single_float( + q + ) # This is to ensure that q is a single value and not an array + eta = eta_from_q(q) + + amp_node_vals = self.fit["amp"](eta) + phase_node_vals = self.fit["phase"](eta) + + amp_native = self.norm_factor["amp"] * np.dot( + amp_node_vals, self.eim_B["amp"][:, start_idx:] + ) + phase_native = self.norm_factor["phase"] * np.dot( + phase_node_vals, self.eim_B["phase"][:, start_idx:] + ) + + amp = np.interp(new_t_grid, t_grid_sur, amp_native) + phase = np.interp(new_t_grid, t_grid_sur, phase_native) + + amp *= ( + mass_scaling_factor / _amp_correction_factor + ) # Correcting for the amplitude normalization factor that was off in the surrogate files. + + if remove_initial_phase: + phase -= phase[0] + if return_amp_phase_only: + return amp, phase + + if return_orbital_variables: + e = np.zeros(len(new_t_grid)) + + l_node_vals = self.fit["l"](eta) + l_native = self.norm_factor["l"] * np.dot( + l_node_vals, self.eim_B["l"][:, start_idx:] + ) + l = ( + np.interp(new_t_grid, t_grid_sur, l_native) + reference_mean_anomaly + ) # The circular surrogate mean anomaly data was prepared such that the mean anomaly at the reference time is 0, so we add the reference_mean_anomaly here to get the correct mean anomaly values at the reference time. This is physically correct because circular waveforms just differing in mean anomalies are simply constant phase-shifted versions of each other + # Caution: This also means that one should make sure during surrogate construction that the reference time of the circular surrogate is the same as that of the eccentric surrogate. + l -= ( + 2 * np.pi * np.floor(l[0] / (2 * np.pi)) + ) # Bringing starting value of mean anomaly in [0, 2pi) + + x_node_vals = self.fit["x"](eta) + x_native = self.norm_factor["x"] * np.dot( + x_node_vals, self.eim_B["x"][:, start_idx:] + ) + x = np.interp(new_t_grid, t_grid_sur, x_native) + + orb_vars = {"e": e, "l": l, "x": x} + return new_t_grid, orb_vars, mode_from_amp_phase(amp, phase) + + return new_t_grid, mode_from_amp_phase(amp, phase) + + +class EccentricSurrogate(Surrogate): + def __init__(self, ecc_data_dir, circ_data_dir, verbose=False): + super().__init__(data_dir=ecc_data_dir) + self.circ_sur_dir = circ_data_dir + self.circ_sur = CircularSurrogate(data_dir=self.circ_sur_dir) + + self.data_piece_names = [ + "res_amp", + "res_phase", + "res_circ_phase", + "shifted_mean_anomaly", + "e", + "x", + ] + self.ei_indices = {} + + self.load_sur_metadata() + self.load_norm_factors() + self.load_eim_B_matrices() + self.load_ei_indices() + self.load_param_space_fits(verbose=verbose) + + self.q_min = 1.0 + self.q_max = 6.0 + self.e_ref_min = 5.0e-7 # The TPI fits below this value fail to evaluate + self.e_ref_max = 0.431 + self.l_ref_min = 0.0 + self.l_ref_max = 2 * np.pi + + def check_param_range(self, q, e_ref, l_ref, override=False): + if not override: + if not (self.q_min <= q <= self.q_max): + raise ValueError( + f"Mass ratio q={q} is out of range [{self.q_min}, {self.q_max}]. Please choose a value within this range." + ) + if not (0.0 <= e_ref <= self.e_ref_max): + raise ValueError( + f"Reference eccentricity e_ref={e_ref} is out of range [{0.}, {self.e_ref_max}]. Please choose a value within this range." + ) + if not (self.l_ref_min <= l_ref <= self.l_ref_max): + raise ValueError( + f"Reference mean anomaly l_ref={l_ref} is out of range [{self.l_ref_min}, {self.l_ref_max}]. Please choose a value within this range." + ) + + def load_sur_metadata(self): + self.sur_total_mass, self.t_ref, self.t_grid_sur, self.l_grid_sur = ( + self.get_metadata(["M", "t_ref", "t_grid_sur", "l_grid_sur"]) + ) + + def load_ei_indices(self): + filename_ei_indices = os.path.join(self.sur_dir, f"ei_indices.npz") + ei_indices_dataset = np.load(filename_ei_indices) + for data_piece_name in ["res_amp", "res_phase"]: + self.ei_indices[data_piece_name] = ei_indices_dataset[ + f"ei_indices_{data_piece_name}" + ] + ei_indices_dataset.close() + + @staticmethod + def _get_sorted_fit_filenames(filenames): + # Use a regex to extract the number before .h5, regardless of the prefix + pattern = re.compile(r"-(\d+)_spline\.h5$") + indexed_files = {} + for fname in filenames: + match = pattern.search( + fname + ) # search instead of match allows for variable prefix + if match: + idx = int(match.group(1)) + indexed_files[idx] = fname + + # Create array of appropriate size + max_index = max(indexed_files.keys()) + sorted_filenames = [None] * (max_index + 1) + # Fill the array + for idx, fname in indexed_files.items(): + sorted_filenames[idx] = fname + return sorted_filenames + + @staticmethod + def load_fit(filepath): + with h5py.File(filepath, "r") as f: + nodes = f["nodes"][()] + coeffs = f["coefficients"][()] + fit = TPI.TP_Interpolant_ND(list(nodes), coeffs=coeffs) + return fit + + def load_param_space_fits(self, verbose=False): + for data_piece_name in self.data_piece_names: + load_dir = os.path.join(self.sur_dir, f"fits/{data_piece_name}_fits") + # List of all the files which end in .h5 + filenames = [f for f in os.listdir(load_dir) if f.endswith(".h5")] + sorted_filenames = self._get_sorted_fit_filenames(filenames=filenames) + self.fit[data_piece_name] = [] + + for filename in sorted_filenames: + filepath = os.path.join(load_dir, filename) + if verbose: + print(f"Loading spline interpolant of GPR from {filename}...") + fit = self.load_fit(filepath) + self.fit[data_piece_name].append(fit) + + filepath = os.path.join( + self.sur_dir, f"fits/mean_anomaly_offset-ref_space-3D-fit_spline.h5" + ) + fit = self.load_fit(filepath) + self.fit["mean_anomaly_offset_fit"] = [fit] + + def __call__( + self, + M, + params, + delta_t=None, + t_start=None, + t_end=None, + times=None, + remove_start_phase=True, + return_orbital_variables=False, + ): + + if delta_t is None and times is None: + raise ValueError("Either delta_t or times must be provided.") + + t_grid_sur = self.t_grid_sur # The native time grid of the surrogate + l_grid_sur = ( + self.l_grid_sur + ) # The native shifted mean anomaly grid of the surrogate + + t_start, t_end, mass_scaling_factor = self._set_time_range( + M=M, times=times, t_start=t_start, t_end=t_end + ) + + start_idx_t = self._find_conservative_starting_truncation_index( + grid=t_grid_sur, val=t_start / mass_scaling_factor + ) + t_grid_sur = t_grid_sur[start_idx_t:] * mass_scaling_factor + + if times is None: + num_samples = int((t_end - t_start) / delta_t) + 1 + new_t_grid = t_start + np.arange(num_samples) * delta_t + else: + new_t_grid = times + + q, e_ref, l_ref = params + self.check_param_range(q=q, e_ref=e_ref, l_ref=l_ref) + + if e_ref > self.e_ref_min: + amp0_, phase0_ = self.circ_sur( + M=M, + q=q, + delta_t=delta_t, + t_start=t_start, + t_end=t_end, + times=new_t_grid, + remove_initial_phase=True, + return_amp_phase_only=True, + return_orbital_variables=False, + ) + else: + if e_ref != 0.0: + print( + f"Warning: e_ref={e_ref} < {self.e_ref_min}. Setting e_ref to 0 and using circular surrogate instead." + ) + return self.circ_sur( + M=M, + q=q, + reference_mean_anomaly=l_ref, + delta_t=delta_t, + t_start=t_start, + t_end=t_end, + times=new_t_grid, + remove_initial_phase=True, + return_orbital_variables=return_orbital_variables, + ) + eta = eta_from_q(q) + + e_node_vals = np.asarray( + [self.fit["e"][i]([eta, e_ref, l_ref]) for i in range(len(self.fit["e"]))] + ) + e_eim_res_amp = self.norm_factor["e"] * np.dot( + e_node_vals, self.eim_B["e"][:, self.ei_indices["res_amp"]] + ) + e_eim_res_phase = self.norm_factor["e"] * np.dot( + e_node_vals, self.eim_B["e"][:, self.ei_indices["res_phase"]] + ) + + mean_anomaly_offset_of_shifted_mean_anomaly = self.fit[ + "mean_anomaly_offset_fit" + ][0]( + [eta, e_ref, l_ref] + ) # This is the mean anomaly offset of the shifted mean anomaly + # Caution: It's important here that l_grid_sur is the un-truncated, full sur.l_grid_sur + l_eim_res_amp = ( + l_grid_sur[self.ei_indices["res_amp"]] + + mean_anomaly_offset_of_shifted_mean_anomaly + ) % (2 * np.pi) + l_eim_res_phase = ( + l_grid_sur[self.ei_indices["res_phase"]] + + mean_anomaly_offset_of_shifted_mean_anomaly + ) % (2 * np.pi) + + res_amp_node_vals = np.asarray( + [ + self.fit["res_amp"][i]([eta, e_eim_res_amp[i], l_eim_res_amp[i]]) + for i in range(len(self.fit["res_amp"])) + ] + ) + res_phase_node_vals = np.asarray( + [ + self.fit["res_phase"][i]([eta, e_eim_res_phase[i], l_eim_res_phase[i]]) + for i in range(len(self.fit["res_phase"])) + ] + ) + res_circ_phase_node_vals = np.asarray( + [ + self.fit["res_circ_phase"][i]([eta, e_ref, l_ref]) + for i in range(len(self.fit["res_circ_phase"])) + ] + ) + shifted_mean_anomaly_node_vals = np.asarray( + [ + self.fit["shifted_mean_anomaly"][i]([eta, e_ref, l_ref]) + for i in range(len(self.fit["shifted_mean_anomaly"])) + ] + ) + + lt_relation = self.norm_factor["shifted_mean_anomaly"] * np.dot( + shifted_mean_anomaly_node_vals, + self.eim_B["shifted_mean_anomaly"][:, start_idx_t:], + ) + + l_start = lt_relation[0] + start_idx_l = self._find_conservative_starting_truncation_index( + grid=l_grid_sur, val=l_start + ) + l_grid_sur = l_grid_sur[start_idx_l:] + + delta_amp_native = self.norm_factor["res_amp"] * np.dot( + res_amp_node_vals, self.eim_B["res_amp"][:, start_idx_l:] + ) + delta_phase_native = self.norm_factor["res_phase"] * np.dot( + res_phase_node_vals, self.eim_B["res_phase"][:, start_idx_l:] + ) + res_circ_phase_native = self.norm_factor["res_circ_phase"] * np.dot( + res_circ_phase_node_vals, self.eim_B["res_circ_phase"][:, start_idx_l:] + ) + + l_s = np.interp(new_t_grid, t_grid_sur, lt_relation) + delta_A = np.interp(l_s, l_grid_sur, delta_amp_native) + delta_phi = np.interp(l_s, l_grid_sur, delta_phase_native) + res_circ_phi = np.interp(l_s, l_grid_sur, res_circ_phase_native) + + delta_A *= mass_scaling_factor / _amp_correction_factor + amp = amp0_ + delta_A + phase = phase0_ + res_circ_phi + delta_phi + if remove_start_phase: + phase -= phase[0] + + if return_orbital_variables: + e_native = self.norm_factor["e"] * np.dot( + e_node_vals, self.eim_B["e"][:, start_idx_l:] + ) + e = np.interp(l_s, l_grid_sur, e_native) + + l = l_s + mean_anomaly_offset_of_shifted_mean_anomaly + l -= ( + 2 * np.pi * np.floor(l[0] / (2 * np.pi)) + ) # Bringing starting value of mean anomaly in [0, 2pi) + + x_node_vals = np.asarray( + [ + self.fit["x"][i]([eta, e_ref, l_ref]) + for i in range(len(self.fit["x"])) + ] + ) + x_native = self.norm_factor["x"] * np.dot( + x_node_vals, self.eim_B["x"][:, start_idx_l:] + ) + x = np.interp(l_s, l_grid_sur, x_native) + + orb_vars = {"e": e, "l": l, "x": x} + return new_t_grid, orb_vars, mode_from_amp_phase(amp, phase) + + return new_t_grid, mode_from_amp_phase(amp, phase) + + +_surrogate_instance = None + + +def _get_surrogate(): + global _surrogate_instance + if _surrogate_instance is None: + sur_data_dir = os.environ.get("ESIGMASUR_DATA_PATH", None) + if sur_data_dir is None: + raise RuntimeError( + "Surrogate data not found. Please set the ESIGMASUR_DATA_PATH " + "environment variable to the path of the surrogate data directory." + ) + ecc_sur_data_dir = os.path.join(sur_data_dir, "ecc_sur_data") + circ_sur_data_dir = os.path.join(sur_data_dir, "circ_sur_data") + if not os.path.isdir(ecc_sur_data_dir) or not os.path.isdir(circ_sur_data_dir): + raise RuntimeError( + f"Surrogate data not found. Please ensure that the environment variable ESIGMASUR_DATA_PATH points to the surrogate data directory." + ) + _surrogate_instance = EccentricSurrogate( + ecc_data_dir=ecc_sur_data_dir, circ_data_dir=circ_sur_data_dir + ) + print("Surrogate data loaded successfully.") + return _surrogate_instance diff --git a/build/lib/esigmapy/utils.py b/build/lib/esigmapy/utils.py new file mode 100644 index 0000000..4a80b49 --- /dev/null +++ b/build/lib/esigmapy/utils.py @@ -0,0 +1,189 @@ +# Copyright (C) 2025 Kaushik Paul, Akash Maurya +# +from __future__ import absolute_import, print_function + +import lal +import numpy as np + + +def f22_from_x(x, M): + """ + Parameters: + ----------- + x: Dimensionless PN parameter + M: Total mass of binary (in solar masses) + + Returns: + -------- + f22: "Orbit-averaged (2,2)-mode GW frequency" (in Hz) + """ + return x**1.5 / (M * lal.MTSUN_SI * lal.PI) + + +def x_from_f22(f22, M): + """ + Parameters: + ----------- + f22: "Orbit-averaged (2,2)-mode GW frequency" (in Hz) + M: Total mass of binary (in solar masses) + + Returns: + -------- + x: Dimensionless PN parameter + """ + return (M * lal.MTSUN_SI * lal.PI * f22) ** (2.0 / 3) + + +def f_ISCO_spin(mass1, mass2, spin1z, spin2z): + """ + Kerr ISCO frequency fitting formula for aligned-spins binary black holes + + Parameters: + ---------- + mass1, mass2 -- Binary's component masses (in solar masses) + spin1z, spin2z -- z-components of component dimensionless spins (lies in [0,1)) + + Returns: Kerr ISCO frequency (in Hz) + ------- + """ + Msun = lal.MTSUN_SI + k00 = -3.821158961 + k01 = -1.2019 + k02 = -1.20764 + k10 = 3.79245 + k11 = 1.18385 + k12 = 4.90494 + Zeta = 0.41616 + + m1 = mass1 * Msun + m2 = mass2 * Msun + M = m1 + m2 + eta = (m1 * m2) / (M**2) + z = 0 + + atot = (spin1z + spin2z * (m2 / m1) ** 2) / ((1 + (m2 / m1)) ** 2) + aeff = atot + Zeta * eta * (spin1z + spin2z) + + Z1 = 1 + ((1 - aeff**2) ** (1 / 3)) * ( + ((1 + aeff) ** (1 / 3)) + ((1 - aeff) ** (1 / 3)) + ) + Z2 = np.sqrt(3 * aeff**2 + Z1**2) + # riscocap = 3 + Z2 - ((aeff) / abs(aeff)) * np.sqrt((3 - Z1) * (3 + Z1 + 2 * Z2)) + riscocap = 3 + Z2 - np.sign(aeff) * np.sqrt((3 - Z1) * (3 + Z1 + 2 * Z2)) + # Equivalent to the above commented expression, and works also for aeff=0. + # For aeff=0, np.sign(aeff)=0, which is alright because its coefficient + # np.sqrt((3 - Z1) * (3 + Z1 + 2 * Z2)) is anyway 0 when aeff=0 because Z1 = 3 then. + Eiscocap = np.sqrt((1 - (2 / (3 * riscocap)))) + Liscocap = (2 / (3 * np.sqrt(3))) * (1 + 2 * np.sqrt(3 * riscocap - 2)) + + chichif = ( + atot + + eta * (Liscocap - 2 * atot * (Eiscocap - 1)) + + k00 * eta**2 + + k01 * eta**2 * aeff + + k02 * eta**2 * aeff**2 + + k10 * eta**3 + + k11 * eta**3 * aeff + + k12 * eta**3 * aeff**2 + ) + chichip = chichif + + Z1p = 1 + ((1 - chichip**2) ** (1 / 3)) * ( + ((1 + chichip) ** (1 / 3)) + ((1 - chichip) ** (1 / 3)) + ) + Z2p = np.sqrt(3 * chichip**2 + Z1p**2) + riscocapp = ( + 3 + Z2p - ((chichip) / abs(chichip)) * np.sqrt((3 - Z1p) * (3 + Z1p + 2 * Z2p)) + ) + + omegacap = 1 / (riscocapp ** (3 / 2) + chichip) + Scap = (1 / (1 - 2 * eta)) * (spin1z * m1**2 + spin2z * m2**2) / (M**2) + SS = (1 + Scap * (-0.00303023 - 2.00661 * eta + 7.70506 * eta**2)) / ( + 1 + Scap * (-0.67144 - 1.475698 * eta + 7.30468 * eta**2) + ) + + # Erad = (M* (0.0559745 * eta + 0.580951 * eta**2 - 0.960673 * eta**3 + 3.35241 * eta**4)* SS) + Erad_by_M = ( + 0.0559745 * eta + 0.580951 * eta**2 - 0.960673 * eta**3 + 3.35241 * eta**4 + ) * SS + + # Mfin = M * (1 - Erad / M) + Mfin = M * (1 - Erad_by_M) + fre = (1 / (1 + z)) * omegacap / (np.pi * Mfin) + + return 1 / 2 * fre + + +def get_peak_freqs(freq): + """ + Inputs + ------ + freq: Array of similar iterable of frequency values + + Outputs + ------- + peak_times: Times at which local maxima of frequency are attained + peak_freqs: Frequency at these times + """ + peaks, peak_times = [], [] + fvals = freq.data + + for idx, finst in enumerate(fvals): + if idx == 0 or idx == len(fvals) - 1: + continue + + if ((fvals[idx - 1]) < (finst)) and ((fvals[idx + 1]) < (finst)): + peaks.append(finst) + peak_times.append(freq.sample_times[idx]) + + return np.array(peak_times), np.array(peaks) + + +def get_polarizations_from_multipoles( + waveform_multipoles, inclination, coa_phase, verbose=False +): + """ + Returns GW polarizations from complex GW multipoles + + Parameters: + ----------- + waveform_multipoles -- Dictionary of complex Waveform Multipoles + indexed by their `(el, em)` values + 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) + delta_t -- Waveform's time grid-spacing (in s) + verbose -- Verbosity level. Available values are: 0, 1, 2 + + Returns: + -------- + hp, hc -- Plus and cross GW polarizations + """ + available_modes = list(waveform_multipoles.keys()) + + # Initialize with zeros, preserving data type and precision + try: + hp = waveform_multipoles[available_modes[0]].real * 0 + hc = waveform_multipoles[available_modes[0]].real * 0 + except: + hp = waveform_multipoles[available_modes[0]].real() * 0 + hc = waveform_multipoles[available_modes[0]].real() * 0 + + for el, em in waveform_multipoles: + ylm = lal.SpinWeightedSphericalHarmonic(inclination, coa_phase, -2, el, em) + glm = waveform_multipoles[(el, em)] * ylm + if verbose > 4: + print(f"Adding mode {el}, {em} with ylm = {ylm}", flush=True) + print( + f"... adding {waveform_multipoles[(el, em)]}, {glm}", + flush=True, + ) + print(f"... after adding, hp={hp}, hc={hc}", flush=True) + try: + hp = hp + glm.real + hc = hc - glm.imag + except: + hp = hp + glm.real() + hc = hc - glm.imag() + + return hp, hc diff --git a/esigmapy.egg-info/PKG-INFO b/esigmapy.egg-info/PKG-INFO new file mode 100644 index 0000000..7a3e778 --- /dev/null +++ b/esigmapy.egg-info/PKG-INFO @@ -0,0 +1,174 @@ +Metadata-Version: 2.4 +Name: esigmapy +Version: 2025.3.7 +Summary: A collection of tools for generating ESIGMA waveform multipoles and polarizations. +Home-page: https://github.com/gwnrtools/esigmapy +Author: Prayush Kumar +Author-email: prayush.kumar@gmail.com +License: GPL +License-File: LICENSE +Requires-Dist: lscsoft_glue>=2.0.0 +Requires-Dist: matplotlib>=2.1 +Requires-Dist: numpy +Requires-Dist: pycbc +Requires-Dist: scipy>=1.2.3 +Requires-Dist: tpi-splines +Requires-Dist: numba +Dynamic: author +Dynamic: author-email +Dynamic: description +Dynamic: home-page +Dynamic: license +Dynamic: license-file +Dynamic: requires-dist +Dynamic: summary + +# ESIGMAPy: a Python package to generate ESIGMA waveforms + +`ESIGMAPy` provides access to two eccentric waveform models: + +| Model | Description | +|---|---| +| `ESIGMAHM` | Eccentric, aligned-spin IMR waveform with higher modes (arXiv:[2409.13866](https://arxiv.org/abs/2409.13866)) | +| `ESIGMASur` | Surrogate model of the (2,2) GW-mode of `ESIGMAHM` (arXiv:[2510.00116](https://arxiv.org/abs/2510.00116))| + +--- + +## :rocket: Quick Start + +Install via: + +```bash +pip install git+https://github.com/gwnrtools/esigmapy.git +``` +Then to use: + * `ESIGMAHM`: requires custom `LALSuite` installation and `NRSur7dq4` surrogate data download ([installation instructions below](https://github.com/gwnrtools/esigmapy/tree/master?tab=readme-ov-file#blue_square-esigmahm)). + * `ESIGMASur`: requires downloading `ESIGMASur` surrogate data ([installation instructions below](https://github.com/gwnrtools/esigmapy/tree/master?tab=readme-ov-file#green_square-esigmasur)) + +Usage instructions at: + * `ESIGMAHM` [tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMA_tutorial.ipynb) + * `ESIGMASur` [tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMASur_tutorial.ipynb) +*** +## :blue_square: ESIGMAHM + +`ESIGMAHM` is an eccentric, aligned-spin, inspiral-merger-ringdown (IMR) waveform model with higher-order modes. It is composed of two pieces: + +* **Inspiral piece (`InspiralESIGMAHM`):** It comes from a combination of post-Newtonian theory, self-force, and black hole perturbation theory. +* **Plunge-merger-ringdown piece**: Assuming moderate starting eccentricities that decay by the late inspiral, we use the quasi-circular (QC) NR surrogate `NRSur7dq4` for the plunge-merger-ringdown piece for `ESIGMAHM`. + + (**Note:** We also allow other QC `LALSuite` waveform models to be used as the plunge-merger-ringdown piece; see the optional argument `merger_ringdown_approximant` in the generation functions `get_imr_esigma_waveform` and `get_imr_esigma_modes` [here](https://github.com/gwnrtools/esigmapy/blob/master/esigmapy/generator.py). However, the default and the most tested choice is `NRSur7dq4`.) + +The full IMR waveform `ESIGMAHM` is produced by smoothly attaching the inspiral piece `InspiralESIGMAHM` to the plunge-merger-ringdown piece `NRSur7dq4`. This attachment is facilitated by `ESIGMAPy`. + +Using `ESIGMAHM` therefore requires installing 1. `InspiralESIGMAHM`, 2. `NRSur7dq4`, and 3. `ESIGMAPy`. + +### Installing `InspiralESIGMAHM` + +* **Getting the source code:** The `LALSuite` fork containing the implementation of `InspiralESIGMAHM` is currently private, but interested users are welcome to write to the developers for access at esigmahm@icts.res.in. + + Clone the [`LALSuite` fork](https://git.ligo.org/kaushik.paul/lalsuite/-/tree/enigma_spins_v2023?ref_type=heads) and checkout the commit with tag `ESIGMAHMv1`: + + ```bash + git clone https://git.ligo.org/kaushik.paul/lalsuite.git + cd lalsuite + git checkout ESIGMAHMv1 + ``` +* **Installation:** + - It is advised to create a conda environment using [the `igwn` yaml file](https://computing.docs.ligo.org/conda/environments/igwn-py311/) and install the above mentioned fork of `LALSuite` inside it to minimize the dependency issues. Please remove `_x86_64-microarch-level=3=3_haswell` from the yaml file in case you get errors related to microarchitecture mismatch. + - Activate your `conda` environment. Make sure that the `swig` version in this environment is below `4.2.1` (you can check this by running `conda list swig`). If not, install its version `4.2.0` by running `conda install -c conda-forge swig=4.2.0`. + - Create a directory where you want to install ESIGMA. Let the absolute path of this directory be `/path/to/esigmahm`. + - Go back inside the above cloned `LALSuite` fork, and run the following commands: + + ```bash + ./00boot + ./configure --prefix="/path/to/esigmahm" --enable-swig-python --disable-laldetchar --disable-lalpulsar --disable-lalapps --enable-mpi=yes --enable-hdf5 CFLAGS="-Wno-error" CXXFLAGS="-Wno-error" CPPFLAGS="-Wno-error" + make + make install + ``` +* **Configuring `conda` to source this `LALSuite` fork automatically on activating the `conda` environment** + - With your `conda` environment activated, run the following commands: + ```bash + cd $CONDA_PREFIX/etc/conda/activate.d + ln -s /path/to/esigmahm/etc/lalsuite-user-env.sh + ``` + +### Installing `NRSur7dq4` +* Download the `NRSur7dq4` [data file](https://git.ligo.org/lscsoft/lalsuite-extra/-/blob/master/data/lalsimulation/NRSur7dq4.h5) in some directory (for `LALSuite` versions >= 7.25, download [this data file](https://git.ligo.org/waveforms/software/lalsuite-waveform-data/-/blob/main/waveform_data/NRSur7dq4_v1.0.h5?ref_type=heads) instead). Let's say the absolute path to this directory is `/path/to/NRSur7dq4`. +* Append the path of this directory to the shell environment variable `LAL_DATA_PATH` by running: `export LAL_DATA_PATH="$LAL_DATA_PATH:/path/to/NRSur7dq4"` +* To avoid performing the above step in every new terminal session, either add the above command to your `.bashrc` file, or follow the instructions [here](http://gitlab.icts.res.in/akash.maurya/Installation-instructions/wikis/conda-tricks), replacing `PYTHONPATH` with `LAL_DATA_PATH`, to set this environment variable automatically on activating your `conda` environment. + +### Installing `ESIGMAPy` +* Activate your `conda` environment and install `ESIGMAPy` by running: `pip install esigmapy`. + +*** +### Trying out `ESIGMAHM` +The instructions to generate `ESIGMAHM` waveforms and the various functionalities that it offers are detailed in [this tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMA_tutorial.ipynb). + +### Trying out `ESIGMAHM` via `gwsignal` +`ESIGMAHM` can now also be accessed via [`gwsignal`](https://waveforms.docs.ligo.org/reviews/lalsuite/lalsimulation/gwsignal/index.html). See [this notebook](https://github.com/gwnrtools/esigmapy/blob/cfe881a6052f1e9a7c3e066d9fd32462a3af2c89/notebooks/esigma_gwsignal.ipynb) for usage instructions. + +*** + +## :green_square: ESIGMASur + +`ESIGMASur` is a fast time-domain surrogate model of the (2,2)-mode of `ESIGMAHM`. + +### Installation +* Install `ESIGMAPy` by running: + ```bash + pip install git+https://github.com/gwnrtools/esigmapy.git + ``` + It **does not** require the custom LALSuite installation above. + + **Note:** `ESIGMASur` currently supports Python <= 3.12, owing to a compatibility restriction in [`tpi-splines`](https://github.com/mpuerrer/TPI), a dependency of `ESIGMASur`. + +* Download the surrogate data files of `ESIGMASur`, which can be found [on the repo](https://github.com/gwnrtools/esigmapy/tree/master/esigmapy/surrogate/data). Next, set the shell environment variable `ESIGMASUR_DATA_PATH` to the directory where you keep these surrogate data files by running: `export ESIGMASUR_DATA_PATH="/path/to/ESIGMASur"`. + +* Using the inspiral-only surrogate `InspiralESIGMASur` would not require any further dependencies. However, its hybridized IMR version `IMRESIGMASur` will require downloading the NR surrogate data file ([installation instructions above](https://github.com/gwnrtools/esigmapy/tree/master#installing-nrsur7dq4)). + +### Trying out `ESIGMASur` +The usage instructions and the various functionalities of `ESIGMASur` are detailed in [this tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMASur_tutorial.ipynb). + +*** +## 📖 Citation +* If you use `ESIGMAHM` in your work, please cite: + + Paul et al., _"ESIGMAHM: An Eccentric, Spinning inspiral-merger-ringdown waveform model with Higher Modes for the detection and characterization of binary black holes"_, [PhysRevD.111.084074](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.111.084074), arXiv:[2409.13866](https://arxiv.org/abs/2409.13866) (2024) + + ```bibtex + @article{Paul:2024ujx, + author = "Paul, Kaushik and Maurya, Akash and Henry, Quentin and Sharma, Kartikey and Satheesh, Pranav and Divyajyoti and Kumar, Prayush and Mishra, Chandra Kant", + title = "{Eccentric, spinning, inspiral-merger-ringdown waveform model with higher modes for the detection and characterization of binary black holes}", + eprint = "2409.13866", + archivePrefix = "arXiv", + primaryClass = "gr-qc", + doi = "10.1103/PhysRevD.111.084074", + journal = "Phys. Rev. D", + volume = "111", + number = "8", + pages = "084074", + year = "2025" + } + ``` + `ESIGMAHM` is built on the `ENIGMA` framework, which was developed in arXiv:[1609.05933](https://arxiv.org/abs/1609.05933), arXiv:[1711.06276](https://arxiv.org/abs/1711.06276), arXiv:[2008.03313](https://arxiv.org/abs/2008.03313). In addition to citing `ESIGMAHM`, please consider citing them as well. + +* If you use `ESIGMASur` in your work, please cite: + + Maurya et al., _"Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates"_, arXiv:[2510.00116](https://arxiv.org/abs/2510.00116) (2025) + + ```bibtex + @article{Maurya:2025shc, + author = "Maurya, Akash and Kumar, Prayush and Field, Scott E. and Mishra, Chandra Kant and Nee, Peter James and Paul, Kaushik and Pfeiffer, Harald P. and Ravichandran, Adhrit and Varma, Vijay", + title = "{Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates}", + eprint = "2510.00116", + archivePrefix = "arXiv", + primaryClass = "gr-qc", + journal = {}, + month = "9", + year = "2025", + } + ``` + +*** +## :mailbox_with_mail: Contact Us +If you have any questions, issues, or suggestions regarding the model, feel free to reach out to us at esigmahm@icts.res.in! diff --git a/esigmapy.egg-info/SOURCES.txt b/esigmapy.egg-info/SOURCES.txt new file mode 100644 index 0000000..80b4a79 --- /dev/null +++ b/esigmapy.egg-info/SOURCES.txt @@ -0,0 +1,23 @@ +LICENSE +README.md +setup.py +esigmapy/__init__.py +esigmapy/blend.py +esigmapy/generator.py +esigmapy/legacy.py +esigmapy/utils.py +esigmapy.egg-info/PKG-INFO +esigmapy.egg-info/SOURCES.txt +esigmapy.egg-info/dependency_links.txt +esigmapy.egg-info/entry_points.txt +esigmapy.egg-info/requires.txt +esigmapy.egg-info/top_level.txt +esigmapy/python_codes/__init__.py +esigmapy/python_codes/esigma_go_terms.py +esigmapy/python_codes/esigma_go_terms_draft.py +esigmapy/python_codes/esigma_pn_inspiral.py +esigmapy/python_codes/esigma_pn_main.py +esigmapy/python_codes/generator_python.py +esigmapy/surrogate/__init__.py +esigmapy/surrogate/generator.py +esigmapy/surrogate/surrogate.py \ No newline at end of file diff --git a/esigmapy.egg-info/dependency_links.txt b/esigmapy.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/esigmapy.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/esigmapy.egg-info/entry_points.txt b/esigmapy.egg-info/entry_points.txt new file mode 100644 index 0000000..e1c29d6 --- /dev/null +++ b/esigmapy.egg-info/entry_points.txt @@ -0,0 +1,2 @@ +[pycbc.waveform.td] +esigma = esigmapy:pycbc_esigma diff --git a/esigmapy.egg-info/requires.txt b/esigmapy.egg-info/requires.txt new file mode 100644 index 0000000..612f901 --- /dev/null +++ b/esigmapy.egg-info/requires.txt @@ -0,0 +1,7 @@ +lscsoft_glue>=2.0.0 +matplotlib>=2.1 +numpy +pycbc +scipy>=1.2.3 +tpi-splines +numba diff --git a/esigmapy.egg-info/top_level.txt b/esigmapy.egg-info/top_level.txt new file mode 100644 index 0000000..4475386 --- /dev/null +++ b/esigmapy.egg-info/top_level.txt @@ -0,0 +1 @@ +esigmapy diff --git a/esigmapy/__pycache__/__init__.cpython-311.pyc b/esigmapy/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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b/esigmapy/generator.py @@ -38,8 +38,8 @@ def find_x_for_y(x, y, y0): return x[abs(y - y0).argmin()] itime = time.perf_counter() - retval = ls.SimInspiralENIGMADynamics( - mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, False + retval = ls.SimInspiralESIGMADynamics( + mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, ) t, x, e, l, phi, phidot, r, rdot = retval[:8] t.data.data *= lal.MTSUN_SI @@ -116,8 +116,8 @@ def eccentricity_at_reference_frequency( ): """ """ itime = time.perf_counter() - retval = ls.SimInspiralENIGMADynamics( - mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, False + retval = ls.SimInspiralESIGMADynamics( + mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, ) t, x, e, l, phi, phidot, r, rdot = retval[:8] t.data.data *= lal.MTSUN_SI @@ -243,7 +243,7 @@ def get_inspiral_esigma_modes( itime = time.perf_counter() f_start = f_ref - retval = ls.SimInspiralENIGMADynamics( + retval = ls.SimInspiralESIGMADynamics( mass1, mass2, spin1z, @@ -253,7 +253,6 @@ def get_inspiral_esigma_modes( mean_anomaly, 1e-12, 1 / delta_t, - False, ) if f_ref < f_lower: @@ -283,7 +282,7 @@ def get_inspiral_esigma_modes( modes = {} distance *= 1.0e6 * lal.PC_SI # Mpc to SI conversion for el, em in modes_to_use: - modes[(el, em)] = ls.SimInspiralENIGMAModeFromDynamics( + modes[(el, em)] = ls.SimInspiralESIGMAModeFromDynamics( el, em, t.data, diff --git a/esigmapy/python_codes/__pycache__/__init__.cpython-311.pyc b/esigmapy/python_codes/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..65e5fd198ce3f277942fb98b06107eac1e7e1aa9 GIT binary patch literal 180 zcmZ3^%ge<81U}4uVloS(1^T7oVJ;l3J`EAD@|*SrQ+wS5Wzj k!zMRBr8Fniu80+AF39F$ejxFInURt40|SgGVg`x<0CVv!L;wH) literal 0 HcmV?d00001 diff --git a/esigmapy/python_codes/__pycache__/esigma_go_terms_draft.cpython-311.pyc b/esigmapy/python_codes/__pycache__/esigma_go_terms_draft.cpython-311.pyc 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zjE#=wtg>Pd>rKOmD|fS_2<1GJ(3yo^C+y)!J??*|doEcyFMeYd8b?<4BP)lUqO@qG zICxc2y`7yNor3bW_`lPWza?;kzz+y8jXF9(Rs4tm(`h5+AJ*7eua`Ax19Ls@9az&&uyD<%`PUN*q97Ot#{B2?|8 z=SFbU4By)jB4z{S^!50&PK3Ks?3=CQv!O)J)9AYm>_su4^Eg(7-%-Y3s&8GE3)3Q!k1Q{!|>Vi>TGReHdvKy z@1}1{XO{2DHndQ=JOL`#j#o=5GaKMD9?r$er4a|^Lg_#vjXrqAm*fAA*#r^j;~;Bg F{}0(2D#`!= literal 0 HcmV?d00001 diff --git a/esigmapy/python_codes/esigma_go_terms.py b/esigmapy/python_codes/esigma_go_terms.py index 199feeb..e0057f2 100644 --- a/esigmapy/python_codes/esigma_go_terms.py +++ b/esigmapy/python_codes/esigma_go_terms.py @@ -3095,3 +3095,249 @@ def hl_4_m_min4( # H43 +def hGO_4_m_3( + mass: float, + Nu: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: KeplerVars, + ) -> complex: + delta = np.sqrt(1 - 4 * Nu) + kappa1 = 1.0 + kappa2 = 1.0 + + if vpnorder == 3: + return ( + 0.16666666666666666j * delta * mass * (-1 + 2 * Nu) * PhiDOT * + (4 * mass + + r * (23 * params.PhiDOT2 * params.r2 + + 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) + / (np.sqrt(70) * r) + ) + + elif vpnorder == 4: + return ( + -0.041666666666666664j * np.sqrt(0.35714285714285715) * + params.Mtot2 * Nu * + (4 * mass + + r * (23 * params.PhiDOT2 * params.r2 + + 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) * + ((-1 + delta) * S1z + S2z + delta * S2z) + / params.r3 + ) + + elif vpnorder == 5: + return ( + 0.0012626262626262627j * delta * mass * PhiDOT * + (2 * params.Mtot2 * (972 - 2293 * Nu + 1398 * params.eta2) + + 2 * mass * r * + ((1788 - 9077 * Nu + 13416 * params.eta2) * params.PhiDOT2 * params.r2 + + 3j * (-2796 + 5299 * Nu + 1622 * params.eta2) * PhiDOT * r * rDOT - + 2 * (-1200 + 2545 * Nu + 162 * params.eta2) * params.rDOT2) - + 3 * params.r2 * + ((-524 - 489 * Nu + 6392 * params.eta2) * params.PhiDOT4 * params.r4 + + 4j * (796 - 1864 * Nu + 133 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + + 42 * (-51 + 94 * Nu + 56 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 4j * (-229 + 366 * Nu + 358 * params.eta2) * PhiDOT * r * params.rDOT3 - + 4 * (-43 + 62 * Nu + 80 * params.eta2) * params.rDOT4)) + / (np.sqrt(70) * params.r2) + ) + + elif vpnorder == 6: + term1 = ( + delta * params.Mtot2 * Nu * PhiDOT * + (6 * mass * (181 * PhiDOT * r - 89j * rDOT) + + r * (4847 * params.PhiDOT3 * params.r3 - + 7338j * params.PhiDOT2 * params.r2 * rDOT - + 408 * PhiDOT * r * params.rDOT2 + + 112j * params.rDOT3)) + / (180.0 * np.sqrt(70) * params.r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + -0.0006313131313131314j * params.Mtot2 * + (2 * params.Mtot2 * + ((-440 + 6801 * Nu - 1428 * params.eta2 + + delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S1z + + (440 - 6801 * Nu + 1428 * params.eta2 + + delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S2z) - + 2 * mass * r * + (-3j * PhiDOT * r * rDOT * + (delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S1z - + 5 * (264 - 311 * Nu + 823 * params.eta2) * S1z + + delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S2z + + 5 * (264 - 311 * Nu + 823 * params.eta2) * S2z) - + 2 * params.rDOT2 * + ((220 + 1659 * Nu - 1512 * params.eta2 + + delta * (220 - 3067 * Nu + 240 * params.eta2)) * S1z + + (-220 - 1659 * Nu + 1512 * params.eta2 + + delta * (220 - 3067 * Nu + 240 * params.eta2)) * S2z) + + 2 * params.PhiDOT2 * params.r2 * + ((1826 - 19530 * Nu + 20145 * params.eta2 + + delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S1z + + (-1826 + 19530 * Nu - 20145 * params.eta2 + + delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S2z)) - + 3 * params.r2 * + (3080j * params.PhiDOT3 * params.r3 * rDOT * + ((1 + delta) * S1z + (-1 + delta) * S2z) + + 2 * params.eta2 * + (129 * params.PhiDOT2 * params.r2 * params.rDOT2 * + (41 * S1z + 23 * delta * S1z - 41 * S2z + 23 * delta * S2z) - + 2 * params.rDOT4 * + (149 * S1z + 75 * delta * S1z - 149 * S2z + 75 * delta * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + (925 * S1z + 491 * delta * S1z - 925 * S2z + 491 * delta * S2z) - + 1j * params.PhiDOT3 * params.r3 * rDOT * + (-2105 * S1z + 753 * delta * S1z + 2105 * S2z + 753 * delta * S2z) + + params.PhiDOT4 * params.r4 * + (11617 * S1z + 4847 * delta * S1z - 11617 * S2z + 4847 * delta * S2z)) + + Nu * (16 * params.PhiDOT4 * params.r4 * + (-413 * S1z + 127 * delta * S1z + 413 * S2z + 127 * delta * S2z) - + 2 * params.rDOT4 * + (-301 * S1z + 213 * delta * S1z + 301 * S2z + 213 * delta * S2z) + + 2j * PhiDOT * r * params.rDOT3 * + (-1625 * S1z + 1009 * delta * S1z + 1625 * S2z + 1009 * delta * S2z) + + 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + (-2587 * S1z + 1267 * delta * S1z + 2587 * S2z + 1267 * delta * S2z) - + 2j * params.PhiDOT3 * params.r3 * rDOT * + (3635 * S1z + 7981 * delta * S1z - 3635 * S2z + 7981 * delta * S2z)))) + / (np.sqrt(70) * params.r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + # Henry et al. ecc+spin terms + spin_terms = ( + 5005 * params.Mtot2 * + (-24 * PhiDOT * params.r2 * params.rDOT2 * + (1j * (-11 + 48 * Nu) * S1z + + 6 * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + + S2z * (11j - 48j * Nu - + 6 * (-5 + 4 * kappa2) * params.eta2 * S2z)) + + 4 * r * params.rDOT3 * + ((-11 + 93 * Nu) * S1z - + 6j * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + + S2z * (11 - 93 * Nu + + 6j * (-5 + 4 * kappa2) * params.eta2 * S2z)) + + 4 * mass * rDOT * + ((22 - 111 * Nu) * S1z + + 6j * (15 + 8 * kappa1) * params.eta2 * params.S1z2 + + S2z * (-22 + 111 * Nu - + 6j * (15 + 8 * kappa2) * params.eta2 * S2z)) + + 6j * params.PhiDOT2 * params.r3 * rDOT * + (1j * (-121 + 963 * Nu) * S1z + + 6 * (-55 * params.eta2 + + kappa1 * (-5 + 30 * Nu + 4 * params.eta2)) * params.S1z2 + + S2z * (121j + 30 * kappa2 * S2z - + 6 * (-55 + 4 * kappa2) * params.eta2 * S2z - + 9 * Nu * (107j + 20 * kappa2 * S2z))) + + 2 * mass * PhiDOT * r * + (-1j * (-121 + 633 * Nu) * S1z + + 6 * (180 * params.eta2 + + kappa1 * (9 - 54 * Nu + 28 * params.eta2)) * params.S1z2 - + S2z * (121j + 54 * kappa2 * S2z + + 24 * (45 + 7 * kappa2) * params.eta2 * S2z - + 3 * Nu * (211j + 108 * kappa2 * S2z))) + + params.PhiDOT3 * params.r4 * + (-1j * (-649 + 4767 * Nu) * S1z + + 6 * (820 * params.eta2 + + kappa1 * (75 - 450 * Nu + 364 * params.eta2)) * params.S1z2 - + S2z * (649j + 450 * kappa2 * S2z + + 24 * (205 + 91 * kappa2) * params.eta2 * S2z - + 3 * Nu * (1589j + 900 * kappa2 * S2z)))) + ) + + orbital_terms = ( + delta * + (2 * mass * PhiDOT * params.r3 * + (10 * (234744 - 1010534 * Nu + 1024443 * params.eta2 + + 5451096 * params.eta3) * params.PhiDOT4 * params.r4 - + 3j * (-2426804 + 1512854 * Nu + 4994115 * params.eta2 + + 610960 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + + (-30341028 + 23936528 * Nu + 89326545 * params.eta2 + + 19329660 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + + 21j * (-668008 + 803028 * Nu + 1908955 * params.eta2 + + 540370 * params.eta3) * PhiDOT * r * params.rDOT3 - + 14 * (-172143 + 155683 * Nu + 680580 * params.eta2 + + 111840 * params.eta3) * params.rDOT4) - + 105 * PhiDOT * params.r4 * + ((-8280 + 24681 * Nu - 151973 * params.eta2 + + 624074 * params.eta3) * params.PhiDOT6 * params.r6 + + 2j * (-32208 + 248485 * Nu - 524074 * params.eta2 + + 24546 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + + 2 * (48924 - 239802 * Nu + 137447 * params.eta2 + + 358156 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + + 4j * (174 + 24488 * Nu - 102039 * params.eta2 + + 44882 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + + 3 * (10455 - 56490 * Nu + 84504 * params.eta2 + + 54016 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 + + 2j * (11175 - 52698 * Nu + 57436 * params.eta2 + + 60808 * params.eta3) * PhiDOT * r * params.rDOT5 - + 6 * (829 - 3726 * Nu + 3480 * params.eta2 + + 4640 * params.eta3) * params.rDOT6) + + params.Mtot2 * r * + (20020 * params.rDOT3 * (S1z + S2z) * + (-11 + 71 * Nu + 30j * params.eta2 * (S1z + S2z)) - + 52 * PhiDOT * r * params.rDOT2 * + (129150 * params.eta3 + + 7 * Nu * (31961 + 8580j * S1z + 8580j * S2z) - + 3j * (32671j + 8470 * S1z + 8470 * S2z) - + 35 * params.eta2 * + (33313 + 1980 * params.S1z2 + 3960 * S1z * S2z + 1980 * params.S2z2)) + + params.PhiDOT3 * params.r3 * + (-10566168 - 70869960 * params.eta3 + + 3248245j * S1z + 2252250 * kappa1 * params.S1z2 + + 3248245j * S2z + 2252250 * kappa2 * params.S2z2 - + 7 * Nu * + (4818166 + 2480335j * S1z + 1287000 * kappa1 * params.S1z2 + + 2480335j * S2z + 1287000 * kappa2 * params.S2z2) + + 3850 * params.eta2 * + (38873 + 78 * (7 + 30 * kappa1) * params.S1z2 - + 3588 * S1z * S2z + + 78 * (7 + 30 * kappa2) * params.S2z2)) - + 2j * params.PhiDOT2 * params.r2 * rDOT * + (6531280 * params.eta3 + + 3 * (5382288 + 605605j * S1z + 150150 * kappa1 * params.S1z2 + + 605605j * S2z + 150150 * kappa2 * params.S2z2) + + 210 * params.eta2 * + (322144 + 2145 * (1 + 4 * kappa1) * params.S1z2 - + 12870 * S1z * S2z + + 2145 * (1 + 4 * kappa2) * params.S2z2) - + 21 * Nu * + (2826484 + 85800 * kappa1 * params.S1z2 + + 515515j * S2z + 85800 * kappa2 * params.S2z2 - + 5005 * S1z * (-103j + 60 * S2z)))) - + 26 * params.Mtot3 * + (770 * rDOT * + (-90j * params.eta2 * params.S1z2 + + S2z * (-22 + 67 * Nu - 90j * params.eta2 * S2z) + + S1z * (-22 + Nu * (67 + 300j * S2z) - 180j * params.eta2 * S2z)) + + PhiDOT * r * + (-38076 + 174720 * params.eta3 - + 46585j * S1z - 20790 * kappa1 * params.S1z2 - + 46585j * S2z - 20790 * kappa2 * params.S2z2 - + 1260 * params.eta2 * + (158 + 33 * (5 + 2 * kappa1) * params.S1z2 + + 198 * S1z * S2z + + 33 * (5 + 2 * kappa2) * params.S2z2) + + 7 * Nu * + (6188 + 11880 * kappa1 * params.S1z2 + + 21505j * S2z + 11880 * kappa2 * params.S2z2 + + 55 * S1z * (391j + 744 * S2z))))) + ) + + return ( + -1.3875013875013875e-6j * mass * + (spin_terms + orbital_terms) + / (np.sqrt(70) * params.r4) + ) + + else: + return Complex(0.0, 0.0) \ No newline at end of file diff --git a/esigmapy/python_codes/esigma_go_terms_draft.py b/esigmapy/python_codes/esigma_go_terms_draft.py new file mode 100644 index 0000000..c24988c --- /dev/null +++ b/esigmapy/python_codes/esigma_go_terms_draft.py @@ -0,0 +1,1696 @@ +import numpy as np +from dataclasses import dataclass + +# Storing constants +M_PI = np.pi +M_PI2 = M_PI ** 2 +LOG2 = np.log(2) + +#---------------------------------------------------- +def rect(r: float, phi: float) -> complex: + """Convert polar coordinates to rectangular form.""" + return r * np.exp(1j * phi) +#---------------------------------------------------- + +@dataclass +class CommonVars: + 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): + """Similarly for QC functions""" + def decorator(func): + H_QC_LM_FUNCS[(l, m)] = func + return func + return decorator + +# 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 + np.log(64.0) + 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) + + +# H33 + +# def hGO_3_m_3( +# total_mass: float, +# eta: float, +# r: float, +# rDOT: float, +# PhiDOT: float, +# vpnorder: int, +# S1z: float, +# S2z: float, +# x: float, +# ) -> complex: +# delta = np.sqrt(1 - 4 * eta) +# kappa1 = 1.0 +# kappa2 = 1.0 +# r0 = 2 * total_mass / np.exp(0.5) + +# 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) + +# def hQC_3_m_3( +# 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 + +# 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 * np.log(2.0) + 60.0 * np.log(3.0) + +# 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 * np.log(2.0) + 43740.0 * eta * np.log(2.0) - +# 116640.0 * np.log(3.0) + 43740.0 * eta * np.log(3.0) + +# 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 * np.log(2.0) + 120.0j * delta * np.log(3.0) + +# 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 * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + +# 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - +# 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * eta * S1z * np.log(2.0) + +# 396900.0 * delta * eta * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - +# 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * eta * S2z * np.log(2.0) + +# 396900.0 * delta * eta * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - +# 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + +# 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - +# 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * eta * S1z * np.log(3.0) + +# 396900.0 * delta * eta * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - +# 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * eta * S2z * np.log(3.0) + +# 396900.0 * delta * eta * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + +# 952560.0j * delta * (np.log(3.0)**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 * np.log(2.0) * logx + +# 2857680.0j * delta * np.log(3.0) * logx + +# 2143260.0j * delta * (logx**2) + +# log_b0_term +# )) +# return numerator_7 / (2352.0 * np.sqrt(210.0)) + +# else: +# return complex(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: 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 index c16d6b7..444cc8d 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -295,7 +295,7 @@ def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: ## --------- 2PN terms ------------ -def x_dot_2pn(e: float, eta: float) -> float: +def x_dot_2pn(e: float, eta: float, x: float) -> float: """Eq. (A29)""" e2 = e * e @@ -1241,7 +1241,7 @@ def e_dot_3pn(e: float, eta: float, x: float) -> float: e_fact = 1.0 - e_pow_2 sqrt_e_fact = np.sqrt(e_fact) - pi_pow_2 = np.np.pi * np.np.pi + pi_pow_2 = np.pi * np.pi zero_term = ( 7742634967.0 / 891000.0 @@ -1312,7 +1312,7 @@ def e_dot_3pn(e: float, eta: float, x: float) -> float: + 1147147.0 * e_pow_4 / 15960.0 + 61311.0 * e_pow_6 / 21280.0 ) - * np.np.log(x * (1.0 + sqrt_e_fact) / (2.0 * e_fact)) + * np.log(x * (1.0 + sqrt_e_fact) / (2.0 * e_fact)) ) pre_factor = -e * eta / (e_fact**5 * sqrt_e_fact) @@ -2497,6 +2497,28 @@ def rel_sep_3pn_SS(e: float, u: float, m1: float, m2: float, S1z: float, S2z: fl 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 + + 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) * np.sqrt(x) + + 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 * np.sqrt(x) + + rel_sep_3pn(e, u, eta) * x * x + + rel_sep_3pn_SS(e, u, m1, m2, S1z, S2z) * x * x) + + # ============ Utility functions ========================================= @@ -2701,10 +2723,10 @@ 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.np.pi: - mean_anomaly -= 2 * np.np.pi - while mean_anomaly < - np.np.pi: - mean_anomaly += 2 * np.np.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 @@ -2745,10 +2767,10 @@ def pn_kepler_equation(eta, x, e, l): return u # range reduction of the l - while l > np.np.pi: - l -= 2 * np.np.pi - while l < - np.np.pi: - l += 2 * np.np.pi + 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: @@ -2761,11 +2783,11 @@ def pn_kepler_equation(eta, x, e, l): u = mikkola_finder(e, l) # high eccentricity case - if (e > 0.8) and (l < np.np.pi / 3.0): + 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.np.pi: + if l < np.pi: trial = pow1_3(6.0 * l) u = trial diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py new file mode 100644 index 0000000..9675e6e --- /dev/null +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -0,0 +1,510 @@ +import numpy as np +from scipy.integrate import ode +from scipy.interpolate import CubicSpline +import math +from .esigma_pn_inspiral import * +from .esigma_go_terms_draft import * +import lal + +# Constants (LAL equivalents) +LAL_PI = math.pi +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 +# # ------------------------------------------------------------------ # +# # Helper: populate powers of orbital variables +# # (mirrors PopulateKeplerParams + kepler_vars struct) +# # ------------------------------------------------------------------ # +# @dataclass +# class KeplerVars: +# eta: float = 0.0 +# eta2: float = 0.0 +# eta3: float = 0.0 +# eta4: float = 0.0 +# eta5: float = 0.0 +# eta6: float = 0.0 + +# Mtot: float = 0.0 +# Mtot2: float = 0.0 +# Mtot3: float = 0.0 +# Mtot4: float = 0.0 +# Mtot5: float = 0.0 +# Mtot6: float = 0.0 + +# S1z: float = 0.0 +# S1z2: float = 0.0 +# S1z3: float = 0.0 +# S1z4: float = 0.0 +# S1z5: float = 0.0 +# S1z6: float = 0.0 +# S1z7: float = 0.0 +# S1z8: float = 0.0 +# S1z9: float = 0.0 +# S1z10: float = 0.0 +# S1z11: float = 0.0 +# S1z12: float = 0.0 +# S1z13: float = 0.0 +# S1z14: float = 0.0 +# S1z15: float = 0.0 +# S1z16: float = 0.0 + +# S2z: float = 0.0 +# S2z2: float = 0.0 +# S2z3: float = 0.0 +# S2z4: float = 0.0 +# S2z5: float = 0.0 +# S2z6: float = 0.0 + + +# def _build_kepler_vars(eta: float, total_mass: float, +# S1z: float, S2z: float) -> KeplerVars: +# """Pre-compute and cache all integer powers used by the mode functions.""" +# kv = KeplerVars() +# kv.eta = eta +# kv.eta2 = eta**2; kv.eta3 = eta**3; kv.eta4 = eta**4 +# kv.eta5 = eta**5; kv.eta6 = eta**6 + +# kv.Mtot = total_mass +# kv.Mtot2 = total_mass**2; kv.Mtot3 = total_mass**3 +# kv.Mtot4 = total_mass**4; kv.Mtot5 = total_mass**5 +# kv.Mtot6 = total_mass**6 + +# kv.S1z = S1z +# for p in range(2, 17): +# setattr(kv, f"S1z{p}", S1z**p) + +# kv.S2z = S2z +# for p in range(2, 7): +# setattr(kv, f"S2z{p}", S2z**p) + +# return kv + + +# ------------------------------------------------------------------ # +# 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)) + print(mode_pn_order) + + 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] + # ------------------------------------------------------------------ # + u0 = pn_kepler_equation(eta, x_init, e_init, mean_anom_init) + r0 = separation(u0, eta, x_init, e_init, mass1, mass2, S1z, S2z) + y0_dot = eccentric_x_model_odes(0.0, [x_init, e_init, mean_anom_init, 0.0], params) + phi_dot0 = y0_dot[3] + + y0 = np.array([x_init, e_init, mean_anom_init, 0.0]) + + # ------------------------------------------------------------------ # + # ODE integration (RK45 via scipy) + # ------------------------------------------------------------------ # + def rhs(t, y): + return eccentric_x_model_odes(t, y, params) + + solver = ode(rhs).set_integrator( + "dopri5", # RK4(5) Dormand-Prince ≈ gsl rkf45 + rtol=ode_eps, + atol=0.0, + nsteps=10_000, + # max_hnil=0, + first_step=2 * LAL_PI / phi_dot0 / 100, + ) + solver.set_initial_value(y0, 0.0) + + MAX_SAMPLES = 2048 * 16384 + + t_list = [0.0] + x_list = [x_init] + e_list = [e_init] + l_list = [mean_anom_init] + phi_list = [0.0] + phi_dot_list = [phi_dot0] + r_list = [r0] + u_list = [u0] + + bad_number = False + + while solver.successful(): + solver.integrate(solver.t + dt) + + y = solver.y + + # NaN / Inf guard + if not np.all(np.isfinite(y)): + bad_number = True + break + + t_list.append(solver.t) + x_list.append(y[0]) + e_list.append(y[1]) + l_list.append(y[2]) + phi_list.append(y[3]) + + yd = eccentric_x_model_odes(solver.t, y, params) + phi_dot_list.append(yd[3]) + + ui = pn_kepler_equation(eta, y[0], y[1], y[2]) + ri = separation(ui, eta, y[0], y[1], mass1, mass2, S1z, S2z) + u_list.append(ui) + r_list.append(ri) + + if len(t_list) >= MAX_SAMPLES: + break + + if y[0] >= x_final: + break + + # Convert to arrays + t_arr = np.array(t_list) + x_arr = np.array(x_list) + e_arr = np.array(e_list) + l_arr = np.array(l_list) + phi_arr = np.array(phi_list) + phi_dot_arr = np.array(phi_dot_list) + r_arr = np.array(r_list) + + final_i = len(t_arr) if not bad_number else len(t_arr) # same either way here + if final_i < 4: + raise RuntimeError("Integration produced fewer than 4 points; cannot interpolate.") + + # ------------------------------------------------------------------ # + # 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_uniform(t_arr, phi_dot_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..49f7259 --- /dev/null +++ b/esigmapy/python_codes/generator_python.py @@ -0,0 +1,1241 @@ +# Copyright (C) 2020 Prayush Kumar, Akash Maurya, Kaushik Paul +# +"""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 * + +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_window_start(freq_, delta_t_, delta_phi_, direction="forward"): + """Integrates frequency backward/forward from the start/end until + `delta_phi_` radians of orbital phase has elapsed. + + Args: + freq_ (numpy.array): Frequency time series, uniformly sampled + delta_t_ (float64): Step size in time + delta_phi_ (float64): Phase shift in radians to integrate across + direction (str, optional): Direction of integration in time. + Defaults to "forward". + + Returns: + int: Index in frequency array where `delta_phi` is reached + """ + from scipy import integrate + + if direction == "backward": + for idx in range(len(freq_) - 2, 0, -1): + # this could be optimized to not repeat integration + if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): + return idx + elif direction == "forward": + for idx in range(1, len(freq_)): + # this could be optimized to not repeat integration + if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( + delta_phi_ + ): + return idx + + +def _get_transition_frequency_window( + orbital_phase, + orbital_freq, + delta_t, + f_mr_transition, # orbital frequency + num_hyb_orbits, + keep_f_mr_transition_at_center, + blend_using_avg_orbital_frequency, + failsafe, + verbose=False, +): + """ + This function first finds where the `orbital_freq` crosses the transition + frequency `f_mr_transition`, and finds the point in time that is + `num_hyb_orbits` orbits before that time. This marks the start of the + hybridization window, and the orbital frequency at that time is returned + + Parameters: + ----------- + orbital_phase -- Orbital phase evolution + orbital_freq -- Orbital frequency evolution + delta_t -- Waveform's time grid-spacing (s) + f_mr_transition -- Inspiral to merger transition frequency + (Hz). This should be the same (mode/orbital) + frequency as passed for `orbital_freq`. + num_hyb_orbits -- number of orbital cycles to blend over. + 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). + blend_using_avg_orbital_frequency -- If True, the orbit averaged + frequency during the inspiral is used to + blend modes, instead of the modes' + frequency. + failsafe -- If True, we make reasonable choices for the + user, if the inputs to this method lead + into exceptions. + verbose -- Verbosity flag + + Returns: + -------- + f_window_mr_transition -- Hybridization frequency window (in Hz). + """ + transition_idx = ( + len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) + ) # index at which orbital_freq becomes just smaller than transition orbital + # frequency, towards the end of waveform + + if verbose > 4: + print( + f"""Transition frequency found at index {transition_idx} + in the orbital frequency array of length {len(orbital_freq)}""" + ) + + if blend_using_avg_orbital_frequency: + # We integrate `orbital_freq` to obtain `orbital_phase` + from scipy import integrate + + orbital_phase = integrate.cumulative_trapezoid( + orbital_freq, dx=delta_t, initial=0 + ) + if len(orbital_phase) != len(orbital_freq): + raise RuntimeError( + f"""Something went wrong while integrating orbital frequency. +They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" + ) + + if keep_f_mr_transition_at_center: + # Orbital cycle based hybridization that keeps f_mr_transition at + # the hybridization frequency window's midpoint + if blend_using_avg_orbital_frequency: + window_start_idx = _get_window_start( + orbital_freq[: transition_idx + 1], + delta_t, + num_hyb_orbits * np.pi, + direction="backward", + ) + window_end_idx = transition_idx + _get_window_start( + orbital_freq[transition_idx:], + delta_t, + num_hyb_orbits * np.pi, + direction="forward", + ) + # FAILSAFE mode behavior + if window_start_idx is None: + if failsafe: + window_start_idx = 0 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform before the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or increase the +inspiral-to-merger transition frequency. `window_start_idx` is None.""" + ) + if window_end_idx is None: + if failsafe: + window_end_idx = len(orbital_freq) - 1 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform after the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or decrease the +inspiral-to-merger transition frequency. `window_end_idx` is None.""" + ) + else: + # phase is always a monotonically increasing function, + # hence using the more efficient np.searchsorted method + window_start_idx = -1 + np.searchsorted( + orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi + ) + window_end_idx = np.searchsorted( + orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi + ) + f_window_mr_transition = 2 * np.min( + [ + orbital_freq[window_end_idx] - f_mr_transition, + f_mr_transition - orbital_freq[window_start_idx], + ] + ) + elif not keep_f_mr_transition_at_center: + # Orbital cycle based hybridization that keeps f_mr_transition at + # the hybridization frequency window's right end + if blend_using_avg_orbital_frequency: + window_start_idx = _get_window_start( + orbital_freq[: transition_idx + 1], + delta_t, + 2 * num_hyb_orbits * np.pi, + direction="backward", + ) + if window_start_idx is None: + if failsafe: + window_start_idx = 0 + else: + raise RuntimeError( + f"""Requested number of orbits to blend over not available +in the waveform before the transition frequency. Either decrease the number of +orbits to blend over (currently {num_hyb_orbits}) or increase the +inspiral-to-merger transition frequency. `window_start_idx` is None.""" + ) + else: + # Orbital cycle based hybridization that keeps f_mr_transition + # at the hybridization frequency window's end + window_start_idx = ( + np.searchsorted( + orbital_phase, + orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, + ) + - 1 + ) + f_window_mr_transition = ( + orbital_freq[transition_idx] - orbital_freq[window_start_idx] + ) + + if verbose > 4: + print( + f"""The transition is to happen in a window from + frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, + between indices: [{window_start_idx}, {transition_idx}]""" + ) + else: + raise RuntimeError( + """We should never reach here. Did you set a non-bool value for the + flag `keep_f_mr_transition_at_center`?""" + ) + + if verbose > 4: + print(f"""f_window_mr_transition = {f_window_mr_transition}""") + + return f_window_mr_transition + + +def get_imr_esigma_modes( + 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 + 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. Its 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( + 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( + 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/.ipynb_checkpoints/ESIGMA_tutorial-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/ESIGMA_tutorial-checkpoint.ipynb new file mode 100644 index 0000000..073dff3 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/ESIGMA_tutorial-checkpoint.ipynb @@ -0,0 +1,670 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d279a82c", + "metadata": {}, + "source": [ + "## Prerequisites:\n", + "\n", + "1. `lalsuite`'s ESIGMA branch should be installed (and sourced!) $\\rightarrow$ required for the inspiral piece.\n", + "2. `NRSur7dq4`'s [data file](https://git.ligo.org/lscsoft/lalsuite-extra/-/blob/master/data/lalsimulation/NRSur7dq4.h5) should be downloaded (for LALSuite versions >= 7.25, download [this data file](https://git.ligo.org/waveforms/software/lalsuite-waveform-data/-/blob/main/waveform_data/NRSur7dq4_v1.0.h5?ref_type=heads) instead), with the path to the directory containing it been set to the bash environment variable `LAL_DATA_PATH` $\\rightarrow$ required for the merger-ringdown piece.\n", + "\n", + "The detailed instructions to set these up are present on the [git repository](https://github.com/gwnrtools/esigmapy)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "108225e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No version information file '.version' found\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import esigmapy\n", + "\n", + "# Configuring some plot settings\n", + "plt.rcParams.update(\n", + " {\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.serif\": [\"Computer Modern\"],\n", + " \"font.size\": 12,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "19bf6f29", + "metadata": {}, + "source": [ + "## IMRESIGMA" + ] + }, + { + "cell_type": "markdown", + "id": "01cb2b62", + "metadata": {}, + "source": [ + "### Waveform polarizations\n", + "The polarizations $h_+$ and $h_\\times$ can be generated via the `get_imr_esigma_waveform` function. They are returned as `PyCBC` `TimeSeries` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "763108d4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.3\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "\n", + "hp, hc = 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", + ")\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"$h$\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "8d52f275", + "metadata": {}, + "source": [ + "One can also specify the eccentricity and mean anomaly at some reference frequency $f_{\\rm{ref}}$ different from the starting frequency $f_{\\rm{low}}$ of the waveform." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2d9e21a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f_ref = 25.0\n", + "f_low = 20.0\n", + "\n", + "# This time, the following eccentricity and mean anomaly values are\n", + "# defined at f_ref, and not at f_low\n", + "eccentricity = 0.15\n", + "mean_anomaly = 30.0 * np.pi / 180.0\n", + "\n", + "hp, hc, orb_vars = 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_ref=f_ref,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " # Returns orbital variables' evolution; explained below\n", + " return_orbital_params=[\"x\", \"e\"],\n", + ")\n", + "\n", + "######### Plotting #################\n", + "from esigmapy.utils import f22_from_x\n", + "\n", + "t_imr = hp.sample_times.data\n", + "t = orb_vars[\"e\"].sample_times.data\n", + "# Orbital variables are only available for\n", + "# inspiral potion, so the IMR waveform's time grid\n", + "# is longer and shifted as compared to inspiral's\n", + "t_shift = t[0] - t_imr[0]\n", + "# Making the inspiral time grid start\n", + "# at the same time as the IMR grid\n", + "t -= t_shift\n", + "e = orb_vars[\"e\"].data\n", + "\n", + "# Getting the orbit-averaged GW frequency from\n", + "# ESIGMA's PN parameter x. This is the frequency\n", + "# that is set when supplying f_lower/f_ref to ESIGMA.\n", + "f_22 = f22_from_x(orb_vars[\"x\"].data, M=m1 + m2)\n", + "if f_ref is None or f_ref <= f_low:\n", + " ref_idx = 0\n", + "else:\n", + " ref_idx = np.argmax(f_22 >= f_ref)\n", + "t_ref = t[ref_idx]\n", + "e_ref = e[ref_idx]\n", + "f_22_ref = f_22[ref_idx]\n", + "\n", + "fig, axs = plt.subplots(2, sharex=True, figsize=(10, 6))\n", + "axs[0].set_title(\n", + " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", + " $e_{{\\rm{{ref}}}}={eccentricity}, l_{{\\rm{{ref}}}}={mean_anomaly:.2f}, f_{{\\rm{{ref}}}}={f_ref}\\rm{{Hz}}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\n", + " $d_L={distance}\\rm{{Mpc}}, \\iota={inclination:.2f}$\"\"\"\n", + ")\n", + "axs[0].plot(t_imr, hp.data, label=r\"$h_+$\")\n", + "axs[0].plot(t_imr, hc.data, label=r\"$h_\\times$\")\n", + "if f_ref >= f_low:\n", + " axs[0].axvline(t_ref, color=\"k\", ls=\"--\", lw=0.7, label=r\"$t_{\\rm{ref}}$\")\n", + " axs[0].set_ylabel(r\"$h$\")\n", + "axs[0].legend()\n", + "\n", + "# Plotting the reference point at which eccentricity was defined\n", + "# in the eccentricity evolution plot\n", + "axsL = axs[1]\n", + "axsR = axsL.twinx()\n", + "axsL.plot(t, e, label=r\"$e(t)$\")\n", + "axsR.plot(t, f_22, label=r\"$2 \\bar{f}_{\\rm{orb}}(t)$\", color=\"green\")\n", + "if f_ref >= f_low:\n", + " axsL.scatter(t_ref, e_ref, color=\"k\", label=r\"Reference point\")\n", + " axsL.axvline(t_ref, color=\"k\", ls=\"--\", lw=0.7)\n", + " axsL.axhline(e_ref, color=\"k\", ls=\"--\", lw=0.7)\n", + "\n", + " axsR.scatter(t_ref, f_22_ref, color=\"k\")\n", + " axsR.axhline(f_22_ref, color=\"k\", ls=\"--\", lw=0.7)\n", + "\n", + "axsL.set_ylabel(r\"$e$\")\n", + "axsR.set_ylabel(r\"$f$ (Hz)\")\n", + "axs[1].legend()\n", + "\n", + "axs[1].set_xlabel(r\"$t (s)$\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "7eba1795", + "metadata": {}, + "source": [ + "### Waveform modes\n", + "One can also generate the spin-weighted spherical harmonic modes via the `get_imr_esigma_modes` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "81532049", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m1 = 10.0 # masses (in solar masses)\n", + "m2 = 40.0\n", + "spin1z = 0.7 # dimensionless spins\n", + "spin2z = 0.6\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\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**12 # time grid-spacing (in s)\n", + "\n", + "modes_to_use = [(2, 2), (2, 1), (3, 3), (3, 2), (4, 4), (4, 3)]\n", + "\n", + "modes = 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", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " include_conjugate_modes=False,\n", + ")\n", + "\n", + "fig, axs = plt.subplots(len(modes_to_use), sharex=True, figsize=(10, 10))\n", + "axs[0].set_title(\n", + " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", + " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}, d_L={distance}\\rm{{Mpc}}$\"\"\"\n", + ")\n", + "for i, mode_name in enumerate(modes_to_use):\n", + " ell, m = mode_name\n", + " axs[i].plot(\n", + " modes[mode_name].sample_times.data,\n", + " modes[mode_name].real().data,\n", + " label=rf\"$\\Re(h_{{{ell} {m}}})$\",\n", + " )\n", + " axs[i].plot(\n", + " modes[mode_name].sample_times.data,\n", + " modes[mode_name].imag().data,\n", + " label=rf\"$\\Im(h_{{{ell} {m}}})$\",\n", + " )\n", + " axs[i].legend(loc=2)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "eb60ec34", + "metadata": {}, + "source": [ + "Other options related to hybridization settings, merger-ringdown model choice, etc. are available as well. One can check them in the respective docstrings of the functions like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "aa78d9e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function get_imr_esigma_waveform in module esigmapy.generator:\n", + "\n", + "get_imr_esigma_waveform(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)\n", + " Returns IMR GW polarizations constructed using IMR ESIGMA modes\n", + " \n", + " Parameters:\n", + " -----------\n", + " mass1, mass2 -- Binary's component masses (in solar masses)\n", + " f_lower -- Starting frequency of the waveform (in Hz)\n", + " f_ref -- Reference frequency at which to define the\n", + " waveform parameters. We require that\n", + " `f_ref <= f_lower`.\n", + " `f_ref = f_lower` by default.\n", + " delta_t -- Waveform's time grid-spacing (in s)\n", + " spin1z, spin2z -- z-components of component dimensionless\n", + " spins (lies in [0,1))\n", + " eccentricity -- Initial eccentricity\n", + " mean_anomaly -- Mean anomaly of the periastron (in rad)\n", + " inclination -- Inclination (in rad), defined as the angle\n", + " between the orbital angular momentum L and\n", + " the line-of-sight\n", + " coa_phase -- Coalesence phase of the binary (in rad)\n", + " distance -- Luminosity distance to the binary (in Mpc)\n", + " modes_to_use -- GW modes to use. List of tuples (l, |m|)\n", + " mode_to_align_by -- GW mode to use to align inspiral and merger\n", + " in phase and time\n", + " f_mr_transition -- Inspiral to merger transition GW frequency\n", + " (Hz).\n", + " Defaults to the minimum of the Kerr and\n", + " Schwarzschild ISCO frequency\n", + " f_window_mr_transition -- Hybridization frequency window (in Hz).\n", + " Disabled by the default value (None). In\n", + " such a case, the hybridization proceeds\n", + " over a window of `num_hyb_orbits` orbital\n", + " cycles (1 orbital cycle ~ 2 GW cycles)\n", + " that ends at the frequency value given by\n", + " `f_mr_transition`.\n", + " Also see `keep_f_mr_transition_at_center`\n", + " to choose the position of `f_mr_transition`\n", + " within this window.\n", + " num_hyb_orbits -- number of orbital cycles to blend over.\n", + " Only used if f_window_mr_transition is not\n", + " specified.\n", + " blend_using_avg_orbital_frequency -- If True, the orbit averaged\n", + " frequency during the inspiral is used to\n", + " blend modes, instead of the modes'\n", + " frequency.\n", + " keep_f_mr_transition_at_center -- If True, `f_mr_transition` is kept at\n", + " the center of the hybridization window.\n", + " Otherwise, it's kept at the end of the\n", + " window (default).\n", + " merger_ringdown_approximant -- Choose merger-ringdown model. Tested\n", + " choices: [NRSur7dq4, SEOBNRv4PHM]\n", + " return_hybridization_info -- If True, returns hybridization related data\n", + " return_orbital_params -- If True, returns the orbital evolution of\n", + " all the orbital elements (in\n", + " geometrized units). Can also be a list of\n", + " orbital variable names to return\n", + " only those specific variables. Available\n", + " orbital variables names are:\n", + " ['x', 'e', 'l', 'phi', 'phidot', 'r', 'rdot'].\n", + " Note that these are available only for the\n", + " inspiral portion of the waveform!\n", + " failsafe -- If True, we make reasonable choices for the\n", + " user, if the inputs to this method lead\n", + " into exceptions.\n", + " verbose -- Verbosity level. Available values are: 0, 1, 2\n", + " \n", + " Returns:\n", + " --------\n", + " hp, hc -- Plus and cross IMR GW polarizations PyCBC TimeSeries\n", + " orbital_vars_dict -- Dictionary of evolution of orbital elements.\n", + " Returned only if return_orbital_params is specified\n", + " retval -- Hybridization related data.\n", + " Returned only if return_hybridization_info is True\n", + "\n" + ] + } + ], + "source": [ + "help(esigmapy.get_imr_esigma_waveform)" + ] + }, + { + "cell_type": "markdown", + "id": "52eb4d06", + "metadata": {}, + "source": [ + "## InspiralESIGMA" + ] + }, + { + "cell_type": "markdown", + "id": "2c052713", + "metadata": {}, + "source": [ + "One can also generate the inspiral-only wavefroms and modes via the `get_inspiral_esigma_waveform` and `get_inspiral_esigma_modes` functions respectively. Unlike the full IMR waveform which uses a quasi-circular merger-ringdown model and thus forbids using large starting eccentricities, the inspiral waveforms can be generated for arbitrarily eccentric (bounded) orbits (i.e. with $0 \\leq e < 1$)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "16d39835", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m1 = 10.0 # masses (in solar masses)\n", + "m2 = 50.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = 0.6\n", + "eccentricity = 0.55 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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 = 8 # starting frequency (in Hz)\n", + "delta_t = 1 / 2**12 # time grid-spacing (in s)\n", + "\n", + "hp, hc = esigmapy.get_inspiral_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", + ")\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"$h$\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4a5b0cb7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "modes_to_use = [(2, 2), (2, 1), (3, 3), (3, 2), (4, 4), (4, 3)]\n", + "\n", + "modes = 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", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " include_conjugate_modes=False,\n", + ")\n", + "\n", + "fig, axs = plt.subplots(len(modes_to_use), sharex=True, figsize=(10, 10))\n", + "axs[0].set_title(\n", + " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", + " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}, d_L={distance}\\rm{{Mpc}}$\"\"\"\n", + ")\n", + "for i, mode_name in enumerate(modes_to_use):\n", + " ell, m = mode_name\n", + " axs[i].plot(\n", + " modes[mode_name].sample_times.data,\n", + " modes[mode_name].real().data,\n", + " label=rf\"$\\Re(h_{{{ell} {m}}})$\",\n", + " )\n", + " axs[i].plot(\n", + " modes[mode_name].sample_times.data,\n", + " modes[mode_name].imag().data,\n", + " label=rf\"$\\Im(h_{{{ell} {m}}})$\",\n", + " )\n", + " axs[i].legend(loc=2)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "fbd28174", + "metadata": {}, + "source": [ + "## Evolution of orbital elements\n", + "The evolution of binary's orbital elements can also be accessed, but only for the *inspiral part of the dynamics*. These can be accessed via the argument `return_orbital_params` in all of the above discussed waveform/mode functions. The available orbital elements are\n", + "\n", + "- $x$: The post-Newtonian (PN) parameter. It's related to the orbit-averaged (azimuthal) orbital frequency\n", + "- $e$: Orbital eccentricity\n", + "- $l$: Mean anomaly\n", + "- $r$: Relative separation\n", + "- $\\dot{r}$: Radial velocity\n", + "- $\\phi$: Orbital phase\n", + "- $\\dot{\\phi}$: Angular velocity\n", + "\n", + "All of these are returned in geometric units ($G=c=1$)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5974efc1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$t (s)$')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m1 = 10.0 # masses (in solar masses)\n", + "m2 = 50.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = 0.6\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", + "orb_params_list = [\"x\", \"e\", \"l\", \"r\", \"rdot\", \"phi\", \"phidot\"]\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 # starting frequency (in Hz)\n", + "delta_t = 1 / 2**12 # time grid-spacing (in s)\n", + "\n", + "orb_vars, hp, hc = esigmapy.get_inspiral_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", + " return_orbital_params=orb_params_list,\n", + ")\n", + "\n", + "fig, axs = plt.subplots(len(orb_params_list), sharex=True, figsize=(10, 10))\n", + "axs[0].set_title(\n", + " rf\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}, e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\"\n", + ")\n", + "\n", + "orb_var_latex_labels = {\n", + " \"x\": r\"$x$\",\n", + " \"e\": r\"$e$\",\n", + " \"l\": r\"$l$\",\n", + " \"r\": r\"r\",\n", + " \"rdot\": r\"$\\dot{r}$\",\n", + " \"phi\": \"$\\phi$\",\n", + " \"phidot\": r\"$\\dot{\\phi}$\",\n", + "}\n", + "\n", + "for i, orb_params_name in enumerate(orb_params_list):\n", + " axs[i].plot(\n", + " orb_vars[orb_params_name].sample_times.data,\n", + " orb_vars[orb_params_name].data,\n", + " label=rf\"{orb_var_latex_labels[orb_params_name]}\",\n", + " )\n", + " axs[i].legend(loc=2)\n", + "plt.xlabel(r\"$t (s)$\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "esigma-igwn39", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb new file mode 100644 index 0000000..5c91f21 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", + "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", + "text": [ + "No version information file '.version' found\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import esigmapy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b962168c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "x=os.environ.get(\"ModePNOrder\")\n", + "print('Poof!') if x is None else int(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a666270a-c644-473b-8559-57df6e16217c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Orbital evolution took: 7.503528344001097 seconds\n", + "3\n", + "3\n", + "Modes generation took: 0.2238857789998292 seconds\n" + ] + } + ], + "source": [ + "from esigmapy.python_codes import generator_python as gp\n", + "\n", + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.3\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "\n", + "# hp, hc = gp.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", + "# )\n", + "\n", + "modes_py = gp.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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "16244db1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Orbital evolution took: 0.05242229400028009 seconds\n", + "Modes generation took: 0.14869545600231504 seconds\n" + ] + } + ], + "source": [ + "# m1 = 20.0 # masses (in solar masses)\n", + "# m2 = 30.0\n", + "# spin1z = 0.5 # dimensionless spins\n", + "# spin2z = -0.3\n", + "# eccentricity = 0.15 # starting eccentricity\n", + "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "\n", + "# hp, hc = gp.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", + "# )\n", + "\n", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9fc2cdd6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real = modes_py[(2, 2)].real()\n", + "hp22_c_real = modes_c[(2, 2)].real()\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", + "# hp.plot(label=r\"$h_+$\")\n", + "# hc.plot(label=r\"$h_\\times$\")\n", + "\n", + "plt.plot(hp22_py_real.sample_times,hp22_py_real,label='Python')\n", + "plt.plot(hp22_c_real.sample_times+0.0185,hp22_c_real,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c208204c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real = modes_py[(2, 2)].real()\n", + "hp22_c_real = modes_c[(2, 2)].real()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"INSPIRAL-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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_py_real.sample_times,hp22_py_real,label='Python')\n", + "plt.plot(hp22_c_real.sample_times+0.019,hp22_c_real,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys([(2, 2), (2, -2)])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(modes_py.keys())\n", + "hp22 = modes_py[(2, 2)].real()\n", + "hc22 = modes_py[(2, 2)].imag()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"INSPIRAL-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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.sample_times,hp22,label=r\"Re [$h_{22}$]\")\n", + "plt.plot(hc22.sample_times,hc22,label=r\"Im [$h_{22}$]\")\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"$h$\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "173f050f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/ESIGMA_tutorial.ipynb b/notebooks/ESIGMA_tutorial.ipynb index 073dff3..b04b657 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", @@ -32,15 +62,79 @@ "import matplotlib.pyplot as plt\n", "import esigmapy\n", "\n", - "# Configuring some plot settings\n", - "plt.rcParams.update(\n", - " {\n", - " \"text.usetex\": True,\n", - " \"font.family\": \"serif\",\n", - " \"font.serif\": [\"Computer Modern\"],\n", - " \"font.size\": 12,\n", - " }\n", - ")" + "# # Configuring some plot settings\n", + "# plt.rcParams.update(\n", + "# {\n", + "# \"text.usetex\": True,\n", + "# \"font.family\": \"serif\",\n", + "# \"font.serif\": [\"Computer Modern\"],\n", + "# \"font.size\": 12,\n", + "# }\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "707a5838", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'lalsimulation' has no attribute 'SimInspiralESIGMADynamics'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 29\u001b[39m\n\u001b[32m 13\u001b[39m delta_t = \u001b[32m1\u001b[39m / \u001b[32m2\u001b[39m**\u001b[32m12\u001b[39m \u001b[38;5;66;03m# time grid-spacing (in s)\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# hp, hc = gp.get_imr_esigma_waveform(\u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# mass1=m1,\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[38;5;66;03m# mass2=m2,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 26\u001b[39m \u001b[38;5;66;03m# modes_to_use=modes_to_use,\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m modes = \u001b[43mesigmapy\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_inspiral_esigma_modes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 30\u001b[39m \u001b[43m \u001b[49m\u001b[43mmass1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mm1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 31\u001b[39m \u001b[43m \u001b[49m\u001b[43mmass2\u001b[49m\u001b[43m=\u001b[49m\u001b[43mm2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 32\u001b[39m \u001b[43m \u001b[49m\u001b[43mspin1z\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspin1z\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 33\u001b[39m \u001b[43m \u001b[49m\u001b[43mspin2z\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspin2z\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 34\u001b[39m \u001b[43m \u001b[49m\u001b[43meccentricity\u001b[49m\u001b[43m=\u001b[49m\u001b[43meccentricity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 35\u001b[39m \u001b[43m \u001b[49m\u001b[43mmean_anomaly\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmean_anomaly\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 36\u001b[39m \u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 37\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# inclination=inclination,\u001b[39;49;00m\n\u001b[32m 38\u001b[39m \u001b[43m \u001b[49m\u001b[43mf_lower\u001b[49m\u001b[43m=\u001b[49m\u001b[43mf_low\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 39\u001b[39m \u001b[43m \u001b[49m\u001b[43mdelta_t\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdelta_t\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 40\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodes_to_use\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodes_to_use\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[32m 42\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/esigmapy-dev/esigmapy/esigmapy/generator.py:246\u001b[39m, in \u001b[36mget_inspiral_esigma_modes\u001b[39m\u001b[34m(mass1, mass2, f_lower, delta_t, spin1z, spin2z, eccentricity, mean_anomaly, distance, f_ref, modes_to_use, include_conjugate_modes, return_orbital_params, return_pycbc_timeseries, verbose)\u001b[39m\n\u001b[32m 243\u001b[39m itime = time.perf_counter()\n\u001b[32m 244\u001b[39m f_start = f_ref\n\u001b[32m--> \u001b[39m\u001b[32m246\u001b[39m retval = \u001b[43mls\u001b[49m\u001b[43m.\u001b[49m\u001b[43mSimInspiralESIGMADynamics\u001b[49m(\n\u001b[32m 247\u001b[39m mass1,\n\u001b[32m 248\u001b[39m mass2,\n\u001b[32m 249\u001b[39m spin1z,\n\u001b[32m 250\u001b[39m spin2z,\n\u001b[32m 251\u001b[39m eccentricity,\n\u001b[32m 252\u001b[39m f_start,\n\u001b[32m 253\u001b[39m mean_anomaly,\n\u001b[32m 254\u001b[39m \u001b[32m1e-12\u001b[39m,\n\u001b[32m 255\u001b[39m \u001b[32m1\u001b[39m / delta_t,\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 257\u001b[39m )\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m f_ref < f_lower:\n\u001b[32m 260\u001b[39m ref_idx = np.searchsorted(\n\u001b[32m 261\u001b[39m (retval[\u001b[32m1\u001b[39m].data.data ** \u001b[32m1.5\u001b[39m) / ((mass1 + mass2) * lal.MTSUN_SI * np.pi),\n\u001b[32m 262\u001b[39m f_lower,\n\u001b[32m 263\u001b[39m )\n", + "\u001b[31mAttributeError\u001b[39m: module 'lalsimulation' has no attribute 'SimInspiralESIGMADynamics'" + ] + } + ], + "source": [ + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.3\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "\n", + "# hp, hc = gp.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", + "# )\n", + "\n", + "modes = 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" ] }, { @@ -648,7 +742,7 @@ ], "metadata": { "kernelspec": { - "display_name": "esigma-igwn39", + "display_name": "esigmapy-dev", "language": "python", "name": "python3" }, @@ -662,7 +756,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.11.15" } }, "nbformat": 4, diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb new file mode 100644 index 0000000..6cf03cc --- /dev/null +++ b/notebooks/esigma_python.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "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", + "text": [ + "No version information file '.version' found\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import esigmapy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b962168c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "x=os.environ.get(\"ModePNOrder\")\n", + "print('Poof!') if x is None else int(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a666270a-c644-473b-8559-57df6e16217c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Orbital evolution took: 7.503528344001097 seconds\n", + "3\n", + "3\n", + "Modes generation took: 0.2238857789998292 seconds\n" + ] + } + ], + "source": [ + "from esigmapy.python_codes import generator_python as gp\n", + "\n", + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.3\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "\n", + "# hp, hc = gp.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", + "# )\n", + "\n", + "modes_py = gp.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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "16244db1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Orbital evolution took: 0.05242229400028009 seconds\n", + "Modes generation took: 0.14869545600231504 seconds\n" + ] + } + ], + "source": [ + "# m1 = 20.0 # masses (in solar masses)\n", + "# m2 = 30.0\n", + "# spin1z = 0.5 # dimensionless spins\n", + "# spin2z = -0.3\n", + "# eccentricity = 0.15 # starting eccentricity\n", + "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "\n", + "# hp, hc = gp.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", + "# )\n", + "\n", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9fc2cdd6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real = modes_py[(2, 2)].real()\n", + "hp22_c_real = modes_c[(2, 2)].real()\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", + "# hp.plot(label=r\"$h_+$\")\n", + "# hc.plot(label=r\"$h_\\times$\")\n", + "\n", + "plt.plot(hp22_py_real.sample_times,hp22_py_real,label='Python')\n", + "plt.plot(hp22_c_real.sample_times+0.0185,hp22_c_real,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c208204c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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t8bp160rdByAiIoKQkBAAsrOz2bp1q0P7tW/f3pbNkmunkWtXNLl2hcm1K6y6r50Q4sYkQbcQQgghhBBCCFFFZEy3EELkk5ub6/C4xqCgIPR6PQBZWVkOj2usVauW7f/p6emkp6eXuo9erycoKMj2OCUlxaEsiouLCwEBAbbHiYmJ5Obmlrqfu7u73bjG+Ph4h8Ynenl52Y1rjI2NLXUf0MaIuru7A2AymYiPj3dov4CAAFxcXAC5dlZy7Yom164wuXaFVfe1E0LcoKq3d7sQQvy7yPhEGVsq106unVw7uXbVde2EEDcm6V4uhBD5yPhEjYwtLZpcu8Lk2hUm165ocu0KkzHdQtwcJOgWQgghhBBCCCGqiK66KyCEEEIIIYQQQtyoJOgWQgghhBBCCCGqiATdQgghhBBCCCFEFZGgWwghhBBCCCGEqCISdAshhBBCCCGEEFVEgm4hhBBCCCGEEKKKSNAthBBCCCGEEEJUEQm6hRBCCCGEEEKIKiJBtxBCCCGEEEIIUUUk6BZCCCGEEEIIIaqIBN1CCHET2b17NxMmTCAiIgJPT0/q1q3LAw88wKlTpwqVzcnJ4ZVXXiE0NBR3d3c6derE2rVrHT5XWfb/888/URQFRVH48ccfCz2fm5tLw4YNURSFNm3aOFyHm8nRo0cZNmwYDRs2xMPDg8DAQLp3787vv/9eqKxc2+tPRa7Zxo0bbdeg4M+OHTuquObXr4q0eVk+j0KIG58E3UIIcRN5//33+eWXX7jzzjv59NNPefzxx9m8eTPt2rXjyJEjdmXHjRvHRx99xOjRo/n000/R6/X069ePrVu3OnSusux/8OBBANzc3Dh27Fih52fPnk10dDQArVu3LuvLvimcP3+etLQ0xo4dy6effsobb7wBwL333sucOXPsysq1vf5U9JoBPP300yxYsMDuJzw8vAprfX2rSJuX5fMohLgJqEIIIW4a27ZtU3Nycuy2nTp1SnV1dVVHjx5t27Zz504VUKdPn27blpWVpTZq1Ejt3Llzqecp6/6jR49W/fz81N69e6tDhw61ey4tLU0NDg5WBw0apALqhx9+6PDrvdkZjUa1devWatOmTW3b5Npefyp6zTZs2KAC6pIlS6qymjeUirZ5UYr6PAohbg6S6RZCiAq466676Ny5M9u3b6dnz554enoSHh7OqlWrAFi1ahW33XYbnp6etGnThr1791Zrfbt06YKLi4vdtsaNGxMREcHx48dt25YuXYper+fxxx+3bXNzc+PRRx9l+/btXLx4scTzlHX/gwcP0rJlS1q2bFkoG/rhhx9iNBrp06cPcO2yodfbtS2KXq+nTp06JCcn27bJtYVvvvkGNzc3unbtyvnz523bVVWlV69eBAYGEhcXd03q4oiKXrP80tLSMBqN5a7L9dZ25VWZbW5V1OdRCHFzkKBbCCEq4NChQ6SkpDB8+HB69erF//3f/5GWlsaoUaOYPXs2Tz/9NPfffz+vv/46Z86c4ZFHHin3uQwGAwkJCQ79mM1mh4+rqipXrlwhMDDQtm3//v00adIEHx8fu7IdO3YE4MCBAyUesyz75+bmcvLkSVq1akWLFi2IjIy0BQXx8fF8+OGHTJo0yfYFv1WrVg6/toq4Xq9tRkYGCQkJnDlzho8//pjVq1dz55132p6XawsdOnTgpZdeYseOHcyYMcO2febMmWzcuJHPP/+c4ODgSjlXZVzbil4zq4cffhgfHx/c3Nzo1asXe/bsKfPrud7arrwqq81L+zwKIW4OTtVdASGEuF7FxcURFxeHoijs37+fkJAQAHQ6HU8//TSffPIJ+/bts31pS0hI4OOPPyYnJwdXV9cyn2/btm306tXLobLnzp2jfv36DpVduHAh0dHRvP3227ZtMTExtteTn3Xb5cuXSzxmWfY/duwYBoPBFpgZDAZOnz5Ns2bNmDp1Kr6+vkyYMIEhQ4YQEhJCUFCQQ6+rIq7na/vCCy8we/ZsW30HDx7MF198YXv+Zr+2oGXUW7duza5du2w9FM6ePcv//vc/Bg0axMiRIyvtXJVxbSt6zVxcXBgyZAj9+vUjMDCQY8eOMWPGDLp168Y///xD27ZtHaofXH9tV14VbXOr0j6PQoibgwTdQghRTocOHQJgypQpdl/OvLy8AJg+fbpdlsTX1xedTodOV75ORq1bt3Z45txatWo5VO7EiROMHz+ezp07M3bsWNv2rKysIoNHNzc32/MlKcv+1nZs3bo1ERERKIrC8ePHcXNz46uvvmLWrFm4ublx6NCha9b9+Hq+ts8++yxDhw7l8uXL/Pzzz5hMJnJzc23P3+zXNr82bdowa9YszGYzjzzyCK6ursyaNatSz1EZ17ai16xLly506dLF9vjee+9l6NChtGrVikmTJrFmzRqH6pff9dJ2ZrPZ7v1fEldXVxRFASre5lalfR6FEDcHCbqFEKKcDh8+DGhfYPM7efIk7u7u3HXXXXbbT506RaNGjXB2dgZg1qxZfP311xw+fJjXXnuNKVOmlHg+f39/evfuXWn1j42NpX///vj6+trGL1q5u7uTk5NTaJ/s7Gzb8yUpy/4HDx5Ep9PRokULPD09adCgAceOHWPZsmU0atSIsWPHkpSUxKVLlxg9enS5XmtZVfTa5uTk8NRTT7Fu3TqSk5Np3rw5H3/8MZ07dy7yfJV5bZs1a0azZs0AeOihh7j77rsZOHAgO3fuRFGUm/7a5teiRQvS0tJ46aWX2LRpEwsWLLAFb2X9fBanMq5tRa9ZUcLDw7nvvvtYtmwZJpPJ7vPviJLarqzv/+JURttt3rzZ4Wz58ePHbZ+dymrz0j6PQoibgwTdQghRTocOHSIkJITQ0FC77QcPHqRFixaFsiQHDx60G7MaEhLClClTWLRokUPny83NJTEx0aGyQUFBJX6JTklJoW/fviQnJ7Nly5ZCryEkJMS2jFN+MTExAIXKF1SW/Q8dOkTDhg3x9PQEtC/zy5Yt48CBA7abAdZlp4ob83vp0iUmT57Mli1b8Pb2ZsSIETz//PNlDiTy16ki19ZoNFK/fn22bt1KWFgYP//8MwMHDiQqKsqWLc+vMq9tQUOHDuWJJ57g1KlTNG3a9Lq7tlD519eqRYsWAHz00UcMGDCAMWPG2L3Osnw+i1MZ17ai16w4derUITc3l4yMjEJjl0tTUtuV9f1fnMpou2bNmjF37lyHjpG/V0tVtXnBz6MQ4iZRzbOnCyHEdatdu3Zqnz59Cm0PCQlRH3vsMbttubm5qrOzs/r2228XKv/EE0+okydPLvV81mV/HPk5d+5cscfJyspSu3Xrpnp4eKj//PNPkWVefPFFVa/XqykpKXbbp02bpgLqhQsXSqxrWfYPDg5WBw8ebHv86quvqoDaqVMn27ZPP/1UBdQjR44UOtf58+fVJk2aqN99952amJionj17Vn3sscfUIUOGlFjHklTWtS247549e4p8rrKubVE++eQTFVB37typqur1dW1VtWqur1VGRoYKqH5+fmp0dHSRZYr7fN57772qp6en6unpqXp4eKhAkZ+nyri2Fb1mxRkyZIjq5uammkymMu/rSNvll//9fy3brryqqs0Lfh6FEDcHyXQLIUQ5mEwmjh07VqibcUJCAjExMYXGpx4/ftw2oVR5Vcb4RpPJxPDhw9m+fTvLly8vtrvn0KFDmTFjBnPmzOHFF18EtC6jc+fOpVOnTtSpU8dWNjMzkwsXLhAYGGibAd3R/WNjY4mLi7Nrl6FDh+Ls7Mx9991n23bo0CFcXV2LzAy98sorTJ48mVGjRgFal9Svv/6ae++9lz/++IP+/fs71Gb526iyr21kZCSJiYmEh4cX+XxlXNu4uLhCs0YbDAbmz5+Pu7s7zZs3B66vawuVf33z+/rrrwFtGEFZM5fLly+3/X/8+PHExsbaZrbOrzKuraNtXtT1Am2m+IKT1B08eJAVK1bQt2/fcs1FUJa2K/j+v5ZtV14V/Zw4+nkUQtwkqjvqF0KI69Hx48dVQF20aJHd9nXr1qmAunnzZrvtCxYsUAH17NmzhY7laKa7MjzzzDMqoA4cOFBdsGBBoZ/8hg0bpjo5OakvvfSSOnv2bLVLly6qk5OTumnTJrty1mxUwdfgyP5r1qxRAXXZsmUl1vvWW29V27VrV+Rz9erVU41Go6qqqjp//nx1+/btqqqq6tKlS9Vnnnmm2GMCao8ePQptr8xrq6qqmpmZqXbs2FGdMmVKsXWpDIMGDVLvuOMOdcqUKerXX3+tvvPOO2qzZs1UQP3www/tyl4v11ZVy3d9i7u2+Z0+fdqWZe3YsWOx5Ur7fL744otqv3791JycnBLPV1GOtHlx16tXr15qv3791KlTp6pz5sxRn332WdXDw0P19fVVjx07Zle2MttOVUt+/1+rtiuvinxOyvJ5FELc+CTTLYQQ5WCdaKtgdtM6W3PB7YcPH8bHx6dSl7QpD+vasr///ju///57oefzj8ucP38+b7zxBgsWLCApKYlWrVqxcuVKunfv7tC5HNm/uPbKz2w2c/ToUYYPH15sGWum7ttvv2XAgAHcdtttuLi4YDAYiiyfnp4OUOSSQJV5bQ0GA8OGDSM8PJw333yz2PpXhuHDh/Ptt98ya9Ysrl69ire3N+3bt+f9998vNCHc9XRtoWzXt6Rra6WqKo8++iiurq4MHz6cJUuWoKpqmSe2mjJlCnv37mXVqlW4uLiUad+yqsg1GzRoEAsXLuSjjz4iNTWVoKAgBg8ezOTJk+16X1R225X0/r+WbVdeFWnzsnwehRA3geqN+YUQQlzLTPeNaODAgerq1asLbR87dqz6448/FrnPH3/8oSqKoh46dKjK6mUymdThw4erAwYMUA0GQ5Wd50ZX1uvryLX94osvVECdP3++unjxYhVQz5w5U2TZ4j6f06dPVzt37qympaU5/mL+5Sqz7Up6/9+IbSeEECUp34KiQgghKsxoNJKdnY3JZLL7vyibd999l4kTJ7Ju3TpUVSU7O5u33nqLs2fPMnTo0CL32bBhAyNGjKBly5ZVVq8nnniCmJgYlixZgpOTdCwrr7Je39KubVRUFP/73/8YOHAgDz74oK3cvn377MqV9PmcNWsWixcvZvXq1WWajfvfrrLaDop//9+obSeEECWq7qhfCCFuVpMnTy40A+/cuXOru1rXpYMHD6p33323WrNmTTU0NFR9+umn1fT09GqrT1RUlAqobm5utlmaPT09C40HF46prOtrNpvVO+64Q/X391cvX76sqqqqGgwG1cvLS23SpIk6e/Zs23FL+nz6+vqqrq6udte2uJnpbxRlabuS3v83Y9sJIYSiqqpaXQG/EEIIIcS1Mnv2bJ588knmz5/Pgw8+aNs+b9483njjDeLj40lLS8PZ2bkaa/nvJG0nhBDlJ0G3EEIIIYQQQghRRWRMtxBCCCGEEEIIUUUk6BZCCCGEEEIIIaqIBN1CCCGEEEIIIUQVkaBbCCGEEEIIIYSoIhJ0CyGEEEIIIYQQVUSCbiGEEEIIIYQQoopI0C2EEEJc59LT09HpdHz88cfVXZXrktlsZurUqTRq1AhnZ2caNWpU3VUSQghxA5GgWwghhMgnJyeHV155hdDQUNzd3enUqRNr1651aN/09HQmT55Mnz59CAgIQFEU5s2bV6jcxo0bURSlyJ8dO3aUuc5HjhxBVVVatmxZ5n0rg6OvuySOtntltx3Al19+yZtvvsngwYP57rvvmD17drmOcy3t3r2bCRMmEBERgaenJ3Xr1uWBBx7g1KlThcpW5D1dlv3nzZuHoijs2bOnyOP07NmTFi1aOP4ihRDiBuFU3RUQQggh/k3GjRvH0qVLefbZZ2ncuDHz5s2jX79+bNiwgdtvv73EfRMSEnj77bepW7curVu3ZuPGjSWWf/rpp+nQoYPdtvDw8DLX+fDhwwDVFnSX9XUXpaztXlltBzB37lzuuusupk+fXq79q8P777/Ptm3bGDZsGK1atSI2NpYvvviCdu3asWPHDrvgtiLv6crYXwghbnqqEEIIIVRVVdWdO3eqgDp9+nTbtqysLLVRo0Zq586dS90/OztbjYmJUVVVVXfv3q0C6ty5cwuV27BhgwqoS5YsqZR6T5w4UQ0KCqqUY5WHo6+7OGVp98puu6ysLFWv16tTp06tlONdK9u2bVNzcnLstp06dUp1dXVVR48ebdtW0fd0WfafO3euCqi7d+8u8lg9evRQIyIiHHp9QghxI5Hu5UIIIarFxo0b6devH35+fgQEBDBgwADOnDlTrXVaunQper2exx9/3LbNzc2NRx99lO3bt3Px4sUS93d1daVWrVplOmdaWhpGo7Fc9bU6fPhwtWW5oXyvO7/ytntF2+7RRx/F3d0dk8nE66+/jqIodO7cudzHu5a6dOmCi4uL3bbGjRsTERHB8ePHbdsq+p6u6P7FiYqKKnaYgKIo5TqmEEL8W0n3ciGEENfcvHnzePTRR7nrrruYOnUqmZmZfP755/Tu3Ztjx47h7u5e5mMaDAZSUlIcKhsQEIBOV/i+8/79+2nSpAk+Pj522zt27AjAgQMHqFOnTpnrVpyHH36Y9PR09Ho93bp1Y/r06dx6661lPs7hw4cZM2ZMuepQGe1WUeVp98pou9GjR+Ps7Mzs2bP59NNPCQgIoF69ehV7MaWoyvZWVZUrV64QERFh21bR93R59k9JSSEhIaHQsQwGg+3/QUFBLFiwoNDzzz33XKGbCUIIcb2ToFsIIcQ1deTIEZ544gneeustXn/9ddv2Pn360Lp1a1avXs3gwYPLfNxt27bRq1cvh8qeO3eO+vXrF9oeExNDSEhIoe3WbZcvXy5zvYri4uLCkCFD6NevH4GBgRw7dowZM2bQrVs3/vnnH9q2bevwsWJiYrh69Wq5J6iqjHarqLK0e2W23R133MH69evx9PRkwoQJVXJDoaCqbO+FCxcSHR3N22+/bdtW0fd0efbv3bt3scez3hDw9PQsdKNo/PjxpKenl2mSNyGEuB5I0C2EEOKask649cQTT9hlw0JDQ3F2dubs2bPlOm7r1q0d/rJeXFforKwsXF1dC213c3OzPV8ZunTpQpcuXWyP7733XoYOHUqrVq2YNGkSa9ascfhYhw4dAso/iVpltFtFlaXdK7PtQGu/iIgIu4C7fv36/PDDD1UySVhVtfeJEycYP348nTt3ZuzYsbbtFX1Pl2f/mTNn0qRJk0LbX3jhBUwmU5HnmT9/Pl9++SUffvihwzclhBDieiFBtxBCiGsmJyeHP/74g8zMTIKDg4ss4+3tbft/fHw848aNY+PGjYSFhfHll19y5513Frmfv79/iRk2R7i7u5OTk1Noe3Z2tu35qhIeHs59993HsmXLMJlM6PV6h/Y7fPgwiqLYdSm+1u1WURVt9/K2HcDBgwe55557ylbhCqiK9o6NjaV///74+vraxmBbVbRty7N/x44di+zq7+/vX2S38wMHDvDkk08ycuRInn/++RLrI4QQ1yMJuoUQQlwzZ8+eJTMzk3feeYfbbrutyDKtW7e2/X/8+PHUqlWL+Ph41q1bxwMPPEBkZCQBAQGF9svNzSUxMdGhegQFBRUZmIWEhBAdHV1oe0xMDKBl46tSnTp1yM3NJSMjo9AY2uIcPnyYBg0a4OXlZdt2rdutoiqj3cvTdsnJyVy8ePGaTkJX2e2dkpJC3759SU5OZsuWLYXaqqJtW9WfiaSkJIYMGUKTJk345ptvKnQsIYT4t5KgWwghxDWTlpYGwC233FJqti89PZ3ffvuNs2fP4uHhwb333kvLli1Zvnw5Dz/8cKHy//zzT4XHyrZp04YNGzaQmppqF7jt3LnT9nxVOnv2LG5ubnYBdGkKzlxeHe1WUZXR7uVpO2vX/FatWhVb5ujRozz55JMcPnyYRo0a8dlnn9G1a1e+/vprNmzYwKJFizAYDPj5+fHKK6/w5ptvcurUKXr16lVksFqZ7Z2dnc3AgQM5deoU69ato3nz5oXKVLRtq/IzYTabGT16NMnJyaxbtw4PD49yH0sIIf7NJOgWQghxzdSvXx9FUfjll18YMmSI3XNGo5G0tDT8/f0BiIyMxMvLi7CwMFuZli1bcvTo0SKPXRljZYcOHcqMGTOYM2cOL774IqB1iZ87dy6dOnWyzdKcmZnJhQsXCAwMJDAw0KFz5hcfH09QUJDdtoMHD7JixQr69u3r8IReJpOJ48eP079/f9u26mi3siiq7Rxtd6i8trPuB8UH3bm5uQwcOJBnn32Wv//+m2XLljFw4EDOnDlDt27dbBOW7du3j5o1a7J161YAtmzZQrdu3Yo8ZmW1t8lkYvjw4Wzfvp3ly5cXu9RZWdq2otemrN566y3+/PNPVq9eTYMGDcp9HCGE+LeToFsIIcQ1ExwczMiRI1m0aBGpqan07dsXk8nE6dOnWbZsGT/++KNt8qr09PRC3YR9fHy4evVqkceujLGynTp1YtiwYUyaNIm4uDjCw8P5/vvviYqK4ttvv7WV27VrF7169WLy5MlMmTLF7hhffPEFycnJtlmdf//9dy5dugTAxIkT8fX1Zfjw4bi7u9OlSxeCg4M5duwYc+bMwcPDg/fee69QvRRFoUePHmzcuNFue2RkJNnZ2YUy3de63cCx1w1Ft52j7Q6Uqe2KazerQ4cOUbt27SK73YOWzTWbzTz99NO2c3/yySesWbOGkSNHkpOTw7lz59iyZQtPPPEEn332GSaTiS1bthQ7CVtltfcLL7zAihUrGDhwIImJifzwww92z1tnBi9L21b02pTF4cOHeeedd+jevTtxcXHF1l8IIW4EEnQLIYS4pr777jtatGjBDz/8wEsvvYSHhwcNGzbkscceo127drZyXl5epKam2u2bmppapu7D5TF//nzeeOMNFixYQFJSEq1atWLlypV0797dof1nzJjB+fPnbY+XLVvGsmXLAC2Q8PX1ZdCgQSxcuJCPPvqI1NRUgoKCGDx4MJMnTyY8PNzueOnp6QBFLtt0+PBhALvlwqqr3Rx53SVxtN0dbbuS2s3q0KFDJXYtv3z5cqFMbr169Ww3Fm6//Xa2bNnCli1bmDRpEhs3bmT//v1s2bKF5557rsTXW1EHDhwAtJsbv//+e6Hn8wetFX1PV3T/oly9ehVVVdm0aRObNm0qsf5CCHG9U1RVVau7EkIIIURB6enpBAQEcO7cOWrXrg1Ar169eOihh4ocm3yjWrVqFQMGDODgwYMOTfgl7aYpa7vlZ10yTFVVHnzwQaKiomzPdenShYkTJzJy5Eg+/vhjjhw5wtq1azlz5gzTp08nOTmZOXPmkJiYeE3W/RZCCPHvJ38NhBBC/Ct5eXlx3333MXnyZLKysli5ciWHDh3ivvvuq+6qXVMbNmxgxIgRDgeO0m6asrZbUTp16gRoXeeNRiNLlizh+PHj9OnTB4Bu3bqxZMkSwsPDcXZ2pnv37nz11Vd06dJFAm4hhBA20r1cCCHEv9aXX37J2LFjqVGjBmFhYfz000/Fjr+9UU2fPr3M+0i7la/dCnJxcWHFihU89dRTvPbaazRq1IgVK1bYJvtr27Ytqqraxm936NABg8FQ7HhuIYQQNyfpXi6EEEIIIYQQQlQR6fskhBBCCCGEEEJUEQm6hRBCCCGEEEKIKiJBtxBCCCGEEEIIUUUk6BZCCCGEEEIIIaqIBN1CCCGEEEIIIUQVkaBbCCGEEEIIIYSoIhJ0CyGEEA5QVRUvLy9eeeWV6q6KEEIIIa4jEnQLIYQQDoiKiiIjI4OWLVtWd1WYNm0aiqLQokWLIp/PycnhlVdeITQ0FHd3dzp16sTatWvLXa44ju4/b948FEVBURS2bt1a6HlVValTpw6KojBgwACHz389q0jbb9y40daeBX927NhhV3b37t1MmDCBiIgIPD09qVu3Lg888ACnTp2qipclhBCiCE7VXQEhhBDienD06FGAag+6L126xP/93//h6elZbJlx48axdOlSnn32WRo3bsy8efPo168fGzZs4Pbbby9zuYqex8rNzY1FixYVem7Tpk1cunQJV1fXMrTE9a2ibQ/w9NNP06FDB7tt4eHhdo/ff/99tm3bxrBhw2jVqhWxsbF88cUXtGvXjh07dhR740YIIUQlUoUQQghRqvfee091cnJSc3JyqrUew4cPV++44w61R48eakRERKHnd+7cqQLq9OnTbduysrLURo0aqZ07dy5zueKUZf+5c+eqgDp48GA1MDBQNRgMds//5z//Udu3b6/Wq1dP7d+/f+mNcJ2raNtv2LBBBdQlS5aUWnbbtm2F3rOnTp1SXV1d1dGjR5e98kIIIcpMupcLIYQQBfz000+0adMGNzc32rdvz65duzh69ChNmjTBxcWl2uq1efNmli5dyieffFJsmaVLl6LX63n88cdt29zc3Hj00UfZvn07Fy9eLFO5ip4nv5EjR3L16lW7btS5ubksXbqUUaNGFSo/ZcoUFEXhxIkTPPDAA/j4+FCjRg2eeeYZsrOzC5WPjo7m0UcfJTQ0FFdXVxo0aMBTTz1Fbm5uia/Faty4cXh5eTlUtiIq2vb5paWlYTQai32+S5cuhd6zjRs3JiIiguPHj5e98kIIIcpMupcLIYQQ+Xz88cc8//zzDBo0iP/+978cOnSIAQMG4OfnR7t27cp8PIPBQEpKikNlAwIC0OmKvh9uMpmYOHEijz32WIld3Pfv30+TJk3w8fGx296xY0cADhw4QJ06dRwuV9Hz5Fe/fn06d+7M4sWL6du3LwCrV68mJSWFESNG8NlnnxV5rgceeID69evz7rvvsmPHDj777DOSkpKYP3++rczly5fp2LEjycnJPP744zRr1ozo6GiWLl1KZmZmpd0sqYzrWdG2t3r44YdJT09Hr9fTrVs3pk+fzq233lrqfqqqcuXKFSIiIhx6HUIIISpGgm4hhBDC4sCBA7z88su8+uqrTJs2zbbdbDYza9YsHnrooTIfc9u2bfTq1cuhsufOnaN+/fpFPvfVV19x/vx51q1bV+IxYmJiCAkJKbTduu3y5ctlKlfR8xQ0atQoJk2aRFZWFu7u7ixcuJAePXoQGhpa7LkaNGjA8uXLARg/fjw+Pj58+eWXvPjii7Rq1QqASZMmERsby86dO+0Cz7fffhtVVUt8LWVRGdezom3v4uLCkCFD6NevH4GBgRw7dowZM2bQrVs3/vnnH9q2bVvi/gsXLiQ6Opq3337bodchhBCiYiToFkIIISymTZuGr68vr732mt32Hj16MGvWrHJNota6dWuHZ6WuVatWkduvXr3Km2++yRtvvEFQUFCJx8jKyipyQjI3Nzfb82UpV9HzFPTAAw/w7LPPsnLlSvr06cPKlSuLzXBbjR8/3u7xxIkT+fLLL1m1ahWtWrXCbDbz22+/MXDgwCIzvYqilHj8sqiM61nRtu/SpQtdunSxPb733nsZOnQorVq1YtKkSaxZs6bYfU+cOMH48ePp3LkzY8eOdeRlCCGEqKCbPujevHkz06dPZ+/evcTExPDrr78yaNCgKjvfu+++y7Jlyzhx4gTu7u506dKF999/n6ZNm9rKzJkzh0WLFrFv3z7S0tJISkrCz8+vyuokhBBCW8Jp1apVPP7443h4eNg9Zx0zmz/oPnPmDE2bNiU9Pd0WLBXF39+f3r17V6hur7/+OgEBAUycOLHUsu7u7uTk5BTabh0D7e7uXqZyFT1PQUFBQfTu3ZtFixaRmZmJyWRi6NChJZ6rcePGdo8bNWqETqcjKioKgPj4eFJTU6/JTNyVcT0r2vZFCQ8P57777mPZsmWYTCb0en2hMrGxsfTv3x9fX1/buHIhhBBV76YPujMyMmjdujWPPPIIgwcPrvLzbdq0ifHjx9OhQweMRiOvvvoqd999N8eOHbMt/5KZmUmfPn3o06cPkyZNqvI6CSGE0ILozMxM2rdvX+i5PXv24OXlRYMGDWzbDh48SNOmTUsMuEGbKCwxMdGhOgQFBRUKhCIjI5kzZw6ffPKJXbfj7OxsDAYDUVFR+Pj4EBAQAGhdlKOjowsdOyYmBsDWjdvRcsWpyP6jRo3iP//5D7GxsfTt27fMN5YrM3NdVhW9nlDxti9OnTp1yM3NJSMjo9B48ZSUFPr27UtycjJbtmwp9zmEEEKU3U0fdPft29c2mUtRcnJyeO2111i8eDHJycm0aNGC999/n549e5brfAW7fM2bN4/g4GD27t1L9+7dAXj22WcB2LhxY7nOIYQQouwyMzOL3J6RkcH8+fOJiIiwC/YOHjxI69atSz3uP//8U6ExwNHR0ZjNZp5++mmefvrpQvs0aNCAZ555xjajeZs2bdiwYQOpqal2gdfOnTttz5elXHEqsv/999/PE088wY4dO/jpp59KPA9oNx7y3/A4ffo0ZrPZ1lZBQUH4+Phw5MiRUo9VEr1ej9lsLrFMRa8nVLzti3P27Fnc3NwKzcCenZ3NwIEDOXXqFOvWraN58+blOr4QQojyuemD7tJMmDCBY8eO8eOPPxIaGsqvv/5Knz59OHz4cKHubuVhnQHVmqEQQghRPerVqwfA33//zZgxY2zbp06dSmJiYqHx3IcOHaJTp06lHreiY4BbtGjBr7/+Wmj766+/TlpaGp9++imNGjWybR86dCgzZsxgzpw5vPjii4B2A3nu3Ll06tTJNiu2o+VAuyFx4cIFAgMDCQwMLPP+BXl5eTFr1iyioqIYOHBgqe0yc+ZM7r77btvjzz//HMB201yn0zFo0CB++OEH9uzZU2hct6qqDmXHa9SoQVZWFpmZmYWGGFhVxpjuirZ9fHx8obH9Bw8eZMWKFfTt29duxnSTycTw4cPZvn07y5cvp3Pnzg7VXQghRCWq5nXC/1UA9ddff7U9Pn/+vKrX69Xo6Gi7cnfeeac6adKkCp/PZDKp/fv3V7t27Vrk8xs2bFABNSkpqcLnEkIIUbq7775bVRRFffLJJ9XZs2er999/vxoUFKQC6qeffmpXtkGDBuqqVauqqaaq2qNHDzUiIqLI54YNG6Y6OTmpL730kjp79my1S5cuqpOTk7pp06ZylbP+PZo8eXK59p87d64KqLt37y7xNdWrV0/t37+/7fHkyZNVQG3ZsqU6cOBAdebMmeqYMWNUQB01apTdvpcuXVJr1aqlenh4qM8++6w6e/ZsdcqUKWpERITd31FA7dGjR5HnX7NmjQqoY8aMUb///ns1Nze3xPpWREXavlevXmq/fv3UqVOnqnPmzFGfffZZ1cPDQ/X19VWPHTtmt/8zzzyjAurAgQPVBQsWFPoRQghR9STozqdg0L1y5UoVUD09Pe1+nJyc1AceeEBVVVU9fvy4CpT488orrxR5vieffFKtV6+eevHixSKfl6BbCCGurZiYGPXee+9Vvb291Ro1aqjDhw9XFy5cqALq+vXrbeVSUlJURVEK3ZS9lkoKurOystQXX3xRrVWrlurq6qp26NBBXbNmTbnLFRd0O7p/RYPuY8eOqUOHDlW9vb1Vf39/dcKECWpWVlah/c+fP68+9NBDalBQkOrq6qo2bNhQHT9+vJqTk6OqqqqmpaWpgDpixIhi6zBt2jQ1LCysyv/+VqTtP/30U7Vjx45qQECA6uTkpIaEhKhjxoxRIyMjC+3fo0ePEr+jCCGEqHqKqlbi4pXXOUVR7GYv/+mnnxg9ejRHjx4tNBGKl5cXtWrVIjc3l7Nnz5Z43Bo1ahTqBjZhwgSWL1/O5s2b7cap5bdx40Z69eols5cLIcS/zLZt2xg0aBDx8fHVXZUb2pQpU3jrrbeIj4+3da2uiFWrVjFgwAAOHjxYruXfhBBCiPKQMd0laNu2LSaTibi4OLp161ZkGRcXF5o1a+bwMVVVZeLEifz6669s3Lix2IBbCCHEv9fBgweJiIiwLfEE2rhiFxeXaqyVKM2GDRsYMWKEBNxCCCGuqZs+6E5PT+f06dO2x+fOnePAgQMEBATQpEkTRo8ezUMPPcSHH35I27ZtiY+PZ/369bRq1Yr+/fuX+Xzjx49n0aJFLF++HG9vb2JjYwHw9fW1rcsZGxtLbGysrV6HDx/G29ubunXryoRrQgjxL3Dw4EE2bdpkt57yoEGDipzwTPx7TJ8+vbqrIIQQ4iZ003cvt3bhLmjs2LHMmzcPg8HA1KlTmT9/PtHR0QQGBnLbbbfx1ltvletOeXGzp86dO5dx48YBed3pSiojhBBC3Ogqu3u5EEIIUR1u+qBbCCGEEEIIIYSoKrrSiwghhBBCCCGEEKI8JOgWQgghhBBCCCGqiATdQgghhBBCCCFEFbkpZy83m81cvnwZb2/vYic2E0IIIYQQQgghiqOqKmlpaYSGhqLTFZ/PvimD7suXL1OnTp3qroYQQgghhBBCiOvcxYsXCQsLK/b5mzLo9vb2BrTG8fHxqebaCCGEEEIIIYS43qSmplKnTh1bfFmcmzLotnYp9/HxkaBbCCGEEEIIIUS5lTZkWSZSE0IIIYQQQgghqogE3UIIIYQQQgghRBW5KbuXO8pkMmEwGKq7GjclZ2dn9Hp9dVdDCCGEEEIIISpEgu4iqKpKbGwsycnJ1V2Vm5qfnx+1atWSZd2EEEIIIYQQ1y0JuotgDbiDg4Px8PCQoO8aU1WVzMxM4uLiAAgJCanmGgkhhBBCCCFE+UjQXYDJZLIF3DVq1Kju6ty03N3dAYiLiyM4OFi6mgshhBBCCHEdOX81g3s+2YyvuzM7X+1d3dWpVjKRWgHWMdweHh7VXBNhvQYyrl4IIYQQQojri9Gskm0wk5Vrqu6qVDsJuoshXcqrn1wDIYQQQgghrk8mswqAk15CTmkBIYQQQgghhBCVyhp06ySRJkG3KNm4ceMYNGhQdVdDCCGEEEIIcR2xBt2S6Jag+4Yybtw4FEVBURRcXFwIDw/n7bffxmg0lrpvVFQUiqJw4MCBqq+oEEIIIYQQ4oZmVi1Bt2S6ZfbyG02fPn2YO3cuOTk5rFq1ivHjx+Ps7MykSZOqu2pCCCGEEEKIm4Ste7lOgm7JdN9gXF1dqVWrFvXq1eOpp56id+/e/Pzzz/j4+LB06VK7sr/99huenp6kpaXRoEEDANq2bYuiKPTs2dOu7IwZMwgJCaFGjRqMHz/ebkbxpKQkHnroIfz9/fHw8KBv375ERkbanp83bx5+fn78+eef3HLLLXh5edGnTx9iYmKqriGEEEIIIYQQ1caW6ZagW4JuR6iqSmausVp+VMubtbzc3d3R6XSMGDGCuXPn2j03d+5chg4dire3N7t27QJg3bp1xMTEsGzZMlu5DRs2cObMGTZs2MD333/PvHnzmDdvnu35cePGsWfPHlasWMH27dtRVZV+/frZBeaZmZnMmDGDBQsWsHnzZi5cuMCLL75YodcmhBBCCCGE+HcymbV/pXu5dC93SJbBRPM3/6yWcx97+x48XMp+mVRVZf369fz5559MnDiRYcOG0aVLF2JiYggJCSEuLo5Vq1axbt06AIKCggCoUaMGtWrVsjuWv78/X3zxBXq9nmbNmtG/f3/Wr1/Pf/7zHyIjI1mxYgXbtm2jS5cuACxcuJA6derw22+/MWzYMEBba/urr76iUaNGAEyYMIG333673O0ihBBCCCGE+PeS7uV5JNN9g1m5ciVeXl64ubnRt29fhg8fzpQpU+jYsSMRERF8//33APzwww/Uq1eP7t27l3rMiIgI9Hq97bE1aAc4fvw4Tk5OdOrUyfZ8jRo1aNq0KcePH7dt8/DwsAXcBY8hhBBCCCGEuLHIRGp5JNPtAHdnPcfevqfazl0WvXr1YtasWbi4uBAaGoqTU94lfuyxx5g5cyb/+9//mDt3Lg8//DCKAx8CZ2dnu8eKomA2m8tUr6KOUdGu80IIIYQQQoh/J8l055Gg2wGKopSri3d18PT0JDw8vMjnxowZw8svv8xnn33GsWPHGDt2rO05FxcXAEwmU5nOd8stt2A0Gtm5c6ete/nVq1c5efIkzZs3L+erEEIIIYQQQlzPTKqs020lTXAT8ff3Z/Dgwbz00kvcfffdhIWF2Z4LDg7G3d2dNWvWcOXKFVJSUhw6ZuPGjbnvvvv4z3/+w9atWzl48CBjxoyhdu3a3HfffVX1UoQQQgghhBD/YmazdC+3kqD7JvPoo4+Sm5vLI488YrfdycmJzz77jNmzZxMaGlqmgHnu3Lm0b9+eAQMG0LlzZ1RVZdWqVYW6lAshhBBCCCFuDtK9PI+i3oQDa1NTU/H19SUlJQUfHx+757Kzszl37hwNGjTAzc2tmmpYdRYsWMBzzz3H5cuXbV3K/61u9GshhBBCCCHEjWrNkRie/GEft9bzZ+lTXaq7OlWipLgyv+tjoLKosMzMTGJiYnjvvfd44okn/vUBtxBCCCGEEOL6ZbR2L5dMt3Qvv1l88MEHNGvWjFq1ajFp0qTqro4QQgghhBDiBmaSoNtGgu6bxJQpUzAYDKxfvx4vL6/qro4QQgghhBDiBmZbp1uCbgm6hRBCCCGEEEJULpNZ+1cns5dL0C2EEEIIIYQQonKZpXu5jQTdQgghhBBCCCEqlcnSvVwy3RJ0CyGEEEIIIYSoZHkTqVVzRf4FpAmEEEIIIYQQQlQqmUgtjwTdQgghhBBCCCEqlTXTLd3LJegWQgghhBBCCFHJZJ3uPBJ034BiY2OZOHEiDRs2xNXVlTp16jBw4EDWr19f3VUTQgghhBBC3ARs3csl041TdVdAVK6oqCi6du2Kn58f06dPp2XLlhgMBv7880/Gjx/PiRMnqruKQgghhBBCiBucbZ1uyXRL0H2j+e9//4uiKOzatQtPT0/b9oiICB555JFqrJkQQgghhBDiZiGZ7jwSdJdFbkbxzyl6cHZzsKwOnN1LL+viWfT2YiQmJrJmzRqmTZtmF3Bb+fn5lel4QgghhBBCCFEetonUJNMtQXeZ/F9o8c81vhtGL8l7PD0cDJlFl613Ozz8R97jT1pC5tXC5aaklKl6p0+fRlVVmjVrVqb9hBBCCCGEEKIyGWWdbhtpghuIaunCIYQQQgghhBDVyWwJup10EnJKprssXr1c/HOK3v7xS6dLKFvgjffs4fLXKZ/GjRujKIpMliaEEEIIIYSoViZV1um2ktsOZeHiWfxP/vHcpZZ1d6xsGQUEBHDPPfcwc+ZMMjIKjxNPTk4u8zGFEEIIIYQQoqzM0r3cRprgBjNz5kxMJhMdO3bkl19+ITIykuPHj/PZZ5/RuXPn6q6eEEIIIYQQ4iYgE6nlke7lN5iGDRuyb98+pk2bxgsvvEBMTAxBQUG0b9+eWbNmVXf1hBBCCCGEEDcBkywZZnNdZrrfffddOnTogLe3N8HBwQwaNIiTJ09Wd7X+NUJCQvjiiy+IiooiJyeHS5cusXz5cnr27FndVRNCCCGEEELcBPK6l0vQfV0G3Zs2bWL8+PHs2LGDtWvXYjAYuPvuu4scxyyEEEIIIYQQ4tqSidTyXJfdy9esWWP3eN68eQQHB7N37166d+9eTbUSQgghhBBCCAFgMmv/Sqb7Os10F5SSkgJos3cLIYQQQgghhKhe0r08z3WZ6c7PbDbz7LPP0rVrV1q0aFFkmZycHHJycmyPU1NTr1X1hBBCCCGEEOKmI93L81z3me7x48dz5MgRfvzxx2LLvPvuu/j6+tp+6tSpcw1rKIQQQgghhBA3lzKv023MhXNbwGyuukpVk+s66J4wYQIrV65kw4YNhIWFFVtu0qRJpKSk2H4uXrxY6rFVy50ZUX3kGgghhBBCCHF9KnOme8UE+H4A7J9fhbWqHtdl0K2qKhMmTODXX3/l77//pkGDBiWWd3V1xcfHx+6nOM7OzgBkZmZWap1F2VmvgfWaCCGEEEIIIf5dVFXlnzMJxKVm2203lXVM96GftH+3f1mZ1ftXuC7HdI8fP55FixaxfPlyvL29iY2NBcDX1xd3d/cKHVuv1+Pn50dcXBwAHh4eKDIO4ZpSVZXMzEzi4uLw8/NDr9dXd5WEEEIIIYQQRVh77AqPL9hLoyBP1r/Q07bdrJYh6M7fpVw1VXINq991GXTPmjULgJ49e9ptnzt3LuPGjavw8WvVqgVgC7xF9fDz87NdCyGEEEIIIcS/z8ZT8QCcic8gK9eEu4uWMDOayhB0GzLy/j94TqXXsbpdl0F3VY/1VRSFkJAQgoODMRgMVXouUTRnZ2fJcAshhBBCCPEvl5KVFy9dzcghzMUDyJfpdqTXcLZldSmdE4S2q/Q6VrfrMui+VvR6vQR+QgghhBBCCFGM2JS8sdzJmQbC/LX/W8d06xwd093oDu3fG3Bo73U5kZoQQgghhBBCiOqXlp2X6U7KzLX939K73LFMt29tGLEIIu6HHV9VdhWrnWS6hRBCCCGEEEKUS1q20fb/pMy8ANxc1tnLjdmwYqL2/1sfASeXSqtjdZNMtxBCCCGEEEKIcskfdCfnz3SXtXu5s2fe//NPrHYDkKBbCCGEEEIIIUSZmc0q6Tl5QXdGTt5yX6ayTKS2bwFMD897nCtBtxBCCCGEEEKIm1x6rtHucZYhL+jO617uwIGyUyAnJe+xBN1CCCGEEEIIIW52+buWA2TlC8KtmW6dI5nunFT7x7npFa7bv4kE3UIIIYQQQgghCsnIMWIwmYt9Pv/M5VBcptuBoLtgZtuYW3S565QE3UIIIYQQQggh7JyITaX91LUM/HxrsYF3eoFMd2Zu4THdDk2kZsy2f2zKKVtl/+Uk6BZCCCGEEEIIYee3/ZfJNpg5EZvGwYvJRZYp2L08O1+m2xqnOzSRmjXobtgLRi2Bmi3LU+V/LQm6hRBCCCGEEELYORydbPv/wUspRZbJn9ku+LhQ9/JDS2D/wqJPZrRkthvfDU3uBs8a5av0v5RTdVdACCGEEEIIIUTV++toLKeupPF490a4OJWcfz0TlzfO+lxC0ROb5c9sA2QV1b1cUSDhNCx7THsioAHU62J/IP8GENYBfEIcfSnXFcl0CyGEEEIIIcQNLi4tm6cW7mPGX6dYtu9SiWWNJjNxaXnjrGOSs4ssl23UgmwnSzY7y657uRZ0O+kVuLwvb6dLewof6I7X4LF14BkE+3+A+FMOvabrhQTdQgghhBBCCHGD23Qy3hYIb46ML7FsQnoulqIAXE4pJug2aAO3fd2dAcgx5E24Zj2XTlEg/mTeTklRxZ94xyxYPh7Oby2xftcbCbqFEEIIIYQQ4jqUkWPkse/3MGnZIdsY6uKcic/rLn48Jq3EsjEpWXaP41KLC7q1zLaPJejONRUOuvU6BcLvhMCm2hMlBd1Ortq/Rpm9XAghhBBCCCFENft5z0XWHb/C4l0X2XHuaollLyTmBd0XEzMxlrD+dqwls13bzx2A5CwDqlo4qM8pGHQb845ptpTXK4o2hrvP/4GbX15gnd+8AfBRc4jeqz2WoFsIIYQQQgghRHXbfuZqkf8vyvmrmbb/G80qMcV0GQdISNeC3iY1vQAta51aYHkwgKYxv7HKZRIdFK37eI6xiO7l1ogzvDf87zyMXFz4hOlXIDUa9C6WnXNLfC3XGwm6hRBCCCGEEOI6dOpKXjfxE7Eldxm/mJhp9zj/RGkAnFwNZi1otgbYQd6ueLjoAUjKKBAI56TR59z7NNedZ2zKlwDkGvMtGabm615+dhNE7ys+g22w1MXVR/vXWPwNgeuRBN1CCCGEEEKIm97KQ5d5b/UJUjINZd43IT2H81czSi9Ygi2R8by/5oQty1yarFwT5/MF0lEJBc6fehk2z4C/p5FtMNkC6fBgLXsdn5bvPJmJsHgkLBwKJiOpWVobeLs54++hZZ+TMgsE3Zf3o0cLsmvnnMGbzCIz3XqAH4bA170gPa7oF2MNst18LY9vrO7lsk63EEIIIYQQ4qZ29HIKExfvR1UhLdvAtPtbQkIk/PMZNBsITe4udt+TsWkMnfUP6blGZo1uT58Wtcp8/sgraYybuxuTWeVIdAoLHu1U6j7nEzPIP8z6QmImqqqiKAr88znEHoFDP4JPbRLbPQ+As16hQaAnp+PSiU/PF0Rf3AWokKItJZaTkQKAj5sz/p7ORCdnFQ668y39tfiWmeTsd8ZgMtvqYFsyzJgOZsuNjOXjISsJRv0EPqF5x7IG2W6WTLd0LxdCCCGEEELcjOLSssuVCf63+21/tC2AXXHgMkajCX4cDfvmw88PQdqVYvedtfE0aTlGVBVm/HWyyAnHSvPj7ou2IHVLZAIXrmaWskfeZGcNAj0BbTx1apZl3PWWD7WAGyA1mqSrWoY5wNOFYG9tIjO7THfCybx//y+U7tFzAPB2c8rLdGcUuO5XjgLwnmEEKTU7koszqqqNFwdsS445Zyda/uMJV45A7CHISrY/ltEyW3rrUTB0LrQdU+rrv55I0C2EEEIIIYQo1dbIBG5/bwNd3lvP4XOX4bfxsPQRSC95zefrwe6oJNv/03KMHItJgT7vahuMWXDstyL3M5jM/H0ir8v06bj0vAnLDiyG+FMOnX9LgXWzS5uJHOCKZRmvugEe+LhpHZjj0rK1gDbL8nos3bWzrmj1qOHpSqBXUUF3pPavdyiYcqiRfQHQZiUvtnu5JSt+QQ22rdMNeTOY2zLdhlTtCXc/cPW2FErPO47ZnJfZrt0OWgyGkNalvv7riQTdQgghhBBCOMhsVjl/NUMLKMqR0bye/d+q4+SazGTkmjj/6xQ48ANEbYXk8w4fQ1VV3ll5jJ7TN/DNlrOVVrdzCRlMWnaY77aeK3W96oJyjWaOxWiBYd0ADwCOxaRra0vf9bZW6OzGIvc9HpNKarYRX3dnOtYPAGDL6QRIPKd1pY7eW+r75Gp6DqeupKMoMLJjHQD2Wm8CpF7OK1ggOxybogXNIb5uBOXPXqdc1Ap4BEJQMwCMV7UguoaXi62s3dhxa9AdficAAYZYQMt0e1sC+vScArOX9/uAj31fJV71IyL2N+7W7QbyBd3WidSMlrHmrt7gYgm6c1LzjmM2QGhbCG4OTm7FN9R1TIJuIYQQQohyKk830ptVjtFU+Ev7dcZkVhk7dxc9pm/k+xnPo04L0dYXzk4tfeciXEnNZsGO8xy9nFLJNa18569m2AJTFwz0SFmhPdH3Awi71eHj/LIvmm+3niPqaiZT/zjO7qhEOL0O1r4JV46Vq27ZBhPj5u5i8a4LvL3yGHP/iSrT/qfj0sk1mvF2c6L3LTUBOHXFkokN66D9e/lAkfseidbapFWYL7c1qgHAoYvJsP8HUE1wYmWp57fOOl4vwIOeTYMBOBydArkZ8Gkb+P5e+GkMzGgMyRdt+8VaMt01fdwI9taC1bi0HEjWAmz86oKvFsSrlkA80MvVlrm2GyZgDe4bdAcg2HQFUPFxc8bLGnQXXDIspDUbnLtSR4mjzf43echpLQC5lvW/zbZMt6UtXb3zMt05+TLdTq7w+Eb473Zt6bCjv8KFnaW22/WkTBOprVixoswnuOuuu3B3dy/zfkIIIYQQ/1Zn4tMZP38H49M+4S7XY7i1H61lxBSluqv2r7TrXCKPfb+bzFwTr/e/hXFdG1R3lcrll72X2BKZQHMlinEZc1EUFS7u1ILGFoPLdKz4tBzu+2IbsanZ6HUKq+64QtP03RBxPzS+q9LqrKoqRrOKs75iubZNp7Tuz10a1SAkcSfeWVnkuAXhesu9ZTrOt1vP2T3euOpHOsS9CqiwZy48fQA8a5TpmEv3XrJbg3rO5jOM61JfW6rq4m64chhufaTY/aMss46HB3vRtJY2s3eDM/Ph1B1Quz2gQNplbeZtr2C7fY9Ybpi0qO1L8xAtoDwemwoJf2oFmt+nZXVPr4OIwUX+jjhuuZnRrJYPjS0zi59NSMd8bhs6Uw4kngXPIK0L9oXt4KcF0vFp+YJuHy17HZeWDTn5g+4wAJzTo4EO+Hk427qCJ2fl6y6emaD9G9oWFB2uai5BpODj7oS3azGZbrQbHrFoGf4QRRu7nWMokOnOH3TrLN3Qc4pZ3uz0elj9ktZudUufTO56Uaage9CgQWU6uKIoREZG0rBhwzLtJ4QQNxNVVTl+aDe+0ZsIbdEd5Qb6IyP+faKTs5i3aBFj46dTw8WA+4D3oeXQ6q7WdcVsVhm/cB+DkuYx0GkL5KDNcOxfHzo8Wt3VqzCTWeXjtac4eGgfL7suI6K2P7o7XgP/euU6XrbBxIRF+2zLFb298hi31g+gRexv0HIYuHhUYu2r1o+7tWDmGa916AwqZ3X1aPDcehSvoDIf6/O/I22Zyv5spenWmdoTBxfD6CUQ3rvC9Y1KyODJH/Zy6koaQ9uH8e6tmehTLkDTvnmzRDvowMVkADo2CKBN7knIghNeHWmt02ljcjOvQintcDoujeMxqbjodfz0xG3c/+U2+sV+BToV3Pxg2LwyB9ygBd0AL/dpytebz3IlNZt/ziTQLSAVvh8Izm7QakSx7zVrwF6/hieNgrzwIZ0Hk2fBolnwShQENITEMxB3vFDQHWlZJ7tZLW9uCdHaNPZKLKrTERSAel3howjITYPQdhBQ+IaTNdPdtJY3dQM8cNHryDaYSTu9A1+A+t3AIwAu74MLO6DVAwAkWtbNDvB0sY3TvpqeC7p8QbclQPfM1DLZPm7O+HlogW+KZVkwTEZoNRwy4sE7RBvXnXqJ2kqClum2BN1p+YPuxLMQuZbm2VkcUrWgu6aidYnPNZkwm1Vbr3pzzVZw91RttvITf2gb84/pzk/vlFenG0iZb3nFxsZiNpsd+vHwuH5+iYobx5GDuznx6X3Ef3Yn5sO/VHd1hCiR2awy8+vZhC/rQ+1dU1G+uxvz7rnVXa0bWmxKNm/M/pHdb93OlffaYtjx9U0zLjPXaOaVuWuYeOV1wtQY3HMSMC97AmIOVnfVriubIuOJiY3hIf1fAOw0a2Mm2Tz9hlhbduaG0yzYcIAZ6f+jZeJf6A7/BPsXlPt4vx+8TFxaDvd4n2N83YuYVZXIZe/A70/Dji8rseZ5coymSu/6fzExk30XknFTDPRWtKWSXs4aS2RG2cegZuWaWLJHCxS/fuhW3FycOW0OJdc1AFQzrHq5wkFHrtHMkz/s5URsGmYVNuw5jHn+IPj1cW295MzEMh3v0CUto9sqzJdm5tMA7DY10Zaaeq8uzOtf6jH+OaNNDtaxQQBt6/pzb1A8EbrzGPVu8PR+aNSrbC8SbVyy9YbA0HZh3NW8JnOcP8Jj7cugdwbPQG1SsTN/F3sM6/radQM8qO3vTriiBaiqT21w94der8LQ77QxxwVEWQL2BoGe1PH3wNNFT3NzJAqqFqz71oagplrhfEts5XcqX+DupNdRP1CLoQzRlt/NoW3yJhaLO27bL8nSPTzA0wU/93yBtHVMt19dLbv+9H6+qfkqoE2M5uteIOjWO8HAT2DEQnD1wuyldbEPUpIt3cu18nbdyy/tgdUvMyb3Z66o/gB4kYUH2eQYzbYsN4AS3By6TIQWQ7T2dPcH8mX8E07DJy3hm7vyMuHmAjOlX+fKFHSPHTu2TF3Fx4wZg49P2e6iCVERq7ftJnTZIJolbSQocQ9n/vgYs+H6/wJU5cyma3KauOQM4i+cAkP2NTnf9eD77VH8ek7HebUWcaofAOrqlyEpqlrrVZ3Sc4ycjkvDYBkTVplyjWae+2YNz11+gQ7qYWpmn+X05h/BUPrSLDeC3/ZH0+fqAnyUTM65NGaTqRU61Yj697RKPc+hS8l8/NcJ/lz3F9mXj1XbTY1sg4kz8em2SX0qy+KdF2ivO4WTXkdWwC08mDtJ+9KZFqN1Ia1kJrN6zcZCx6Zk8/nfkTzvtISaSjIXzUFMNT1EQocXy31Ma3D5pscvvBT3Co/qV7HtiuWL9e5vK/VvUFxqNg9+u5Omr69h3P99S9SPL8LWTyola7YlUut++2Cti+hzU0nU1WCv2oSd5yzBaxnOsTkyniyDiTB/d3rfEoxvh+HclfsBr9f5XgtIEs+UGCQ64qc9FzkRm0aApwvP39WEePx50fhfVCc3uHoa1k1x+FjpOUbOxGuZyRahPgSmaWOvN6bV1gK73DS4Ggm5Jf8u3W4Jum9rqGVGh/ocAeC4Rwctk1sO205r1+WWEB+CfdzoWdeJO3X7aB+3TPvdc8tAreDJVcUew5bpDvQg2NuNML12TXO9tK7ZtByqBYwFMvnpOUbbDOD1anii0ymE1/SmqWIJemu11P61jgu/tKvI819ItJ5fW/rLOpmbe+KxvONYJkQjLu93qnU2cX+PvOx1UmauNs7+0XXQbIDWrgENuZqrPe/t5oSPJejONpjJNhT+/OWEdeUPU0eSVC+83JxsmW6730MZ2nCDONWXDNww67VMe4CSRq7RbJu5HECXP+Ls/6HWe+C2J/O25aZr49BTLmk3SgDMN3Gme+7cuXh7eztcftasWQQGBpa5UiJPrsGEMauYMQ/CzsXETNz+epkAJZ0op0ZMMz3EgOQX+HFf8esq3swycoy88dsR3pjyP3g7gLiPu6Fa1lusbKnZBj75+htyP25F0HcdOD7jLm3M0U0uM9fIJ+siOaPWZs/dv7D6jj/ZZopAb87FtPXT6q5etfhx61Hun7aI3h9tptv7G9gTVbZMTGnmbjvHiOTZBCjpJHo1YYLhWfolPsvGcxmVep5/I1VVmb35DFvNLYn3bkbg4OlMVbUxjkrkn7alXypq/vYonpy5gl5bR3LP1mG4zelMzrz7ypxVq6hl+y4xbtpXJH/eE6YGkTKzt12GqLxyjCa2nk7gb3M7Iscdwu2hn6kd6Mdnxvs51PpNqHNbxStvoaoq3249R/upa3n8rQ/5+93BJC1+othsWWVYuPM8HqY0RjptBGC233N8Y+jDkr3R5TpeSpaBvReSCCKZ2in7ADhZozcrjJ3IcfbTxslWMLi0ysw1MubbnWyJTKCvbiff5r5E/RNfE3/+aF6X1QrYbfl91NtTy/LGBnVBRcelYzvhyy7wdU+Hj7XumPbd5K7mNVEUhf6tQlHRsfJUJsYWWtdh9s8vd11VVWW+ZTKxCb3CmdArnCY1vVieeyt/tf/KcvwfIC3WoeOdupKGqkKwtyvBHjqMXV9guakLO9KDSdYHgGewlqGPK34iNFVVbW3YqaHWhbxNlhaE/p7VSit09Qz89Qasf8fh1/rPaS2Q79ZYizlu4zB6RSXSXJtsrzDbbNyc/6fYY+Rluj3R6xSauiUDkO5Wq8RzW/cL8HSxZY/rBXjQTGcJuoMjtH9rt9f+LaJXUUaOkWRLxrq2v5bcrO3njh9peGZbvsPWbAGBTUDRQXYypF/BaDLbMtX+ni74WSZHS840aN2463QAnxDbeVItZX3cnPF2dUKn5Nuem6nNjG4J5mM7/o/xhmc54dICvU7JC7rzZ7oztJsdCSYfQEF1126a+KMF3eZ8N1udkk7Dpb3FLy1ntHwndHYDnbV7+U2c6RbXjsms0ueTzbwz5QWc3g8j9v9akbp/WXVX619t2R8r6aXsw4SOuo//SK17nicHF6b/eYKs3GuTyb1eZBtMPDxvNwt2nOeX7A4kq54Epxwi65v+kOT4sh+OMJlVps/9kScuTSJM0X5Bv5vWl1Ff78zr1nST+mVfNClZBurV8OCBLs0Y2bUJC12HAaAeWFxoaZDKlpmrZS7+LbMJ/7TjDHX/fIzpfIKLzkxsajavz/uD5DXTKiVTajCZWbN1OwN02wEIGP0NIV2Go6Jjxl8nq2UW6rjUbM4lZNhlBKrKkehUzsRnsEF3G27/3YJ30540b9GGH4x3sqb2RHDxrPA5dp1LZPKKo2SqLri5e5KNC7mqHtfzmzD/MPSa9XL5/eBl5i/5he/Ut2ivi8QFI77xuzHOHVDh33F7opLIzDUR5O3KLWFBKH516duiFgtNvfkm+45yjUctzmfrT/POymMkZxpwI5c7ctbjf/JH1G/v0jLElcxkVlm86yJD9FtwwQA1W3JLF63L8JqjsdoX8zKux7w1MgGTWWW43wltQ2hbbmvTklyc+cddmyXZkdmdHfHRX6c4dSWdtl5JzHSfjZNiZrOpJQ/H3F8pvR12WTLa3g1vgxZD0De9B4B/YhWIOwqxR4qfHKoAa3a8V7gfHFlG60Co5eNGZq6JQ4GWbtqR68r9mdkdlURkXDruznqGNXdDl3qJYe21sb2zzwVDnU7azNqHfnboeOfiteCyUZAXOLni1uM5PvB8CSNO2izf1oxu7KFijxGflkNCei46BVqE+oIxF+9M7fP4e3oz7WZ8+hVtfoQ932njxB1gnVG9bR0/APxjteB6k7mVNla69q2AAknninz/mswqVyzZ6jBL0NvQJRmAq3pLZjsnHU6u0dbdzseaIa9XI29Ibb0aHmw0tWZPQH+o31XbGHyLpRFOFvp7djk5C7BkoC3duGv7u9PI0sUd3zra+HtnN9tM5CSeJSXLYDuUn3sR47StzCZY/zaPJX2CO9n4uDuh0ym2bHdKlkGbLfz9erBIu+GTlm2w1QnIm728qEy3WUvIWoPuACWNXJN9ptvln4/hmzvg0I8UyRp0O7lJprskWVlZREcXvgN69GjVZM1uBnqdgtGsclbV7rDVyj2Pz/KHSfrzvWqu2b9TTEoWjSK/AyC54b3ogpswtnM96gS4k5OZxuFfZzj8h/Bm8N4fR9l1LhFvVyc+H3s7Kzr/xBFzfTwMSaQtnVCpXUGX7jrLqJj3cVdySandg7P/OcUpz46cjktn8vIjlXaeisrIMbL9zFU2n4q3dRWraod3rudx/e883tYDvU7BxUlH0079iTTXxsmUVWJXuIpIyzYwZcl2Fr/zEO5ftGT9tPt47qcD1+x1F+VMfDoX/5hOF/0xbnGO4eCzEXQNc2GR+j/8dnyQN/FKBfx9Io5eWWvRKyrmhr0gpDX/7RmOq5OOK9HnubziLW1m2mtg/4UkBn2xlY7/t55eMzbS6f/Wsem3r1Gvnqmyc644qP2d7n1LTbzdXUBRGNgqlNeNjzIt6Q5UN78KHd9sVnlz+RFUFe5o14ymL60nftwORiofkKR6obu8FzZV/d+wy8lZvLV0B587f46HkoOpQU8m1/yCo+Z6JOYomJMrltHffCoeUOnZJAidJVXUvYn2xXz72auVdvNm17lEPll/CoBJfZsx9eUX+dj3f/xuug1FNaP+8UKlZYitDkenkJCew/3Oloxg+7HcFVELRYFbLi/DPD0ctn5cxtehZSH7uFu+Eza+x9ZeP6a00Lad+rPCf3diUrKYv10L4L4J/hmdKRtjvW686PI6RxJU20Rb5JavV0t0chbRyVk46RTq3z4Mhn5HSJcRABxO9cDsUwdQIXpfqceKT8vhQmImigLtnc7A0odRvuxMl0Za0PLX1WDo/xE8tU0LtMph9ZEYAPq1DMH7yA/waStGZC4CYP/FZDJusWTTDy9x6HhnE7Su5Q2D8m7ONbLOsh2fntf1uYTfYcctk4XVD/TE3UUPTi4oL5/jUc+ZxFCDQxdTtG7YTu6Qlah1gS+F0WS2jYe2TmKmWLLJe8xNtWW33P3ygt6LhZehSkjPwWRW0esU22RkYTrtpshlLD1202Jh8XD44wW796q1W7i1O7j1/7+bu/CJxzNQ/3ZtY43wfFlq+78zlyxBd22/vCG8tf08yMaVba7dofHdeYXrdYVGd4DO2Tae28fNCSe9Dj93LdOdkZEJ696C7V9q2WJFB9u/pJ/hLwKVFFtg72ebwdxgC6C1sdaQmmVEwUxNV+0ctonUsvMF9JnaZzsRrd0Nd03jDZ9pHDA3Isdgtrtnoli/g7t4abOTfz8Q/nwtr4B1Lgwn17wx3ZLptrd06VIaN25M//79adWqFTt35r2ZH3zwwYoe/qb26Yg2TH/hvxwauZefne8DwH/7u2Tu+qGaa1a01GwDK/45xC/zPuavOZPY+cunpF6p3KxpcVbsPm2b1KRG72cBcNLreKRrA35w+T86Hv8/1MNLr0ldzGaVDSfjeHnpQUbO2cHj8/fw619/kxJ79pqcvzR7zyfSee9zvOP0HV8Nqc+dt9TkoT7dWNpgKjmqE97Rm+Hshko5V1auiWN/zeUW3UWynf3wHTWXhrVrMuvB9ugUlcxDy0n4Zmi1zlCZmWvki+Vb+GvaYH76dgYPfbeL295dzws/HyT1qmPd7sojOjmL1vEredV5Mfcnf2/bPrBNKJ8Yh/CccTyJde+p9PNeTc/hqS9+5eHDD/Go/g9ClUSOm8L4dX80/T7bon15qYaM75zf1jNRp31GnQd8iHtwQ94Z3pmfzXcAkPXXOw5nPYqz+tBlBuh2AKBrOwbQuuTd37Y2X7p8Su39H1dosihH/XEohplzvuTlKy/hoTPg5qwjIT2HevumkzuzK+rpyg2krHYcPc0w/UaGhee1Y+dGNXDR67iYmMW5hIp1sd9wMo4TsWl4uznxRv/mKE6u1KnfiOED7uEVw38AUP/5AhIiK3Se0ry3+gTDzH9SRxeP6lcX/fAFTBjzAP/lVXpkvs9fGY0qdPwDF5N5zmkpb5wfC/u07r9t6vjh4qTDKe0y8Ru+1LJGFaCqKm+vPIq7ms2ItkE80aMRoX7ujH38Bd52fZHFxl7aJE3LJ9qvdVtB2thYlUt+HbXMZbMBBHu70aq2L+mqOzpDRpn/PlgnuGqUbQm6699ORKgvfh7ObMxpgsnJQxsLX8HJ/L7efI5ck5nhYYnUuLwJFD1OAz/h8Z5NAPh7yybUuf1g/qByHf+wZRKxJjW98XDRAhAfN2dboJTkb+keHXOg1GPtu6DN8Nw42AvPK5YgPaw9XcO1mxH/nL2qzYJfo3zvVVVVWWvpvn53RE3tpqVqxju4Lk1reqOqsM3JskpG7CGHbjaetWS6GwR6autVx5+koZ8WHF1MysyrawmBsnVZLGtwDIBOj189LUt+KDpFy3Jau2Jf3FFqvaKuZpBjNOPurNcCX5PBNozkmFqPY9b1z2u3y3u9BcSmaFnWIC9XbYkxIFjVgtCLRi0Ixbe29q8hQwucC+xbyzfv5ki9GtqNifOJ+X6nOrtpqxsAxJ+wO390khZ0W7PsAKF+bhxV6/Miz8KAj/IK3z8LHvwV6nSwjecO8NSCbWumW58dD1s/grVvaF21FcU2Fj2IvKDbNplapiFvuTBPrZxr1N+cch3L9Mw3gLyMd3qOMe/GoqV7+VVVu54u4T056dGeFLy0THf+idRyLUG3q7c2qd25zfafeVum2x1CWsF9M6HHK9xIKhx0T506lb1793LgwAHmzp3Lo48+yqJF2p206uiqdyOJCPUltIYPrZqG0/PpOczXDwHAafVzqPGnqrl2ecxmlW+2nOWL916m7589GRI1hbsvf0mnw2/iNas1Z78ZV6XdZFVV5eeDV+mdO4O9LV6HkDa254a0D2O9qk1ekbH3pyqrg9XFxEymff4lKT+MZcmeC2w/e5W/jl3Bect7eH7Vnsjv/oOanVLl9SiOqqr88esi7tHvYZTTBrqG5v0KGDfwDhabtHFPaRs+qZTzrThwkVFG7cunU7dnbN0u29b15/FOQXzgPIfAS2sx7yv/uLWKiEvNZupnX/LQvge4X7eJB1030yjIE5NZ5fT+jTh/3ork7d+XfqByWHUohtt1hwHwaDXItr1hkBfnat7Nr8aurD1TueOMc41mnp+/hXdSX6eeLo5szzAYsZh+j75J42Av4tNy+OWrt0hf8tQ1Dbx3nL1K+/Pf4qoYyKrdFV2bkYDWFtERT5CmuuOedAKitpT7HDlGE+tOxDM69zUudngdmvSxPTeiY11+NGrBvXnfD1X62refucr3Py1mpv5juuiPseuOUxyecg+T+zQkDn9czVkYF42o9NnEY1KyCEvew3TnOXTbM8G23dPViQ4N/AkimYsb50L03nKf47tt5xijX8t3NZfin33Rtn1ouzBiQnqz3tQWk6pU6XjkI9EprDh4mW/M/YjuOg1lwCfg5kOQtyv9u7QhCze+t4xzLQ+zWeXY5VTaKGfwST9ny8S4OetpX9efXvoDBG9+FXZ9XaHX8efRKxyJTuU51+VMi34EItcC2pfrNwdG8I7xQS6pQZB6CXZ+VaFz5bclMh5QSOj0P3hyq208aKeGNdhmjsCMoo3ZdXAccLbBxNHLqYRwFfesWFD0ULsdep1C10aB5ODCeR9LgHVuc7nrnW0wsWSv9p573lObUZ6I+yEwnAc61MHTRc/BBB3qxV3aRFbleJ+fiNUCxttrpEL8Kdvkb7dY1mW+4FxfK+jAvAHWoLtdXX9t5m+AsI50DdeyqoejU+wzimV08koal5KycHXS0b1mDlzeDyjQpI9t3PP6C6o22dbDq8G99AnMrDflGgV5adnemR253azV/UJiVpmC7ub5g26gdR1fQJuAEYA6HbV/i8hKFz5m3lJbOp0CCafAlIPByYuLahBn4ix/Rzs9qb3W2/5b6BhXLMu21cwXOO9u9RZP5T7DQbPldTm757VT6uVC+9byydu3vmcujZVLJCUl2Q9r6PaCFkxaZzK3iC4q020JwK+kZhc7qWiSZbkw61huX0vQ7WVM1gp4BtnWBDd7aMF0DSXVFkDbdS/P0LLWeGjf05LxxFkxUUPV3qvWTLdZhSzrxGuW7PhV1RsXvc7WYw+wm0hNUfJlul198oYy5Z/A1DqMwslVW1e87Rhoki/DfwOocNBtMBioWVObVr59+/Zs3ryZ2bNn8/bbb6MUsfi7KJ9gbzdaPjSdLeaWuKi5JC/+zzWbcbokqdkGHv1+N1P/OM7OnPo4KyZiPZpwJLAvR/XN0KHS8NKvxH/aA7WSxwpbHb2sjVOM19ek8YBnbb9gQLsLndJIm7XSM2aH3S/KynY8JpVFX7zJa4mvMUj/Dx83PcYnw9swqU9TglxNOGGm8YWfSfioK8YrJ6usHiX5+0Qc/RPnAZDd9hEIbGx7rn6gJ+fCH8KkKnhf2qQt31ABqqqydusOQpWr5Oo9cepov3btE3e1ZRba+OWcvz8AY26FzldWCek5zJj5OW+lTcFHySTFvyXtHvmY9S/05JenujDKYzfu5ODz5zNkH15e6ec/dPgA9XRxmBUnqNfF7rm7mmu/UzefSqjUc87ZfIahMdNpoLuCwas2bk+sg2b9aNWwNkue7Mydwem8ZP4Wr2OLMWz7olLPXZI/Nv3DYL0WULv3ecvuM/zA7S1ZYdLaJ3dX+ZdS23s+ifQcIybvUGr3fRFcvWzPtQ7z5ahfD7JUF3RJZ6ts+az4tBxeX7iBmU4f46oYUJv2w6vXczjrdTzcszlRfRey0dQaZ3MOOYvGlDoLcFlsP3OVLjot06hv0N3uudvDg/iv03J6HH0dDiwq1/Gjk7P453Q8/3H6gw5XfrabsEinU5hwRziTjWO5y/w5KU2rbk3wOZu1HkX9Wtel9l0T8iZQAsbcVg+dAvvOxhCzfXG5Jo67mJRJWo6Blrpz2oZ8N3k7NgjIWzosel+FukZ+t+0cHmTzoNN69KkXwZT3+7F/yxDqhwQz3aD9/uTkqkq5UZSZa2Tvee0L9u2N7Wdo7lA/gCR8OKurr21wIBgCOHo5BaNZpaeHpb1qtbR94e7YQAtgFjkPhoeWV2h98z+PxpKWbaSunwvBOi2wo4t2c8nHzZkBrUKJx4+DPpblqPYvLPM5rAHj/Zm/wMwOsOH/AGhWSwsgjxhCtYIlTCRmdeyydqzWYb55s1nX6UgtXzfC/N1RVTh0MVkb17z00TIPe9lwQguGuoYH4n7WchOiTifwCqarJejefvYqdHpC+/tTyiRzZrNqC7obBnpos5QDbrW04PFCYiYENtVmym5+X7HHORlr7QZumZR5/iD45T+089N+1x2JTs2rK+TdkCiB9WaILXuelQw1GpMb1AIVnW3GdWq11F6ru1+hY9iCbm9X2zan2m1Ybe7E2ax8yx/7WLLd+b5LxhYRdAfFbmGt68t86/yBLaAGtECy7Rjwtp+czZrprp0v0x3o6UqwLhVVNZOQXsTQL2NOoUy3t6uT1kVesbSjZ97n2OiuXfdAJcUWdNsmXsvfvdxTK5eAn/aSzcmgqni46G1/mm2TqQ35hti+33BKDcPVWQfxJ7kzczU9dAftxnTrFSVviKerd17QnX+oh7O7NlGcZU3xG1GFg+7g4GAOHcrrqhEQEMDatWs5fvy43XZRcW3r1eB4h2nEqX4sS2lCZnb1zv6ckmXgsW82s+FkPK5OOh4YNAjTk9up9fJuWkz4kWav7eCXNt8SowZwPtOFr3dWzVjJP49qd9zvvCXY1mUmv9tvbc9ucxMUVNSjv1VJHWJSsvjpmw94xfw1OkUlvflIBo36L4Pa1uaJnuHc+uo6VrSZw2U1gKDci2TOuQdjbOl/mCvburV/0F4XiVFxxvOOlwo9f/ftt7HQ1JsveIBsfcUmVTp5JY11cd50NX5Fzqhl4OZr97y/pwveXf9DnOqHe1YM6qGq74lglW0w8f53PzI5ezrOiomM8HvxHb8exXJ3vX09f26fMIdfdXehQ0X99SlIrLzhAVm5JnxjtKAkp1Z7uwAQtPGhdZQrNIn8GvPOimXMrM5fzWDfhl8YqN+BWdHjPGK+3aymfh4uvPPIvczQPQyAbv3kCmU9HRWbkk3js/NxUsxkhPXQZlvNp2WYLzv8tRtn+lMryz03w94oLZjo1LCGbRyulaIo9G7diL/NbbQNR6tm0sp3fj/Ki4avCFJSMQfdgjLk27wJY4AHOoezptlUotUauKZdwGz5Ul8Ztp+5Smed5XdOgaC7fT1/dlmCRdWBL7lF+ePQZW7THaeeEqd91lsMsXv+rltq4h7UkHM53vy6r3JmSS/oYmImqw5HAypPdC/cLTfUz51eTYP5wvlzQv58Eg4uLnyQUhyJTiWERAKUNC1rWzPC9lybun6cVUNIwxOMWXClfHNWnIxNY9e5RB5w2oybKV0bC9qkr+15nU5h4h3h/G7uwv+UZ8l+aLXdjary2nkuEYPJzL3ekdQvsNprh/paF9tduQ20DQ72VrB2yU6ocw88sRnuyXtPt6+nHfOnK7Ux1+9RoYn8rOO1B99aD2Xscnj+BIS2tT0/oLX2u+6blFu1DceWl3lYkzWjGpZjuSFtufbNLAHkP2mWQCr+ZKlJkdNxlqW3PJO0YEfnbLuB07au1i77LyZrk+UdWVrirNtFsY6j79KoBpzdqG20ZA3bWY5/ITGTq0UFc0WITs4ix2jGWa9Q2yUDslMAhRp1tXHSFxMztb8nIxbCnW8WeQyzWSXqar5seW6mVrfDP9OwllanhPQcrU7WruAJkaUOn7BeF1sgX78rTNwDD60A4GpGri0jXBxb4Jwv0x1kCcDj8s914mO5sZLvht2VlMJZcsWyRvYlNZBLSaXfPM3LdOcF+Dqdwh8ukzjhOo7UqAN5heOOwweN4PP2tjHd1m7liqLg6+5MoGLpUZkv6M5x1W5y1dKn4aTXwj8ft3zjtAt0L483at9LnFQDZKegKHkzmKdZJ1MLu5XEOveQihduzno4t5mHEz/mAf0GDPm6l+t0BYJu5yKC7ub3woTdcO/n2o2TU3/BmcoZ6vhvUeGge8GCBQQHB9ttc3FxYfHixWzatKmihxcFPHjP7TzgNod3MgYxa2vVfHFxRLbBxPuz5zIzfhxd3C+w5MnOjO5UD32t5rYyep3CkEFD2XrHUkbnvsr/bYxja2TlZu4A0g6vZrbzRzzoc6DI53s2DWI92hIuGUcqPhlTQblGMx9++wOvGr8EIKfjeLyGzdJ+sVjodQr3DhrOqftWcUyth48picxvB0JqTKXXpzgHLyZzW7w2YYrhlvvBu2ahMp0b1uArz6eYkT2Ivyv49lp1SHttnZqG4d2o6CV0RndtwjxVm6U1e+OH16z3xofLd/HM1bfxVHLIDOuO58jvtC5N+dT296DumJnsMTfF3ZxB0o+V1+V6d1Qityla13K3pncUer51mC/tXS/xDIvI/WdWpZzz03WRNDWfw4wOpePjEHZroTKhfu60H/oyK02d0Ksmsn4ZX+UTmSzeeZ5bFa3nh2evZ4ss07RNV86aa6E3G2zdbMtqT1Qi052+Ygyriswg39smlN8tGXXz4V8qvYv51sgE0o/8QV/9blTFCd3g2eDiUajcK4Nu433dY9qDHbMq7WbPqTORhOsuo6LkzaZr0SrMl8NYer1cOVquiaZWHoqhj84SsN9yb6HXptMpPNi5HgALd15AjT1c6ZNb/rDzPHezk+1er9A89rciy/RrGcKfZst7/9CSMl/no5dTaKmzXJPgW+wmuWod5oeKjr2mcG3Dxd1lfQkA/LxH+8L+qIdlOEWnJwsscqv1hqnl68GPWR1ZeaRss4kXZ2tkAvWUK3xmmIwyo0nexEZoN+UaBXmyX7W8NgdvyJ20THDVJNQPQlrbvfea1fLG3VlPWnbe+s/lkZJp4B/L2s+D21rWVM53QxG0v201PF34M6sZBld/LcA4v9Xhc6TnGLmQmIkOM14pluF9tbQx3I2CtOBkR5IXhLbTsr0lvLfTsg3EWAK1RrmW+Q1qtbS9l9pYZuA+cDEZ6nbWnr+w3eG6mswqeyw9FjrW98sL2Ot3A7RxvNbJ0A5eStbmH1gzyTY+tyhRtuW0PHBKskyU5leHsGAtkEvMyC21O3xcWg7ZBjNOOkXrRp1wClDBowYe/iG2ichOXkkDr2DwqqV1dS6lR8qJosaJA57uroRaAmHrJHAcWaZN3lWgJ19sivZer2nNVidE0vDMArrojhCflpM3XNY6rtuS6TabVVtQnj/TTfIFAC6pQVxMzJfpNmRrwygKzDFUVKab3EyCSMJVMRJj9s/b7uarvX/TYkhO1/YLsGSsQZscrQaFM91Zztoxajrl/X63di9PzTLm616uZboTDU6kqZb6WN4b1qA7I98M5tau5m7OOtskbDWs63SbtHZzKhh0F5Xpzu/qGVg0DFY8XfTz16kKB91hYWH4+voWOXu5n59fRQ8vCnB30fO/gdov+q+3nCUu9dpnu1VV5cMfV/Ni0lsEKal8Gb6bVmF+xZYf1qM9w27T/lC/sOQAKXEXKq0u8Wk5NE7azD36PbQ2F505dnPWk15XC2zcL++A7NRKOz/AV+sOMyF5Oi6KicxG/XHtM7XYrEPPdrdwaeBPnDLXxseQwJXvx1VqXUryy8ad9NNpXQLdbx9fZBmdTqF/K+3Lyl9Hyz+JmKqqrD6k/aHs3zKk2HL+ni5ktx5LiuqBe+q5cgdUZbHhZBwBB2YSpiSQ5VUXjzE/2GUb82vfsCY72rxLtuqMf9wODIccm+W1NNtOx9FFp2XBlIa9Cj3vpNeha9ADk6rglnIGUsq3Pq7V2fh0fjsQzSzTvZy5fxVKz+InJ7krohb/NJ1EouqFe+JxzP/MrNC5S2IwmVm8+yIDcqexves30KBnkeX6tgrlL/OtJKleZKWU/cadyaySdOEow5w20+H0Z3lrgObTONiLc/5dSFe1pXUcmYG4LL5Yd4xXnbSu20rn/2oBSBH8PV1o0Ws4m0yt0KlGjFsqvl77xcRM6qVqr8dcs6Xti5GVm7OeGqENuKwGoKgmy/hPx8WmZHP4UhJ36y2B2C0Diyx3f9vaeLjoeTxxBspXt0Ml9m4xmMz8sjeaEfoNhBgv2b70FtS7eU3W05Ec1QkSTjo0O3J+Ry6n0qKIruWgdfGsE+DOfrMlML1U9qBbVVX+OhZLHeUKdXLPaNn0Ar0GQPsdMfo27SbGgh3ntaxtBWfe3xqZQDfLPBOEtCl0IzIi1JcD1td2eb9DmWJrd+KmtXwKPeek19EqTOsBFXVggxYMHSl7L5PNkfGYzCptgqCuR9GZWye9jrsjamLEiYNeWvDJMceHDZ20dGG+1fsqijFLy9QFaFn/+pZJs5KyTCSN/hOGfltkF2arM5YJyYK8XfFItgw1q9XC9nzbutq++y8ko1qHHp3fVoa6ppGWbcTTRU9zp8vaLODOHnaZ/7Z1tN8BBy4kw4Z3YceXJb5frTN016/hmTcRYo3GeLs527o2X0jM1G5ipcVC2pVCx7AG7mH+7lqm1TqZmGXW86a1vG31B2D8DnjpNAQ3K7ZeKZkGLltuYDSt5a2dP9+Em9bZ1W3jund/C9u/KHTTKC7Nkq22Bs4XduC/+U0e1/9BjtFMqrU7tTXTnar9TU7IyMFoVlGUvMw4YBd022W6sxK1WbuXPW67oZ1rNHPFcv78Y7qtx0hVPYjOyRfQewZrs5GbjRhStXb298wLun3cnalh614eaNueodc+a0G6vBtc1t6hKVkGaNZf+91teY2p2QbbBGnWruee1rW6c4xa0mjnHDzOacMX3Jz04KHdhPEl3S7TrVeAXpOg+0vajZTSgm7rcAezzF5uR2Yvv/buiahFu7p+tDYeJWP2PZB8sfSdKtG36w8zIvJFApR00gJa4jfs81L3ea1fc5oEuvBU5mzcvupY4SDCavPJOHrotfGXHrcUP9Nzy9bttUyZaoToypvE50h0Cn7bplJfd4Us9xA8hs0qlJEo6O5bb2Frxy84YG7EuCtDbeORqlJsSjYhpxbirJhIr9UJQtsUW/au5rXwJhP9iRUYj5evZ8CpK+mMTv6Kta4vc49S8uyjY7pH8JNJCzwzt1VOVrc4KVkGXl56iE+MQ9gR9ADuI+YW6vZe0Lj+PZin18ag5qx63S77U15Rp47gRRYGJ8+8bnQFtG1Sj0OqpYustXtgOX216QxmFXrfEkzj1p0LBV0FPT+oCx/yEADmje85PGlSWa09doW4tBwCvNxp32tIsZ+dRkFerPYfw605s1jvPaDM5zl1JY3mRsvMyWG3gpNLoTKKotC9eR02mNuQq7hasjCVY3dUInEXTuKlZGN2rwHdXyyx/EOd6zPX7UE+Ngzh18AnKnz+7WfzupbrG3Yvsky7ev7lDhb/OZNAa+UsIUoiuHhDgx5FlvN2c+a+NqEcVetrG3Z/W2k9CjaciMMt4yLd9Zag0TI7fUG+7s60Dq9r605P5F8On0NVVY5Gp9BSsQTdRfwebR3mxz7V0mugHEF3ZFw6FxOz6O9kCQrqd7V9kS1oeIc66HUKXtFbMH7cAn5/tszns4pLy+bklTRut9wMpFHPQmVa1PbhtBrKPq/ucPtzYCr5d6Gqqpy6kk4QSXQ/8Y42PrkAaxdz49ktWjBUjlnf/z6h3WyY4L9T63abfxmifHo21Xpl/pJuWU86cp3D779jli7MvXwtwWTN5qDTA1oyJMSSUT13tfReIpGW7H/jYC9oPRIGfaX9axER6oOLXsfVjFyifSyBcuwRS5fu0u2O0pa6alfPHyffULj3C+jxst3N5TbWwP5ict6QHgeC7joBHnk3qmpovy/qWLKzl5KyYN0U+LBpkcvKXbCtZ20JuKwTzlmC7maWoNu6/Bfu/qUOmzhu+f5U289dCyBTLsJ7dWDeAFBVGgZq5zpjzXRbJ3tLtF/WzDYDuTXotmTXE/Ra0GpbTrNpfxjyLXTW5gu4YsmQB3q54qzP9/fLEjBHq4FcTMqX6faqpa1DrZq0ulrOrarg6qQj0Cvf36akKAAuqMHEpeXrHq93Ai9LT0XL32b/fJluX3dnali7l3vl9UQ+W/teuuV8zHf+z9i2+bhrwW1qtgH6vgfDf7Bl89OyjVyl6KA7I8ekrUu/+iXCDnwIaDdvrd8t/JQMDCY1b0y3Xgddn4E7XteG07l4aje/nd3ybt5tnwlfdoZt+W6Myzrd9mT28mtPURReuqcZzzr9QoOMA6SvefuanXvv+UT8Nr1GI10MGa7BeD+81KFxWO4ueqbc15oIXRSu5ixS/3ijUupz/MhewpQEjIpLoS6T+fVqFszzhv/SIXsmcUFdii1XFtqSLsfYY2pMklMQ7kO/LDWAsxrXrxefNfiK48ZQnll8gGzrTJBVZMGOKL4z3M1Sz1F43Vl4LHd+7ev5M8x9Nx/yEVnrp5frfH+fiKOb7jCNlUu4u7qWWLZhkBdn64/gH1NzluvvKtf5HPXx2lPEp+VQO8ifNo9/VWQX64K8XJ3w6/0CF8xBrDG0ITurYjOKJ2fm8tcVL1rnfE3q8F+LzbJbZwsGMFdgVt/0HCOHD+4lTInjyR6OLT8T6OVKnV6Pss8cjs6UTc7JqumB8Ou2wzhjZGTHOrYZT4tza9N6mNCzpRyTy+05n0QHnZZR0tXrXGy5O2+pyVTDGHrwDcaWw8t8nuLM2niGs2ooX7b8Cd2YpaX+nnBz1nN79958ahrCVzviMJsr9rd0+5mr3FbMeG6rtnX92W+2BotluzH5z5mr9LZmuRvfVeK6wiM71uUXU3eyVBdtwqlyBKZF+XnPRR7Qb9QeNOyVtzRPEXo0CWKD2RLMlCHovpKaw9WMXM5SG3PQLXaZQ6s2dfw4aG6kzfKdFAWZiQ4fH7At8zTEw9LboFnRvQZA+5z2aBJEvOqHU3qM9lrKeD6rbacT0GPidifL+6Rh4WEvLUJ9UdHxrPl5LYgr5W9/dHIW6TlG2jhF4Xd8EeycU6iMdXzxmlTLWPGLO8t0I8ZkVtl4Ugu6O+Tu0oIZ62RXBXRpVAMnncJvKY3ICB+ovQbVsWUIrZOotXG2dHWu1dLu+QaW4O5cfIaWac1KKvZYpy1d6RsHe2lBYJuRdpNpujrpaR6qBTt7E10s72XV4c/lLkvQ3bF+gHbDpt2D2k2SfNpaurAfupSCWtsSdJcwn8PF/GtRW4e8WALYMH+tW/ilpCzwq6s9l3Su0DGsme56NSxDT+ItWX7L+tlNampB94nY4rvmF1Soa3nMIchN18YEK4otwLfWn4CG2r8F1hLPG9Nt+b5iCbpTXbRx+tZMODWbQ8uh2r8UPYkaqmoLqAtlunW6vN9NiVobXUrWnq/t524/AbWlDbWgu8ANLks22iVDC7oDPPO+R/i4O/OeYSQrOiyw6yVz1ezNRbUmLh55Qx99bd3LC2eUU7MMbDc353LoPbaMuaeLdqMpMzevO3qOizbbef7u5X6kk2MsMJFafu7+8OZV+N+FvKx26mXtb0JmgqzTXRyZvbx6dG5UgzUhTwLgcWKJdhe0iqVlG/j9h88Zqt+MGR2eI+cVmoGxJF0aB7O2jvaL3+fUL3CpYpM0mcwqrue1SRYyQjqW+AUg2NsNNexW4vG33RWvqD+PXmHXuUT+1HUj66m90Kjwl5Ti6HQKHwxrTaCXCyevpPHDL8sqLftfULbBxKKdF4jHH6++k7UvxSXQ6xTUcG3CFa+EAyWO8yrO8eOHaaSLwazooUG3UssP7NmFUYbXeedMuNbNqQqciE3l5M7V6DDzzn0ttLuyDhrcsREPuc/kxcyx/HioYku+7Th7FVWF0KAa1Gjcqdhy4UFeHHHWhpIYz2wqd0Zw1eEYJrKYra7P0j72Z4f3G9e1AZ+5/5d+Oe/yVXLRY/Ir4nRcGndf+pztrhN42L/02cKty9xsjYxHzSz+C21R9kYl2saNU7f413JrPX+y3WsSk6Vn34XkMp2jOMdjUvn7RBw6BR7pGVFsz4aChneog7erE2fiM9h4Kq7c8x2oqsr2M1cZnPsWJ7rPLDRTvlWbMD9bplu9uMvh95v1+JmqK9keIdC45CVeWtb2JSykFitNluuwd57Dr6U4canZbD4ZywN6yzwy7ceWWL57kyA2WCbNU6O2OTy2/Ei09tn/OeApdON3FHnTrlWYH6l48oTTVO0LZTFZ6uKsO64F3RdaPwcdHtO6fJbg/ra1OaXW4ZTSQOuKWc5JALdEJtBaOYOXmgFufkVm8SNCtZtFFxIztXV9S2HNWHbxtPSUydeF2qqdJdO9JikEVe8C6VeKDNiKc+BiEkmZBmq75eATZ7mB07RPkWW93Zy5tb4/WbixpME7WjCqc+zvgDW4a2iyBJwFgu76lqCbyL9gWi1YNKLYY52+ogXd4cFexZaxjuvefyE530zepc8Yr6oqu89pQXeHBsW/9xrX9MJJp5CSZSDexzIZYOyhYj/3F/IH3e0fhl6v236XhNky3Zm2LvfWgDK/8wUz3fHWTLc2A7ot0x2bpt1ozE7V2vHT1sWubnKi4GzosZaeLpbrYx0nbq1/XqY7b66MzFwjaZbu48HW4DlVC7qzPLThcfEFg14La9BdM3/QnREPxmxUFGLUGvZjugH8LW1keZ8XOZ4b7DLd8dag38pbq5drtvb7ws8u0+1EPP6cdmmmLb1lkWoZc++db8Jha/fyrKxM7UZRvuuflm1khnE4p3p8YbvWdt3LLdnvLGc/wD7T7a7kohoybUG3p5Ktdem33uwoKj60rdPtlq97uWS67VTn7OUzZ86kfv36uLm50alTJ3btKt+sq9erIffex0rTbehQSf+j6O5UlenTJet4Plfr/mvo+nyJmeXijB58H8tMWhCWuuatCtXnwMVkOhq1cYpeEUX/kc3vzmZaN5v1lRB0G0xm3lut/cF4vHtDQms4luHOL9DLlfeHtGKwbjNjjz9Bys9VszbyioOXtS8lfu62pahKc2vLCI6a66Ggwul1ZTpfZq4R32htAqDcWu0dyv53aVSDpjW9ycw1sWRP5Q+XUFWVBUt+YaHTVNb7TqVrvbLNkuvipOOxXloXuK+3nMNYzJqZjth2Wrs7bF2PtTg6nYJTvdvIVfW4ZMSUe0KtNbuO0Vun3eBSyvCZdXPWM7hfX06odZmz+UzRS5ZUwC9bjzBQv51AJZWAWvVLLd+pQQ26OJ1kafZjZC8YVqZznTt3hnq6OFRFB2Ediy3npNdxh+X3xLrjVypl/oevNp6ml24//VsE530xd4C3mzMjO9Wlq+4wdZbdV2R3TUdEXc0kNjWbDL0f9buNtJvgMb86Ae5cdm/C47nPcWzg7w7Phn0hMZPo5Cy+5n7MzxyGVg+UWF5RFIbfGsZik3aTUj2yzOFus8VZuu8S3dhPLSVJGy/YtF+J5RsGepLr04Aoc00Us8HhmaGPWpZ5iqhdeHyyVYvaPugUWJvegCu5hYcxlCQuLVubPAtocftA6P9h3sRNxbireU28XZ34KddyM+Vg2cfJq6rK1siEvK7lDboXGYz6ejhbAiyVyNPH4ULJQaA1IGrtYskO55vp3SrA04X6NTzIwYXUAEsge6HkIUn5rT+u/T1/LOQsitmoLVtlzWYWwdrFfNMpxyefM5tV22sxdngSevwP6t1uV8bajflUpqfW7b6EuQIiLTOXR7gna9n/IjLM1nHdBy8la0G3onNozP6FxEzi0nJw1iu08UmHHV9pkyMW4OqktwX9h3NqaZnF7BRbhrbQcS0Bc90aHtC4N/R4yRbYhuXvXm4LKKPsxlZDXqa7fg0PrUuxs4fWjThIy3TXD/TERa8jI9ekzebt6q19NpOi8sZ/F3C8YKbbGnSHtMqrb776294biWds37eupGp/2zxc9HhbgkprptvorX3+bEG3qsKJP2DX15CbYZu53JYhB+013fEGOR3+iwEnEtJz7HszFrgxUdQa3bY2BC6qwYWDfkum2ydXe08EeOYPuovOXmelp/Cy04+MSfjY9tqtE6k1ydgH79eHr/OSR9Yg3VoGCkyklql9l8l00gJtN2c9uPpgRvvdoc9OwWw5zy3Kee3YC0tYLtKQL+iWTHfRfvjhB1um2+pazF7+008/8fzzzzN58mT27dtH69atueeee4iLq5plqf6NWoX5savBeAyqHq+LG+Fs1bX38gPR/HQ0nU3mNqQF34rrHZPKdZx6NTw52ey/GFUdPpc2VWhJom3HLtBJpwW++lKytwB3NAuml24/j5x5BsPG8nWbtlq+P5r/pU7jMfdNPHF7+dcUvPOWmoRE3I4ZHb7RmzAc+LFC9SpIVVUObvqV+c7vMqlpDHqdY1+kuzSqwUZLJij7+J9lOufOs4l0UbTMpWvT3g7toygK47rWJ5AU1E3TMZfji2NJfj94iZHxn6BTVIIbttLWgyyjoe3DCPB0oUbKES78/HK5b5Ckn9zASpdXGW0oPSPVplEo+9XG5ChuZZ7wCSAqIYM60X/gopgwBLcslJ0pzYCWIbSs7UtGronFq/4u8stbeWTkGOHgYtwUA+l+zSCsQ6n7uLvoCazdkBAlEdfYfQ4HxLEp2dRO096P5uAIcCs+YAJt6cGmygWG7xkO31ZsuMOFq5mkHVnFXJfpfJD0XJnfMw/eVo+aJNE49zjG3d+VK9u93TKrc5u6fiX27lAUhWZ1gvnL3IHdV0seEpKf9SZS2zr+eLg6O5Q5HNS2Nkf0TTllrq1NSnW4/BMUqqrKkj2XGKHfqG1oPbLQBGAFKYpC96ZBvGB4ks8ifi41O2915HIK/qTSolbxN088XJxs3WQPWgJoR204EYeqaqsX2GXOSuDmrKdfyxBWmLpgRqet+VzGG3SRcenEpeXQ3ckSsDQqPLmjVfMQHyKUKG5d1g0WjyjxPX3KEqjWN1mynjWL/v1jXSLrlIslKC/DTN3Wnmu99ZaJD4vJcltZe8zsPHeV3NgTWlBqyCpxnwuJmWTmmnBx0lGzXX9tQqigJnZlrJOp7U61zJeRmVBkF/Nsg4mLlu7GjbMPwuqX4O93CpVrbZmc9ujlVHKbD9V6TQz8pMR6AuyyZLlbhfnhdn4DrHkFVr1cZFlroHosLts2rtoWtOaTkmmwTSRWx7/wigt23ct9w7TJ/0w5kJ43F4iqqvaZbr0T/Hc7vBZr67rsrNfZZlU/GZum3fiz/s2KLZzEM5lV2+z41iy5rZxlP2t9U7ONWu8M602B7BTb9ck/nltRFEv3cC3o1lsyxbagV1Hgt//Cqhch+YJtfW+77uUeAdD9RVz7TbMF8XZdzPPfmCBfprtg0N2gB8kN+nPMXK9w9/JarVAb9uJUrtZ21iXDAHz+v727Do/qzB44/r13LO4ukCDB3aVACy3UabelQoUupdS9W9vK7nZLf7u17UrdXakbpYIUlxb3IAmEuCeTmbm/P+6dmUwySSYhQc/nefJAkpGbyU1yz3vOe47NzF3m9xi4722fc7u81sH15s8ZXviZp4GZe2RYUJ2xLaVevxd39j/CpnrGtoXa9N/vFbVOT9BdYTRoC7KYQFH4osccLrXfR5kShsPIdIdhHEf9Rd/PboTXz4F8o3+KJ9Nt8267k0ZquhUrVjBx4kROP/10rrvuOv7617/y+eefs2ePt2PomDGtz4QG6sknn2TWrFlcddVV9O7dm+eee46QkBBeeaVxo47j2RVnnczbTj2wqfzq/kYri+1hX3EVf/50PeWEsO2kfxE+8zNv6UcbXDx5PJ+59HOj7Ps5bX6cdVu38bvWhYqQNE95UnP6pETQNbiSUcp6qn8PvGtpQ06Xxpr57zLFtII/qW8SxqF1kJ913mReMhp1Ob66GyraZ/wLwOo9xUws+ZhxpnVMMgXekTg61MreGD1zouz8qVXn1cIt+xmj6sGZ0i2woBtg6sBULghewSzHO1T98Fi7ncuVtQ42fPEf+qrZ1JrCCD2zbbOPgywmrhwczXvWR+iy5cVWdZN1O1BaQ7fy5fRVs8nUWs7oj8iM5Rb7DYzUXsHZLbDAoL6PV+/jQqPk1jKk9Y0tVVXh3tN7co66mOs2XErNpze3SzXGZ2tyuEDT99KGjJ4VcFa1R8++ZLsSUTVnwNnJlbuLSFUKcGDC1Mx+brdxWfHkq3GkO/fp2RX3BUEbPL9gB7NNXwAQnDWh1bOU02NCKOlyFiVaKObynDbNLF2yo4D/WJ7hVvMn+j7HZgwwSlp/2xd45vnXHQUkU8iYrlEB3ycqxMqUPsm8Z2S72fRFwPdtaNXuYnYVVPKWciaO3n+Awc2XlruN7RbPKq0HX+cEB/x92Zhbxj8sLzDjlzGNRv7U1y81kjCqiF70V3jzvIB/l83beJAwqnjY9rZ+fgf4s3buoBTyiWYJRoDye+DbSEAvLQf4MPkumPIYdG+6KWmv5Ai2a6l6gF9dBOVNj73ckleBDTvR1cZ1oZ/ycvBmdRfajZ4CAWa6c0qq2XygHIviJLXA+H1cb5653+NPiiA21EqV3Yn2xlQ9KG0hyHc3O81KDPPMN24o0wgWNxW60IzS34b7hgF25FegaXqQ5Bk9ltC70e06x4YQFWLB7nCxuVhrskKlIXcTteGZMZBtvCZNVDi5S7I3HSjzBrfu5mb1uEuz48NtBNfk6fOT65WP+5SXmywQZSQi6t2msNJORa0DRdGrajxMFp+fP3fw7A6m3Rlrf4sB2YWV1NS5CLKoeiBfVeTN1Cfq51qw1eTpKr6nqEofZRhudCA3vj/uwDkhwlisqyr0BIDWWP1r8TuruyzXf3m5QVEUT8m4TzO1pjLdDcvLR11P1bkvs0brTn55rW9vj8GXU3rBB7xnNKGNCvZmuuMsNdxg/pypB/8LeF/bwloLNZoRzBozud1Z7FCHsUBkLIBomkZ5TR2nqSvo+lwmvK1Xl4Va9Wv/qnqZ7gqTvngTZPRl2ZNwCr+6+lKtWTzl5eGqcZ1sq7fovWcp7PrFU6buU15ui4DT/6lX+xxH/cHaHHRffvnlmEwmrrnmGjIzM/nll1+46qqryMjIIDY2tj2PsRG73c6qVauYNMl7Qa+qKpMmTWLJksa/PGtraykrK/N5O150Swhnd98bKNeCCS1ch7b123Z9fKdL42/v/Eh5TR2DOkVx88TueufBQ5AZF8qWbrNwaQqWvb+2ac9wQUUt8/YHM83+EFXXLAnooklRFJQsPXCJKFrnd6RFIL5Zl8uFlXpGWht2dav37DUUFWIl45x72ejqTLCjlPJPbz+kx6vvm58WcIppLS4UgsZc16r7xvQYS4UWhM1e7HeVuSn5W34lQqnCbo1stkt6Q8FWE9bB0ynXggkr3wk7fmzV8TblpXmrme14CwB14v0QFt/CPZo2bWwfPnXpZYVlP7fctb+hxdsLGGOUcFq7t9wDoFdyOBW2RIpraHWXe6dL47cVC+mrZuNULdCvdSXZbqO7xUHGWOowE7R/JWz5pk2P46ZpGr8t/Jyu6n7sphDUFsqR6xvTLY5f3c3ldgYWgK7MLuZF51n834DvYXzTo9LcIoIs9OnSicUuI0DY1LYFuoPlNWxd9RMj1M24VAuMvL5Nj3PBiG7MdernnKuV+581TWPPjo2cZVrKyJxXwdR8ufPA9CiSKaT/judgfssNOt37ud+2/p0bVkxuVeXSRcPS+cQ5ltu5neoL217h8+FKPSMV328S5mmvNMpANmVkF/339uYD5QFtnSiqtJNTUk0/dReqs9Znr2RD/dOjqMFK/wMf6b/HCre1+PjVdieLtudzsrqWQTnv6PNpA1wMGJkZS1JEEB+5S8zXtW7++KJt+kVv196DYeR1zZa090qOoBYr+1TjNk1UvzicLnYcrKCbkoOiufSyf3fH5Qbc46s+yTces6YsoFnxPxlZ7ouT9qPWlOhZuhaqZlRVMbb2KGwLM/bkt/C3xt25fErEHtjyrd+F8fToEEyqQnWdE3ukEVT5qU7aftDbRE1xB7hGI7H6FEXxZLt9KiZa+L66M93DO0d7F4ab6OPgznRv2l8OE+6G2zbASXc0up3Pfu6dP+vzk7+81fN5d7BYXuPQe7I02LMMsNsoLU+JDMZmbroaxj1aztNMzZiFzv7G1yCb93vH0ZlUBfKM7RFRnX1GtjXa1z39A7h9s6cnQ6NmaLYIuPpHuPgd4iL1RYD8JoJuT6Y7sl7QfXCT/lZXrXd7B/YV1ct0Jw+Es5+BMx8HmikvR9+GCOBwaRRX+e5rLzb6KoTZzD6NSOOMcWFVSrBPU8uy2vrdyPWAOdzIdEdjvN7GjO5KuxOXBmWE6mMkG3Yvtzs8TRvL1CgATyWV+1jsDm/38nB3pttaL35oODbMPRnGEqwvjoy4Bob+sdWL1UezNgfde/fu5dlnn+XGG2/kiSeeYP78+RQWFrJr164OzzYXFBTgdDoblbUnJiZy4EDj0TZz5swhMjLS85ae3vZy4KPR1VOG8w/XdG6xX89CZUi7PvarP6zi4YM38ZLtaZ45N6PJVd7WOmfSBK6ru5VRNf8it651+2sBFhj7sfqmRpAQ1Xy5aH3D+/XmN5e+p0drRddaN03T+PX7Dxmo7qBOtWEbe3OrH8OfMwZ04qO0e3BoKuHbP8fVxlFd9eWV1dBtx+sAVHQ+1dtAJEBjeiSxxAhwtAA7DOeUVLOlWOED5wS0fhcF3KTG7eKT+vCha4J+zL8c+nziXQWVRC17ghilgoqIblhGzDqkx0uODGZHF30UUVj291C8u1X3X711F/3d44a6+B+rVJ/ZpDI0Q78gXb6rqFUX0kt2FDKh2ug6nnX6IS0OzT5rDK869bLNmu8eanNTL9CrL8aXfQ6gnyMtlHvX1zclgtVm/SKsdltg22lW7jbKLbuk+Mwsbc5pfZL4xmXs/d74ecDHV98ri7K5WtEDdqX/NO+FWitN6pXIN1Yj87jlm1bNYt6RX0HvGqPsNnWofiHTjAFpUUQolVxlfxdt2fMtfp+35lUQVJlDF/UAproKiO0e8LGN6hJLeEwCn9QM5ZtNhQHfr74qu4Ov1umZ1guHNB0E+xMbZqNXcgRnq79if2e6Pj6qGRtyS4mnRN83rqjNbtUYkBaJAzMbNCP4CKDr9OLtBdTUuZgaZFQk9Qp8NJ6qKpwzUJ9l/2Pk+foIqgDZHS6WGYFaS30mQC8vB/jdYVxH+clAgt5LwO50kWUxAtSE3k1ePPdMDifIorKvJojsy5fDHZsDmoriLi3P6DUYznpanwEcQBXeWOPr/K7GyDC3UEHi3jd8etXn8O5FsOaNRrexmlVPxrckWJ+f3lzQ3S0hXO/UDJDQeK87eCtP1uwt0RcGXpqklzY34WB5DdmFVSgKDIkq12dJq+Ym+1i4g+7swkqqQtOM0vDG36M9/jqXuwNr9C0Vscae4pzian3W86gbvSXrQHaBMec7zvgd9OVt8MIE2Py1z3N5Mt3uReb6me4GFSPuhehe7tJykw2ypjRqatso6E7qBxHJnq/VHTgnugNnsxXShkDPMz1Zcr9Bd/n+xqPGAL5/AP43EtZ96Lvf3S0sXm/22Hk0LpfG/hJjRnf9THdNGZTlYlW9+7UblpgXV9mxYfcpLQeIRn9dipUon4+X19RRpBmvlZHptplNBFlUT6Du/hvp3g9eqhg9eRrN6XbClDlw0dvsCtbP3yCLHh+klf/GJab5xFVu8wTdIf7Kyz1BtzHOLSRGnzxgC/ya4FjT5ghqzJgx7Nu3r9HHO3fuzLnnnntIB9Xe7r33XkpLSz1ve/ce3rnWHS01KhjL8Jl85hrLP77fesjjZdx+21NMxqI/kawUMSIsn/T4qHZ5XIC+qZGUZUyhxBXC60uyW33/JRuziaSCCVkJLd+4njHdYlmAPualcl3rA9v5G/OYWq6PxHMOvuqQsqb1KYrCrIvO43X0C62aube0WArako8XrOE8VW9oFnHKbS3curEhnaN5SruEMTX/YkdG051Y61u4NZ+tWjrvJf8J29mt3zefEhVMdtfLcWoKYfsWQN7GVj+Gm6ZpvPTxl0xX9cWV0HMfb3I8V2tMnjCBhc6+qLioWfpSq47HseMXVEWjKrJbwEHY8MwYrjB9x5Sfz4EVgT/fhyv3MFHVAy7TYP8ziwPVJyWSvb2voUQLJah4K9pvbc9MfrTgN05T9SDENqp1iyBmk4qrk14qGVy0ybNa35TKWoeexQHP4kUgJvdO5AfXEByaqld5+OnE25yymjp+Xforp7ob2I25pYV7NM1qVhk0bLQ+vk1zwNp3Ar7vkh2Fnq0eqp+5yw1Fh1qxR3enUrOh2CugoPkM7eLtBYw2GVtJUoe0agFFVRWmDdEDt/dX7NUvqFvZNOe7DQfoY1/HP8LeZXhY67fljOkayzB1Cym582B782PxNuSW0Uc1zoO4rGaDwp5J+pzllQ5joTOn5aD7h0152LAzlpZHhflz7sAUKgnm2sJplMUPDDg7tGZPMVV2J48Ev0Ov/Z+22Mk9LTqYcJuZDU5jNJQ7u9iAexb1joRT9f3I5/6nyce0mFT6p0YBsLw4JKBjr7Y7WbxdDxzG9O8BQ6+CUTe0eD+Asca+7nfzM71fQzOVb+6gO7nGCKLd2dcG3Pu6c01Gxt5P0L3N6FzeO9rpLc1P6NnoduAd6/Xb3hJ9n/S+FdDM+MgVu/QS4Z5JEUQcMJrcpQxucrEtLsxGfLhN7w3WzJgunxnd7qC7QbM6nxLzYTNh8t99uvvv9owLM35uctfobw26U/cwAuid+ZXYHS79Z81kA3t5o6727u+LZz93pxFw6fuN9r6nNwy6G/C7L9vgDroP1u8ebpSnO4r3efa6J9bPdBszuolM9+wp31vs/7nzK2qxO12YVMX3+Td/BU/2grf/QIK/wL+uhn5vDWRL0AxSgnxfwyiXfh4Uar6/j8uqHRS5P1bl/dsZEWQhBt+g272fu8ZmLMTVlIDDTpixp7uy1qFXMvY6iwJFX9B3Z7p75X7MHMvLZFUs9zRSC28u6K4zXpvzX4DbN+oLjpoG2Yv0BbHjqJlaq4Lu888/n4cffpi5c+dy7bXX8re//Y3i4taNbmkPcXFxmEwm8vJ8f0nm5eWRlNR4hJXNZiMiIsLn7Xhzw8ldCbWaWJ9Txhcrt3maHrRVZa2DX976O5PUVdQpFsIueyOglefWmDlW/4P3zrLdVO0PfN+kw+kicvtcVttmc0XxM616zhCrmcJkfQ+Mdc+CJsdQ+KNpGj9/P5fh6hYcipWgcbe26rlbkhwZTPBpf2adK4Mnas5iT2Xb982X19TBypewKXWURPeHTi3vZW0oyGIiJqM/OcR79vy1ZKFxETS2e9sXI86ZMIrvXfof65pFTV+otWTexjxWZhexXutCZZczUJppDtQawzKimR9hLCyuer3FJjxu2w9W0M/IOgZSWu42IjOGcKpJrtuDFuC87rKaOr7dkMcU+2PsHv80dJ0Y8PM15fopQ3jeNRWA2nmPeDuNtsKB0ho+3FTNGfY5HBhxv99uxi3p16M7W1xGVjN7YbO3Xbu3hOuVj/k6+EGSd38Z8HMkRATRpVNnlrmMss9W7jl+c8luLnV+hqpoaD3OCKjnRHMuHtbJ0+3bsfK1gCseFm87yCgj6Caz5coKgH7psax3Z2hbKBf/dUcho1qY/92cC4amoSqQsedj6p7q36oFBdBLy68wf8c0xxcoK15s9fOP7hbLUvf3OHtRs7ddn1NKP3eVSvKAZm9rNav0Sg5nrcsIulvIdLtcGj9sOsgYdT02V5V+Ue9nBnhzeidH0D0hDLvDxbfrGlf8NWXR9gKSKOQy7UvUL29tcUSPqir0TA5nk2ZkupsoL9+a5y6jDtcnWDQzNx28+7rXuEf1tXCOL9lZQK3DRUpkED0SA9vv7JYSFUyX+FAKtAhKo4zfQTt/9nvbspo69hVXE0QtwWX+x4W5uWd1r6cr9DoHOjUu6952UA9u+1uMEaFRnZrcr90/Tc8y7sivpCyuvx54l+3zNPlqyLOfO6NeaXkLEyu8JeZlsOx5ePfSRuer74xu42cgJtPnNj7N1PzINpqoZcSG6N/bAmNBIs63OiY5MojwIDMOl8aO/Ap9oTxtmP5m972udS+oejqXN8Gd6fY0MyvZA/MegnkPArC/YbZ6yzew9Fk4sJ6EcP1jxVV1+iIAeBbM7cX699Cn67mmeYPuqE7+M92g/9ysfpOibcs9z+1TSWo0WSMyrV7gXy/otgShGZVImbYSn4cOc+jvH3T5vi7ltXUUYZxr9bZ1RgRbiHFnuo3ycnfncoIi9fMOoKrAW15e6/094e7M7g66HVb9vLU5yjyN1EIxXnu/me4mtpK8dia8OfWQp1scTVoVdHft2pXFixcze/ZsLrjgAn788UeysrK4+uqreemll1i1ahV2e+BBTFtZrVaGDBnC/PnzPR9zuVzMnz+fUaNaH1wcD2LDbFx/cjcmq8sZ/fVp1Px0aN25X/zgM2bXvgqA45S/oLRwkdEWp/RMoG+sxpuu+7C+ODbgssnf9pUwwrkak6IRl9L0aJCmdBkwhnwtEquzEvYE1owJ9C69U4reBKBuwHS9PKmdXTwqi0dT/8fL9lO5+5P1ba5a+GDxRi7W9P39Eafc1uY9MWM9s5FbDrqdLo3929bQX9nBuK6BZxUbGtI5mp+i9X2+5vUfQlnTjXqaUlPn5K9fbmSL1onvR71J6LTn23w8DSmKQtZJF7BPiyPIUYqrmYZK9S3als94Vd+XZm5F0N0vNYo1Jn1/sXPnwoCaMn35235qHS7SE+PoNGHGITU+dEuPCcE59GpytRiCqnJxtSLr7vbm0mwcLo2ojAEkne6/o25LxnSL4yPnOF51nYE9qvmf/5XZxYxSN9Jb297ogq0lU/om8a3L2B+6KfAS85o6J68v2k6mqgc+yphbW/W8/mTGhZKffibznYP4NnEWaC2fA06XRsHONcQq5TgtoX5nSvszMD3KGyw2E3Q7nC6W7SxgtCeob33QnRwZzLiseCKpwFK+F1a/HvB99xZVsW3Hdk4zqgkYclWrn394ZiwrMUqM89Z79ij6szG3jH7uTHfywBYfu39aFGtcRkCRtwHs/jNdoP9NK6io5SyL8bX0PBPU1hUiKorC1EF6hnXDsnn6nnB3I61m/LI1n/EmY79s6hCf7sVN6ZUcwSaXUUJdsM3vApw7uMxKDKwHjLuD+cbd++G96fBk72ZfM/eosOtTtqAsf7HJQLQpJxkl5r9ZjcWNJvZ1bzEywGPCD+p700Pjm9yb7g66F9iz4KI39T2p9dgdLk8H7wyXsTUpvvF+brfYMJun6djveU5vI7om5nW7twmM6BLrvU2D0WYNuZupbcwt0yfgbPlKz6jX47e8vMlMd7UeeFYc9AnefTLdFXl65lpRGz2Ooij1SsyN7PuML+HqH3wWu8pq6jx7oXsmRejZ0CaqFRqVl9dWwOKnYdVroGmesV+ebPXv78O398CuBUQFWzAbU188vR+MoFsr04NuT9dzMJqwVQMKRKZ5sux7G2bZV7wEn9+Iaau+GNzUuDCiM/2XuAOVNj250cnsG5SGGJ3IDzrDvQsFNMx01wu6g8z84hrAgdTJnsWUciPoDgu2ebdlVeZ7Gqk5a8v1cXfrP6amTn8Od9DttEUBEOwo81zDrrUO1rd/1P87YWlQXl6foniD/RM10/3Pf/6TefPmcfDgQfbu3csXX3zBrbfeSmlpKf/3f//H8OHDCQ8Pp39//6U37en222/nxRdf5PXXX2fTpk1cd911VFZWctVVrf/De7yYdVIXEiJCiKcY07L/eVcSW2nu4t85b9s92BQHRWkTCR7btgZALVFVhYvG9sWFgtlVi2tpYPvQFmzM8VzoqQGMCmvo5J5JfOccygJXfyrqAv8R+M9P2/iX4w9sjRhF8IT2a3ZWn6oqPHbBQIItJpbsLOTDJZtbvcpXbXfy/K8H+EvdFexPGIfa+5w2H89J3eMYpW7g8l1/wvn9Q83edkNuKRfWfcHntgcYtL31TcbcFEVh5PjTWerqxdfKWOzO1i88PPvTdvYVV5McGcSNE7NaVfYaiHMHdeIDJpOjxbIlP7BM9/JtuazQelBuS4KMkwJ+LqtZJSRjGJWaDXNtsXcfYDM+XpkNaFwwJM17MdAOrp3Ul+eYRpkWzLr9TV8Q+1Ntd/LhUr1b7FWjM9p8DN0Twpgb/Af+Yr+MVTXNzzBek32QQarxe7CV1R6T+yTxnXMYnzrHUD70xoDv9+HKvRysdHJ78KM4Zv6olzy2g/NHZTGz7i4e2dUTh9by93RdTikD6/RRaWrnMQFvrRiQHsXvRtCtNRN0b8gtI96+lySlGM1khfSm55835+Jh6XzsHEcdZj3IP+C/XLmht5ft4WLTT1gUpz7HuInO2M0Js5lJT+/MVpdxHjVROVFR62BnQSV93UF3AA0i+6dFsp8YitQY0Jyw/7cmb/vDpjxMODnVbOy/b8V+7vrOGaAHA93zvtQXMNa+3eztCypq+X1fKRPUtfoHugX297RXcgR5RPNZxCVw3nNA49/R2/IqiKCSCzfeAN/c0+JioTvTve6gHde+lVCeC7mr/d5W0zRPE7Uzqr7Qx25tbF3DQ/f+9U/LjCqUJrrFu0uYx0UYi79J/ZtcxHYH3bsK/GfudhdW4nBphNnMRAy/DP74PYxvfvFxoNFkTp/XPVL/oJ/56KVVdZ49zsMyYmD2Qrj8U2hhYkPv+pludwa/XtMyh9PlCW47h9TqZcbQqHLBp7zcXgGPd4eXJnpGO3oz3aHesvuoTn7H+7lLzD0l735eb3cTtdSoYCJDLPqe7yey4L8jG93WHXTnFFfjcLq8x15Tiquy0JNB9mS63Qs4kWmoqtI46E0eCBe8wop++jWRT+fyEmMxJTwZzDbP61JcVUdFveywe7FBMSoHGnUud5fSR2f4L3EHSsx60J2i+i4WWmv19wuI8GSsXS69G/lzjrPIn7lcnzVviAi28JTjQhYOesLT1K+s2hgXFmyGUGMbZ2W+J9MdUnNQ/7n78jaqjUx3sDvoDorSb+P0Zrp/CxoGp/wZutWrurOG6gsv7qD6rQv0vgXuiSGqkSxoofrmWNLmPd2pqamceeaZ3H///Xz44Yds27aN0tJS5s+fz+zZs9vzGP266KKLePzxx3nwwQcZOHAga9eu5dtvv23UXO1EYjWrnP6HP7LI2QeLZqfivT+2eoVo7d4Sor67mc7qQcqCUoi55MUO7Rz4hyFpvGE6HwDnshcCmr2bv+EnQpVaqm1xrZ47DHrG7o2Ym7nCfg/fVwaWKV+1u4ilO4tYq/YifOZc/Y9FB+kcG8pdk3swWNnK6O/PofKTW1rVQOvlRTs5WOlgZcSpxM0+tPFuvZIiSA2yM0FZjX198xc1C7fmM87ImJjakPWq78wBKdxm/Qs3V13N2xtb7ixc39a8chIW3s895nd5cEoXQqyHnuVtKNRmpmrQ1YyrfZonDgxu8fZ1ThcLsyu5ve56dl++rNUTAEZ2T2Kly7g4bKHEfPvBCgbnvsv31ru5KDiwBniBigm1kjT+KsbVPs0N24dS6wi8odo7S3bwrvMOngt9gVM7t70ho6IojO6qT8j4dUfTFRhOl0bN3rUEK3Yctih9b2ArpMeEkJDamVvrbuDz2sBKfeucLp5foGeCZk/oijm9/RpbTu6TSHSIhQNlNfy8peX9y4u25RNMLdVKCEoA+7nd+qREsJ5u+jt5G5rcRrB4R4GntFxJH6F3nG2DU3omQmg83zuNn6MAst01dU4+XL6LS8xGdnLozDY9N8DorrGejvhN/WytzylFxcUX5inQ44yA/u7oTbAUVju7ooXEecfi+PHDxoOkKgWYLTYIioLObRu3mh4TwtDO0XzmMO6/8fNmt78s3JaPBQfjTcZCR4CL2Ho5r8JfK/+A1u/CRt/7OqeLnQUVZCl7iclbom/PaCFznxgRRGpUMC5NoSjWOBeaGOW1cX8ZuaU1xFlqiM7Xy3PJan4+d0Mju8ZiUhW+LOlEwblvwQ1L/V7ruIPu/majF1Az33t30L2nsAqn0wVluT6zurcZTdS6JoShBEXoC3ItVKAMMErM1+4t8S5s+cl0r8guQtOgS3yoHqRZQ/R56y1sC+yToj/+5gPlOP3MxN5fWoPTpWE1q8Tbc/UPhiU1elyf8nJbuKdMmeJdlFTZ9a7mGAGwu1dEE40XvR3MG1wP1juX3Z/z7Od2L2qFN44BEsJtWM0qDpeml5JbQ/SGXUBJzmYcLg1F8e7fptQo/TcmFDQKusPioe8f2GzS/6Yk+dvPbVwjhgdZPI3OfGZ1x+gLmyEV2YCfTHe9igJ3iXvDTHeRqv8dTMA36FYn3M0lPMoHzgme173S7sClQT7RhCV289nnHxGkH5/7tuDNdIfbLHrT195TISjaM6fbUlui3zAktl55uf4z7jIy3SHOck8jNZPqJ46Y/Cg8WAQTjAWA/Wv1Kgv3bG530K21vXHr0aZ9WlEbwsLCGDt2LDfcEFgzi0N14403snv3bmpra1m2bBkjRrRPRuFYNrp7PAt6/YVSLYSwgt+onvdIwPfdU1jFNW+s5Om68zhgSSPsyg8htGPHv4VYzaSNOJ9trlQsdeVoK5vvfL+roJK+JXqnUbXH6W1eEDi9n54R+GxtbkC3/8+P+srsHwankRzZtovL1pgxOoNeyREkU0Do1rnULXs5oPsdLK/hlZ/1C+E/TemB5RC7zauqgqXbBByaSnDZrma7de/atJpUpRCnam1yREmgbGYTN52qr7g+M3+bd39RCxxOF2+9/RrTTfO41vwFU2LbNhYuEJeMycKJiR835/n+MfVj2c4iKmodxIVZ6W1c5LTG6K5xLHHpZbCuFoLuj1bu5ULTL2Sp+4hUAsvCt8aMsd2whsexr7iat5fuCeg+1XYnOb+8Sld1PxNMv2EOat0ezIbGdIvFhp3yjT802T1584Ey+jj0ihhT55GtLtcFOHeAfmH2wcrASlc/Wb2PlJLVZIbWMW1o+07JsJlNXDAkjThKKf3+Mb2fQDMWbC3g387z+XjSwlaVXgdZTEQkdaFAi8CpmBs1L3JbsqOQFa4erO1yDQyc3qqvpT6rWeX8wanemd2/vd9sWTHA57/lMrx2CSlKEVpwDPRue/PW0V29Y+i0nf474q/bV4oLlTUZV8Ml7wY0M7lrfBghVhM3117H1svXQBMVR3sKq9iSV06OkoT9pvUw+5dDavh47qBUVmpZHFTj9RLerd81edtftuQzRN2qdxYOjQ+obB6gR2I4qqLPXm4YCICe0a1zavSzGH9fExvPofbHne3eaDZu38S87h826lnumcnZKC6HHry1cjpHRJCFgelR2LHwQ92AJoNT97iwjDp3E7Wmg+6UqGCsJhW700XtO5fpjbDqbT+qPy4sUAONZmpr95agpRvXuAfWNerbs9zYzz0is3VTKjLjQgmyqFTZney1GUHwwU2exTZ3OXx6dDBqTAac/6KesWzAJ9MNPrOo3VnuxAgbwVaTN9Md5z/oblRe7nTA/0bBoymeEvINOUbncvd+7v1r9X/9nMOqqniy3e6vx51prszVs6pxYTb9eslh9za4i9R/hyf421MN3s7lfoNu7+9/TzO1ovqzuvXnj67ZB2ie1w/Qmxm6F+liMv3v6QbyFP36PNbVoKFoWAJ7g3uxT0vwdCF3N3yzmlRPcOwWHaQQSQVl1d7twe7bRwSb9aZ4016HtCGEGZnu4DpjMale0O3OdGvB+jkY6ir3NFLr5NyrV9/WXwQ0mX2v4d0jw8zG6+mefnMI01KONq26Cvn9999xBbCf0G3Dhg04HMdPWcCx4tYLTuaZYH0mc/DSp6lb3XyJGejdGy97eRkHy2upSRhE6G2rUJNbX67XFjPGduElTb8gqVv8n2YbNH3z+14mm/TsnW3A+W1+Tvfet+3bt1CyvvnRYetzSjlz59+43/w21w9rfcDUFqqqcMMVl/Jv9VIAlO/ugX0tz8B98/33+V65kVvjV3lKDQ/VsJ4ZrNGMzFcT+96q7A5iD+gXrLWpo1ocTRSIaUPT6BofSlj1Pg7+78wWM7wAL327lJtK/6Ef04CrUDo1LjVrL13jwxjbLQ5Vc7Dq61ea7Unw0+87GaBs59ReCaj+Vnxb0DMpnPW2gQC4di1q8o9QndPFtlU/kqXm4DQFQZ/zWv1cLQmxmrntVH2Ff8X8j6h9f2aLpaNv/rKea516kyzzuNsP+fwY3TWOO8wf8nDJfdQt8b9ff9nOIkaqmwFQ2tBIEOC8wamYVYWifVso+vgOb/bBjzqni9d/XMOL1if4jusIKtrcpudszsXDO3GyaQ1/KHkFx4InmnzdCypqWWGMSju5T2qrX+8BnaKYav8b/xj0vd8ZwjV1TlZkF7FVSyf4tAdh4CWt/2LquWhYOotcfdmrxUNtKaxvuk+Cy6Xx0oId3GA2xrENu9pnFm1rDe4cxWq1L7WahRpLpN+A//ccfYtPv7TAf/+bVIW+qZFUEcTafSVN3m7eJj2IGJ4RQ2SorcWGYy05s18yJtXEx3bjd9+6D/3ers7p4uet+UxWjWqYbpMCXpgKtpr0YI1actd+7xNYgreJ2rAQd3fupvct1zcsQ79Y/6bMqEDbu9zv77p5m/R+CWeYjb+JWZMDevyG3KPDFm33XzFjd7g8me6KM/4L5/6v2SoEk6rQKVb/WSuwGVsW8r2/B7YaHd37Rtn1kvs1LV+b9U2NxKQq5JfXsp84vRt5zzMabTtz7+cenhENb18I393f4nQH9zH3NDLL68rC9Ay15vQ0yNtZoH8vM+PC9CRM/2kw+PJGj+Mujy7zM6s72yi3d1cCYIvQM71NNJjMMhrj7S+tobSqTg/OXA69l4WRhd+wX//6+6QYQXfuWv3fJrZ+dDaC7mxjb7k76LXn6wsAntLyMj0Ixhzs2cvsd0/1zp/psec90pU8367jGWPhlAd8FgIbLUiA8XOuEKJVEUO5Z+834G1WFxIHQZGeoL+gQdCd69K3HkTVNa6iaZi9dgffXYLKUOb/BX542HPbbq6d/BZ0DX9c6T1md6IjPMh3AdBdORjqNM6/kFhPeXmQVQ+SFaMvRJir3FNefkfJI/CfIc03lXQY1/7uLQeK8fvoRA26Bw0aRGFh4PM0R40axZ49gWVCRPsJsZq54MpbedkIZNXPb6J2j/+9UQDbcw6y5ulppJasoFNMCG/OHE54SNsvYlorLsxG8JCLydFisVbnw2/+O9hqmsaeVfOIVcqptUS1al9sQ5lxoUxL2s9Cy03YPrum2TL8j7/8kgtMC5hl/opOQa3bx3ooUqKCGXTRg3znHIpZq6P69fO9e138+GrZei7a+1filDKuTN7Xbnt5T+oezwKnMRt5i/+xOst2FjGOtQAE925dmV9TzCaVP5/Vmz+avqVb2TJqPrmp2e0HP67fy6BltxGvlFEakUXIWXPa5Tiac/mozvzP8i/O3XY/dcv9V2m4XBp1G7/iM9uD3HXw3jY9j6oqxHUbygZXZ9ZHTWhyrM+Pmw8yudbIbPWeCsFRbXq+llw4JI0h8S4ed/0T26aP0Jb+r8nb5pRUY1r4DxKUEipC0zGPPPTtR+kxIWwPHQhA3Q7/2cnlO/MZZgTdbf1dERdmY2KvBP5ifp2YdS/pjWOa8MnqfUwtf59IpQpzTIbPjNr20jU+jAPpZ1CmhWAu3Q07/S+C/bAxjzitmH4pEY3LFgMwIC2KfVo8a/f535u6ancxNXUuEsJtATfKak63hHBGdInjDYdR3rzg8SYvtL5ev5/sgyX8qg7GFZYMI687pOe2mU30zEhnQO0LvN/vJb8LFOv2lTBU2czQ6MpWbfUZZoypW7qzSF8g8TMt47v1BwijitN6tk9VWUyolXFZ8cx1Gg20tn7rtxnlou0FlFTVkWypREPRf1+0Qq/kCNKUfAb+eDl8cYvPApB7LFYP1agQSQgs0+1u3Dk3NwotKBJqyxpdpOeWVLM+p4wgxU56/s/GwbStb4n7+VZu34/2/UP6ftJ6i/6b9pdhd7iIDrGQ1rUvDGq5gap7bNgek7EF7aA36Ha/LgPNe2DZs3ozrxYEWUyefdfLdxXBNT/BRW9BpLefRXlNHeuNhaFRUaWw7Xu9E3mAi1HuwHXjgXJvszIjc7zDUxLffJl6o1nd7sWj4mx2Ngy6J9wNN6+GITP8PlZksIUUI3u8xVio8IxpO/A7dU4XWw9UGMceqf9cuXudNNH13z2qzNNMzQi61WJ9IdWzL7t+ptq4hooP87OnesHjXFLwDIOU7b6Z7tQhMO5OfVa5wdtMrV6W1xKEZnwPM5QDnmw4oPegGXOLZ3GjqUz7FkcKC5z9qElo0Oj458e4xPk54VQ1CroTbXWw6ClY4b1eiVX017hC9VbwePZ0u4NulwtqKzyZ7hiM70tIrLeRmtnoXh7dlevtN/NY0C2eRmohrkrv1+aWvUhvmvjj3/Xf907j96PZ+Jt1ou/p1jSNBx54gNtvvz2gt8PRyVz41ys5gqzpj/OZawxfOYcz/cuqRs09HA4n8798F/WF8Uxx/cJ/bf/lnSv7keBnVmFHmzkui5edevOY8rX+OwX/vq+UzwpTuNc5G+foWw955nLfoeMpJJzgumJcG/zvV/51ez6n7dPHVpVnnR/win17mdAzkZwJT/KbqwvBdSVUvnQGWu6aRrdbvW0PiV//kTSlgJKgNKLP+0e7HUN8uI19MXqmUN21QC/1auDndbs8AY7SxqyDPyf3SGBH31vI0WIJKs+m9sNZfp9/7e586j68mhHqZmrUECIvf6vNe0xbY2LPBBbb9IDOsfxlv4s3q/YUM9ZuzEvv1vbM+2n90jjTPodbqmbqF6R+fLp0E2eZ9JJM09AZbX6ulphNKn+9ZBz/dOllxdq8B2FX4yZUDqeLl994nauUrwAIPfsffhvntEVw15NwaCohFXugZK/P51wujY279rHQ1Y+aiMwWRzw1Z/qIzrzm1M9pbfUbfjtcl9fU8fa3C7nSpC94qKf9zVsa184uGJnFx079nHMu9t+w8Id1e5hvu4s3q6/zXkS2grukdV1Oqd54qIGF2woYq67j2sRNKE2Ne2ml2yZl8abzVD52jmP/6a/4ff0cThf/+mEbdixUjb0X9bb1ENK6clp/RneLpQYbv+5onFQoraoju7CSF61PMuLTcd4y1gCM6aoHdd23Po/2RBasedPn8wfLa1ixu4ibzHO5YslkWP3GIX0dbucOTGGrls5apZd+wepn29YXv+ml30sG/h/K7Zuga+ATFUC/xtilJWNXrHrjrHrbEPSMrkaa3agMCTDo7hIXSkpkEDVOyE80Rtxt+drnNj8YlQEzE7ah1lVCZKc2N/EbmB5FmM3MgSoNx9p39f2k9faRr9lT7LldoIvYmXF68LShzp3p3gToWfMd+UbWWDN+JgO8nnD3sFjcREZ+8fYCnC6NLnGhJB34Wf9g59EBj3ntbQTdG3LL9EyxOdjTMG1Hvv7z3TU+TB/pt31+k9s/UutndOuXlxvXnu4FiUD08JSYGwvtyUbQvf83tuVVYHe6CA8y693dD27UA7agKIjq7PfxMozvi/tY3NsRrBX6/u3kyAZBd6S3PDw+ws+e6nB98SVZKfLetwl+M91AXWQGAJnqAZKj6j1GdAac+leY9LD+/EbQXVHroMruvf5ZVJfFFXX3UjaiXmNfpwN+nsPlZS9gweEpE3f/6ww2FvdqSz2LgO4Z3SWK97rCs6c7yKxXsvwtFj68kiCLiqpAtOIOumOothvl5UamWw2J4mvXSFZovT17uoM1PyPDyg/A5i/1nzlHvdfWfY0w4R447REIS2j8oh6jWhV0jxs3ji1btrBmzZqA3kaNGkVwcMdf+Ar/TspKJO7y13jIdBMr95Ry6pO/8NCLH5D99GlseOoc8v7ei4krr6WLkkuxGo152mukJcYdkWNNjwmhqs8lXGu/ldnOu9D8ZBPeWbaHaoKo7nMJISffdsjPef6wTD5Q9KxsxU9PNspgOF0a8z97nVGmjdQpVsLP+MshP2db/HHiAH4a+j+2uNIIrc1n/Tv3k1emr7g6nS5++v4zQt86k6HKFqrUUMKvfK/dM5wpvUdRooVicZQ3GiPkdGmUb/oRq+KkOqxTq/fWteSuc4fzSMg91GpmbDu+pfrdK3wy3j9vOcj2V2czWVmKAzOmS9455LnIgTKbVJJGXUy+FklwzUG/48O+XLKO8are5MXc/w9tfq7xWfHYzCq7C6u8XV3rySmpJmHnp4Qotdiju0EHltaDnmHodNpNzHWOQdWc1L19sd4B2KBpGs999A03Ff4NVdEo63UJSs8z2u35h/To7J0n3aDr9LaDFeyptvEnbkO9efUhNRM8qXscxUknscnVCaWuEn5pvKD17/nbuNP+LDbFgStjvG+H1nZ2et9kvgg5D4emYtr1c6Ofx/2l1YTv/IoIpYpQk8vTMKg1usaHEWlT+Bv/xfnv4T7NoAAWbc/nOtPn/HHfn9stUBzRJZZh3VO5o+5a/rxE8/s34NXF2ew4WEZUiIWrxmS2yyg80LcrACzdWYizqsTnb8HqPcVkKgeIVirAZIWEwGfLD+4cjdWsUlnjQKnMb7Q95/sNeWiaxlTrCkzVhXrQ0A4m90kiLszKC7WnUhWc1Cg7W1nr4PsNevB61oAU/fNma6ueo3dKBE5M7FKMjG693grrc0vprORhc5SDyRZwcKkoindMpXmYnjVsMFLqc6MPy7i4Sv370fe8Nvd2sZhURnaJARR2hBuB+86fPJ9fu7cEgKtM38CS/3kbbDUjM06v/FhVGQco+gipinyy63cuLzMaiQW4GOHutL54e4H+c6FpkLfRU/H0y1a9vHhcVrxe2QDQ4/SAHhu8Hcw35pbB2Nvh3n1w0h0A7DQWCrrG2uDzm+Ct8/WvyQ+fsWH1yst31c90u5wBVYu492qvN/Zuk2o0pdyzjA05JZ7jVhTF2+U+ZWCT50KjPd1dToZb1/N0+jNAvX3Zvc+Fq3+EiQ947uvOdOdXeANDlxF0JylF3vJyTdN7KORv8an88OzpbjCre1efm7jUfh8bw0Y1238nzGb27JeuH/gXV+lBc3RIvZ9d43vjQqWEMO+ebuNfNTiq3txt/baRLr1KoghvFtq7p9uibwfQXFCZj6IohNrMxCjeTLe7oap7r7jV+FrqnC6cLg0VF8Ga8bXb6iUMrEaVlL3SW1oO3j3dw2fB6Ju8I8uOA60Kun/++Wd++umnVr0lJ7f/LGMRuDHdE/j0pgmMz4rH4dLovvs9MkqW0af0F1K1PKqxsSH9EiLuXENEr5OP6LHeOGUAP6oj+XVXCd9v9G2AlVNSzdw1eqna9JH+VzJbK8xmpm7wH6nRLEQUb8C5+Rufz7/x41pmlelZ7rphszu0Y3lLbjl7JEsmvMNbzolML7iS0Y/9yOSnFvDuI1dw8q9X0EPZQ5kahXLl55iSW9/RvSWn90/jR9cglrr6UGX3zeau2VPM3Kq+XMFfMU8JvHFfoCKDLdw9czr3mW6nVjMTvP0rKp4YwMp3/8LsN1cy49UVfGwfSY0ShOOCN7B0P7zn8aWju/GOciYA1d/9xac8saTKTsymt7ApDirj+gd8keVPqM3MuKx4FFysXvB5o/3FryzYzkyTnlG2jpzdoVMH3K4am8mGIY+w1NULi6MC56tnUTL3Lrbu2s2N76zh9TXF1GGmKLo/Eec90a7PPaqLt+t07db5Pp9buE2/CB2aoQc9h0JRFG6a2J1HHXp/BW3Fiz4jdX7dUYBjyf8YZ1qH02RDPevJDn3trWaVqSeP4lOXXj7s/PFRn4vY91fs5WKTHtxZhl7Zpoy7qir0SYthsLIVW8k2n8WUwopa9uQeYLi7dL8dK1sePKs3VpPK/M0H+XDlPtj+g+fnadP+Mn6Y9xXzrHfxz1EOIoMPrdKpvn6pkYQHmXjJ9QDqPzO93ZXRA3HPLPL0Ea0KToMsJoZ0imaBy8jS7fzFpxrmm/X7GaJsJdF1UJ9Z24YRmE0971VjMvnWNZwLrM+hNWik98nqfVhqixgZU8GQTi3P5fbHHaitqTMygsZe29LqOnYXVpGsFOEKMSaMtKIqbUIPPav1ZG5ftKvnw5ArPZ/bXVjJyt3FqApknHsv3LkNRt/cpuN3cwe0P9YZv5uNhRFN01i6swgFF6NyXoPv7m2yqWB97ozq5kInRBvXKvmbPfu5uyeGoRj7pQPdgjIsIwarSSW3tEYPYN++EJ4dBVu/Q9M0zzSDSZlW789qK7q590yKQFX0XhAH7WbPYlZlrYNco1lYd3O+XjVhDfN09W7Ip4N5XBaMvB5t9C2+QffGT+H/OsOXzSdO3HPbVxvVBqQO1RdZKg6Qu1N//dyd10kaACOubbaHiTvLvruoUi95toVBVDo5pQ3GhQVFQtoQnzL1hAijvLvMG/CWW/VxXclqMbFGUE5FHrwzDf430qfjdlOZ7s22fvzq6kt4TIOO6wfW61tCjN/ritJ4bFlNndNT1h1lcXh/rxgN2KrMkbhQ6zVSMzLXITZvdZARdIc5SwAocHmz0D6Zbs+cbr3SItRq5nnHWWSf+hL0PMub6TYWBqxmhYnqKs52fIe5ppAw6i021M90uysx6qr0cyssEYJj2m0x9WjUrt3LxdGpc2wor/9xON/eehLWkbP4NP1PfN/5DlaOfg7u2EKfmc9hCmnbH972lBYdwtVj9dXRRz9ZRmm9MvMXvlzMV6Y7+XPSMoZ1ar9mZtNPHsw76CvCVZ/d4WlOsmLnQTotuIMkpZiy0AxCJt3Xbs/ZFoqiMOOUAfSe9TJZndNxujS25JUT4iimBgubkqYSfMsygjs3P36krfqkRPBM+B1cbL+feRWZPp/7dG0OGiqxvcZj6dv2LsLNyYgL5babbuOBiEfY7UogrK6IXRtW8p2Rrek+4gy4cSVBfc/skOdvTmSwhdBxN5CrxRBavZ+ahc94PvfmL+u5VNFLjkNOuvGQg7ELh6Txd/MrTN90A46FT3s+XlRp550V+7iz7loOdj4LBl12SM8TKEVRuO+cQfwy9H986RyBCSdRv73A7Bfm8dW6/RSpMawe+zwxsz4LuNQxUPHhNnZE6l3y1W3f+ZSn/bJhN72VbE7pEd8uz3Vq70ToegrfOYeiuBy43rsMinezNa+c/7z9Mfeb3gLAdOpfIa5buzxnc6YNTee9oAuxayZMO36AzfpiS3lNHRsWf8kIdTMuxXxI58GA9ChvsLjN28th/qaDjFHW6fOx29A1ujndE8O5ZZLe0Xjj50/AW39Ae/sCsn/7hfdefpIX1b/TVd3PpOL32+05QW8mNbJLHNWaDUVzweYvPJ9bsrOQ0aoxUqsNoxDH94hnvZZBiRqtdxPfri8Q7Suu4tcdhVzqHnvW57x23RJz2YjOBFstbMyr8pnSYXe4eHnRLm4yf8o7Vdei/tK23hcJ4TZiQq2scxl/D4y91xuMvcX7Ioeg3rUdLp/bqsc9uUcCIVYT+4qr+W2fb7Owj1frmeax3eP1PbjBUYecBTvJyKy/edD4Og6sg4p8duRXcKCshoHmPVhqi/RgM63lMnZ3E7C9xVU4Yo2gOn8zW43qpN7xNk+TskDmvYNetjuks36N9uPmg96Z9Ju/ZH1OGftLa7CZVUZULdCDvYTe3vLuAB+/S7yeddyQ660i23FQP+bYUCsRZUY/mbisJv+OuYPLnJIqvenalDkU9LqciloHimLsbc7boF9ntdAca7DRyX7bwQq9mZolSA+8AXYv1m/TOcp44iFw+v81uUcc9NJ3k6pQU+fy2Rvt3uPtboDnT/1Mt7sCp1DVz5t0U4l3HFZxtv5vZJrPQpN7MaK8xqF/LYZ9RubbZz83wJtT4cmePnPqG+7rLqzUs9yfW/9M+BPp3t4HFXqTwRqrHlh793TX26MdYpSYVxlBtENf2MhzegNi9/0igy36ZAPQA3pNI9RmYpuWxv6kU9Biu3kbqRlBt8Wk8oD5LR7QXiCkIptwjMUGc5DvoqW7f4a9Ui8hv3Mr3F1vYStvA+xdEdAo4WOFBN0nkJ5JEUw763Smzryf0656kKGnXUJw+JEPtuu76ZTuDI938rbjdsI+vZKSJa/z9oINnLH1frqrOVxi/tnb0bAdxIfbsE28l31aHOE1uex98RLeWbaHW19fSA9lN3bFSvglL7dLN+72MLhTNB9dN5qFfzqZ1/84nK7Tn0S5dy+9rn0dS2RShz2voiicPVAvU31/hXf/bJXdwadr9AuhC4b4X/1uL2nRIcy57VrWnTePl9P/zu70c5g9vgvf3TqOv57bl6DY9h3R1BrTx/biZdsVAFgWPIYrZy1b88qJWTKHeKWUytDOKO3QSfyUngksDNb3Xipr39HnwAJPzttCdZ2LqpQRxM9467Cer6qqcPc5g4m47C3+L+YvfO8aRqE5nlN6JjD3+tFMOXVKu+y79Seyx0nkaVFY6so9F2KlVXWE7F3I17b7uGTjte3yPIqiMOf8fvyf9Qb2uOJRS3fz+bvPMvW/i/m1KpWFtnHUDbwSRhx6k7hABFlMzDh7Ei84z+Iz11jWqXo3+We+XsPdzhf1Gw2Z4dNsqbVGdYnlZ5e+F17bPs+Tdfl6/X7ONhn7Xnu0T9PE+q4b35WzB6SwxZlClWZDyV5Ixtxz+IvjKSKUahxpI1HO8b+X/VCM7hrL1y5jJNNGvcdHaVUdG3OKPfPIyRzf6sc9s18yGqq3m/havWP1hyv3Ea2VcbbJmLk8NPCxboGIDLFw/cn6AtAjX22ibOkb8MGVvLxgKzFFa7nCPA8Vl569bwNFUeiVHM4Kl7GVZ98KcNaxzt3pPTVSD87qN04KQLDVxMReeubvk9X79BLqVa9TU1nG20t3E0Qt12Q0PSmitbrGh5EcGcR+RwRl0cbWgQ1z+WWrHpBMjzEqOjLHB1TlEBdmIzHChqbB3qRJekOs5IGeLUHDQ3L1OcQhsU3uP/ZnSl/97/sXv+/3Nuna8g3frdTPzUm9E7H8bjShHXhpwI/r1i/VmAe+p0RvGPmfYdiNyRC9UyIgp14JdxN8yssN7ix3alSwHpTlGT9Lic1v04gNs3kar63Za2S7e59DXZ9pLCmJAmBo58D/rlhMquf43B3MHave4N6Kx5igrvGUn/PDw/rXX687vDvLbHe4PMHrfvTANUWp1zncHXQ3mEAQbDURF6afO3vrZbtzCss4V13EJaUvepssVuQb2WrFpxLCMzbM2Frozng7Te6u68bWB+OaoCZErzIubZDpjgg2e2eoG5nroFr935y6euXl/oJupx1qSj3N1CprHdQ5NYxt256g22pWKUH/3llqSwh3jy5tOGrRU17uO/7O44Mr4OVJkLfe/+ePQRJ0i6NKsNXEo5dNYKkyEBMuor67mYvmj2WEuhm7GkzoRS+1e+nmpSf14v3MOZRrwfxz/yDum7uOnNog/h3/MNr5L6OkdUz2+FCkx4QwPiueAT17YLMdnr4JFw/vhElV2L5jO/t+eRWAD1fs5UXXX3gy7C1GJXR8h0mTqnDWoAxmzryRO2fP4t7Te3karhxJwVYTZ1x6M185R7DW1YWLPing8ucXkajpf5BDpj7R6j2T/phNKsMnnMUyV09MWh01n97Kz5tyWbV8EQD3n9G73brWt9a4HgncffOtTHp4Hr/99RxemTGM/mlRHfqcUwencXfdLM50/JPSJL3c+ovfczlL1QPwoHT/nWzbIi06hGf+OJEbbH/na+dw7tozgiq7kxGZsQy86R0s5/7rsJT0u53ZL5nfu9/ELfbrufjtnVz16nLGrLmTbmoutcEJqCcfWnXOyC6xbLT2p1qzopTug9zVFFXaWb89m4mqcQHe/+J2+Ep8qarCU9MGMHTCuZznnMP3ziGUaKEUmJOoHn0X5is/bXUgF4jxWfF87xyCQ1Nh/29QtJN5m/IYpmwiRqnQ91unDm7146bHhDAgPYqPnEaWfMs3VOdn89bS3Vxj/hIrdr2U1b1ntR1dfVImWYlhmCtysX17B2z8lEk/n8+b1jmYcUKf8w+p/0CflEi2aalUmcL1EtEDv7N6TzEKLvqltv17NG2ovoD70aq9OF+aDF/czKaPH6Ww0s7doV8zdsF0+OqONj9+fYqicFZ/PUD5Upmgf3DVa3z5Ww6gMclhTEdoRT8Kd+n9otBT9YZY6cP43cjae2aXpwxu1e+L0/sloSrw294Sdtt66M0hHTVYf9cXcc4bkKxXYtgiof9FAT+um3tc2/JdRXrX+IKt2HL0xbW+qZHgbuCa0vTPQJpnHrURWFYXU7RlEZnKfk8mnYNGlr+FoBu8c9tX7TaC7pHX8euAR1nq6kV6TLC+Dzt7sbFto+XrD+++bj3ort61gjNNyxht3qJns2vK9M7e39yl72E2BFlMRIXomev9ZXoAucupLwxFu4q9M9ObCLqhfum9N+jeUVDDXy2vMTTnTSjYon/Q/fpEZ/hUh7kz3e595e6gu8xqNBkzgm33v45Q/Zx2B9vuIDoiyKJXIYCnKajWeSxfO4ezwZ6I06X30/DJdFuC9H3dAJUFhFkV/mj6hsidX1Bd491O597TbTGplGr699tsL6VEC+W7mMtg2CzfF8XiznQ3MRXIvff8RO1eLsTh0C0xggHXvsJHoRdTq5kxKy5Kg1KxXPUFxGe1+/MpisJtV1zI1xO+IjvldPqmRnD3lJ787drp2Pq1bRTJ8Sg1KphLepr5xXYbKT/dTuHOVWyerzeaO8c1H/UQu8kf64ZkxKKe91+uc93Nipxa8qpcPBnzEKUXfojSTns1AS4b2ZnXI66jVjMTtPM7Br83hC8t9/KfzCWM6to+Y4cOhaoqhy3w75cayf74cWxwpPLBqn1omsaXS373zh5uQ8anOX1TI3n7jvMpPftlZk/sw6szhvHurJFEhYcd1oAb9N9bT108iBGZMVTanfy0JZ9aLFSbI7FNf897YdVGVrPK2N6d+NY1TP/A6jd5f8VepvIzNsUBif28Za7tzGxSueO0Hnxw3xXEXv0x1bfvIO7PWwg+7c8dNpWgS3wYXTp39vQJYOUrfPFbrneBofc5bZ6YMX14JzZpnVmh9AVXHRvm/hN7ZQnTzUbTrgn3dsj5YzObeOHyoWgRKVxvv4lKzUZ3NYdQpRat8xg49z+H9PgjMmPQUHnEdCNc8zOuxP4s3VnESHUTs5ZNhq/vatPjju0WR6/kCKrsLt6z6c0n++18nn+Yn+dK58f6jZqZl91aFw/X+7X8Y/8AXLZISlLH8/veIkapm4iq2q0HB70D3zrl7ga+0ZjxfaC0hgNlNagKJE24Wt+Lfvr/teoYE8KDGNtdzzi++utuGH4NAJc755IVWs24Hokw6SG4fWObuj0Pz/Tuoa5L11/bzmWrUXDRNznMOwe7mYWn9OgQFEVvwpVfXguLn2HK0suZZfqSXsnhUF3i7Q4eQH+T4cZCQP056r/u0P/vXiTg5znwxjmwvOlxjm5djcB/uzEGLS9E38oy0LIXVVW8vRxCEyDYtwrUUzpvZPH3VFu5yX4jL/d8ydtt2z1f208Fg78qgB0FlWzSjNu6GxE2UQngzXT7Bt1VQcZ+8HJjLKCR8dbCm8p0W/SO4Lf85hlJZhl/B9fX3coarTsVNQ6q65zUOfX0tad3hmdfdz5J5koetLzJ0JV3UuvQFydUxdtAzWLSm7gBmGpLOEAs3ybO0kfF1edeVHDa9UqKlyfDp9d7P+/uR3Iczek+fneri2Na96RIut/1PKXFc1DrComM69JhI3hADxQuOnkIFx3ZXnJHvdlnjWXRvwZwKitQXj+HB6gDBZSxtx3yRf7x4PTB3enfJY15Gw4QHWplSt8kbOb2PW8tJpX7Zk5jzvM53F39NBFKFXbFxpRhbW/SdqxSFIWrxmRwzyfr+O/P21GLtnJO4StYzU4cyYMxH8KosKZEBFm4ZPiRa6pYX6jNzDuzRvL1uv1kF1SSHv0wwb36NrpgbKtpQ9P519oJnGdajGvtO3zDMC5XjO0lI65pl+doTmSIxbOX9XC4eFg6r30ymXGmdThWvMbG8l4sUS5h6qmnENe19Vlut6mDUnnmx238reQi7goN54bsUygniBWnfcwp9l+g+2nt+FX4yogL5cubTuKlhak8kj+aqZGbGda7B2q3Uw75b+rwzBhMqsI7Zf24LqgHpXn6XOBTbBuw1BQ26nofKEVR+Ms5fZj2/BLu396DIMtJ/MG0kGlmI+s89I/Q9/xDOvb6usaHMS4rngVb4fqE16krDsNJHo+GfQB2YMDFjUtjm9E7WS/V/n1fKdSUsm/VD6Qp5YQldiHEagZrQpsC42tO6sKCrfm8s3wPp/acRLySQRbZvBD9BlbzBfqNbGGtflzQX4OYUCtFlXZ+07owxBJCRF0ZvZXdDLUY/QiCIptt/hZsNZEZG8rOgko2Hygj3mhG1l/dRUhShHffcUyXgLYcndxTf43W7i0hv7yW+HAb8zbm0UPZwwzrViiJ8WwrCqQSoadRFecu9d9u6kI3oIe2S98+4842+5l+khYVwvqcMk/QvKewim9doxmS3tu7GGeMh/P3GnlndetZ3ZIqOwUVdjaaOzNS3eQNuvfrk04aBt0J4cbYsgaZbntoCpQCxbv1G058CAbPoKLEAiv3esrhvXu6zRDlu+3IalYJtpiornNSVlOH2aQvAJpVhRBjDBjdJull77YwklT9b0C1NYZqp37bIIvJs9huM6uUaHpAbaotAUD1t6gYHA337dcXUnf+DHuXejry63cynluToBuAhQsX8vzzz7Njxw4++ugjUlNTefPNN8nMzGTs2LHtdYziBBYZHQN0zH5Q0XrpMSFsPedf7P78PDorehOz6pSRBJ906CPcjhepUcHMGBN4E5u2SIsO4d4772HtlmkkVO0go/cwlBN00eOCIWm8tngXFxX+l6vWfuf5q2Y+9eEjelyHi0lVOHtAivFe93Z97BGZMdSljWH+/kGsd2SwzRHE/+Lu4NxpD2JNHdiuz3U0OHdgKs/9PIbfyz6mf90uzjAtI7/3lcSNP7QGkVazypPTBnLZy7VcXnEToAf4p4zuD4xqhyNvXny4jXvP6AX0Aia12+OGB+mLIst3FfHl7/uNGcIa51hWgINWddBuaHhmDH89tw9//WIjf6q7hv1hfZiZUURw1ngYOL3dvga3+87oya/bC/h2WxVQhUlVCet9KmwrgfH3tOqxhmboC0Ub95dhn3sTQ7d8xsWmc8npdGhbCMZ0i2V8Vjy/bM1n+iur6KrcwLtBj9GpruWu6i1RFIWx3eL4/Ldcvt1YSKfEsSTs+57poStI7HUDXLsYSna3uFDTMzmcnQWVbNpfxtj+A1GAHspeLPEW2LpUv1GAfQQSI4LolxrJupxSvl63nxFdYnAVbOc72z1ov5kg/wu9DDxzvN+S7sbHplcgbNqvB3Yrq5I4VVOIdJXoM6PzjaA7rnFFZcMO5O594Z2Nfee4nN77+xmRl16/szvebPu+oCxwfAt7jNdmr/s18m3a5+6gfsDoJp9fof/riMyAXKBoh37D0DgIjSPYWg7s9Z/prs9ZBzVlRAXpQXdpdZ2nMVxksMVbtXbGP73HoujbDiosMZ4O6u7O5eCb6bbUlhBPCUl1GtRk+m4NUhRv/xn3yDBLvXnlyvGX6W5zefnHH3/M5MmTCQ4OZs2aNdTW6qsupaWlPProo+12gEKIo8vEIX0Iv3kx2SMfwX7OcwT/8QvfX5TisLCZTYzo053MYVNO2IAb9FLkl2YMwxndFbtmwokJ16mPQJfWN70SvhRF4R8XDuBu63085bgQsy2Mf108EGunYW0utT6aWc0q/5w2iD8rN/Gc4yzmhZ7Dw2cHPpe7OcMzY/jqprHcNimL/1w6iEfPa//RjkfCHwbrWbPFi39h0JKb+Mp6H4mOHL0k+xCCboArRmWw9L6JfH7zeK676zGCL3pR78jfAaX4PZMieOaSQUSHWAi1mvi/P/Qn/vR74covIDyx5QeoJzEiiKzEMDQNNoTrCahT1VVcqX0Gr58NGz5t0zEqisKT0wYwItNIRMRlUX71MtTzngto9nVLzjT2tn+1bj9favpxn81Cfb9+Ul/o2fJ0kF5J3pnfO2qjyNcisChOutq36NMOup4CXSYEfEzuBq2vLt7Fcz/vIFtL5veQkSiaE3JWAQoE2L8iKzEMxRiNVlBRy/r8OnZoxoLlgd+93cL9bJupXx6uaRq7C6tIoYB+uR/C6jf14H/qs/oCjZ8FgPQY/f47jcZyW/P0oLsg3miwmLtGLy0vzta/prRhDZ7fmynXNM2T6Vbd0zKKdvkEp+7guqymDpdL8wbdQWbI3wrzHoJFT+sLBf/swpcOvXKprLrOdz+3H3HoFSxlpthGnctBXwguM4Jua10p080/cNe26TDvQb+PB3iDbnO9a0kpL/d65JFHeO6557jiiit47733PB8fM2YMjzzS/rN6hRBHj5jYeGKm3HSkD0MIwBg3eMcctJp7UDRNHyUk2kWX+DDm33kyq3YX0S81yrO38Hg1uFM0L90xnQ25Z/NdRjThQe23uNA9MZxbEo9848f2dGb/FB79ejPhldmcYl3mTeUM/WObS53riwuzERd2eM65M/olM6VPEi5Nw2zsTyUhsFnaDZ3UPZ6teRXctz6JzzQTPdR9sM7IFh5CA8LYMBvvzx5FWU0d4TazkYlsn9GI47PiiQ6xsL+0hsdKO3OqNZ50Rz4c3KyP5QpA3zS9tH7l7mKWZRcT4urHeabFmHfM05vKDWhdk7cLhqTx1A9byS6sIrtQzzK7pj4HK+7Wy8FPugM6jQzosUKsZrrEhbIjv5I1e0rYfKCcNa7uZKk5ennzvlX6Df1k4j1Bb3EV+eW1VNc5Ocm0m7hfnoDEvvr+6Ga2PPQ1ZorvKqikpMrOupwSAFI6dQVHT8jfDDkr4Q8v66OygnxH47qD/kq7k+KqOk/QHZyQoS9iRGfoTdR+/TfE9yBi4AxAX4upsDsortQD6egQq77ve/HTEN8LkvWxkBUmfbGkrKbOk91ulBV3OcFRQ4xLb8BWbIrF6Qm6fXO4i5VBXGePoXt0P2IKPjC+AX6SA9/dr+/zd5fkm+v9rKtGiCqN1GDLli2MG9d4ZmVkZCQlJSWHckxCCCFEqylBkRJwd4DIYAun9Ew87gNut4SIIE7umdCuAffxKsxm5h8X9Ge+OpLXXWegoehNzia0riT7aKGqijfgPgTThuojLDeVmPjAOcH7ifDkdtmPHhFkafeGlUEWE7efqpdW27HwRsJdaLYIiEgO+DGGZ8RgMSnsK67m9V+z+cFpBOu/fwh1Nc3f2Y9Qm5knLhyA1ax/T24+pRsDszJh+gdGM7ArWvV4w40qgbeW7qakqo4l6gA0kxX2/64Hd0GRejDaQGdjjvfO/EpPtrow3LjdwU1QV93oPvVFh1rrjUArYc2eEgAGpkd5m/QVbIN+F+gN8RoIspg8Hcz3FlWRU6I/X1JUGFzxGZz9Lz14Xf48LP4XQRYTNuM1yy+vpaJWD1xjw2zepmhVBVCqN14rN+sBcVm1w3+me/Ub8Lc4+OQaYu36ffJMSZ5Md7DVd9tBjimNb1wj2GHuSoxi7NP2F3Rv/Q42fe4tj3d3NAcYdDlMuM/vHvtjVZsz3UlJSWzfvp2MjAyfjy9atIguXboc6nEJIYQQQoij3OQ+Sax54DRc2qkoFo7LrQet1SMpnIuHpfPeir08b7mc87qGEFqTB5Mf7bAO/O3hspGdiQqxkldWw0XDTkMxXQGmwBfbQm1mhmfGsHh7IVvzKtjJUOzhnbBq9jZvQ5vYK5Gl906kstbhaUjWViO7xPLu8r38slUf51nW6VSUK+7WO2nXlOoBtNp40SUzLhSrSaXK7uT7DXo/m8jEzlCYBBUH4KM/wrCr9QWnJr7OQZ2i2FVQyfcbDrAlr9zzMTJn64F3C2PUOsWEcLC8lq155eQZXcw71389GjSCiwy2cLC8ll35+iKBxaTo5eV17qC7EIr1fgClQXqZvbsM3X1/j/BkvYS+cAfRDqOTO0l0shuZ7gYNY92dzGvqnETjDrr99GdydzCvOGi8X686xuiufjxpc9A9a9YsbrnlFl555RUURSE3N5clS5Zw55138sADD7TnMQohhBBCiKNUqE2G4TQ05/x+XD6qM2lRIYSGnHekDycgilK/MSNA6xdQZo7NZPH2QgAGZyRgPfdtWPSk3pm6FV3g64sJtRITam3Tfeub1CuRMJvZk/kd3zvdG/gFRTZZqm42qXRPDGNDbhkfrNS7d/dPj4K4KbDqNdjyNWz5Rh/ZZknx+xgndY/jk9U5vLtcv3+/1EgSI4KAoICmv6THhLBydzG/7tBf2/Ags2d+OPZKvZoAGgXdO/L1/eMxoVa9OiIsAVQLuOpg968AVIboe+fLqutwdwfwCbpj3XvHd7Jh/Ae8++1PKGpvYozXseHPf7ipjhHqUgaUmIlWjDnm/jLd7iDbXqH/vx22pBzN2vxb8p577sHlcjFx4kSqqqoYN24cNpuNO++8k5tukr2eQgghhBDixKQoCn1SIlu+4XHmlJ6JPDK1L5sPlHHzKd0hIggufO1IHxagB4e3nZrFI19tpGdShKdRWyD6p0WyIbfME7D3S42E0Ev1oBugx+kQ4T/gBjitdxKh1vVUGtnhs/oHXrYP0DVeXxyYt1HPtHeKCdGD6Ny18OLJeiYaIF1fOIgPt7HtYIVnRFpMqFGxoJr0sW0FW2CP3om8NtQIumsc1Dr044sNq7fIEdVJr3hw1mILCeM713D6OSOpNF6LsAZBd4jJyf+sz0AJFClGIO03021k6ofObJzZLtkDNWX6axrAiLljQZuDbkVRuP/++7nrrrvYvn07FRUV9O7dm7CwMKqrqwkOPnrLZ4QQQgghhBDt77KRnY/0ITRp5thMzh6QTFyoDVUNfF/8xJ6Jniy11awyokss2BLhvBf07udjb2/2/qE2M385ty/3fbKOfmmRXD6qda9Rn1R9Accd9HeNN4LZxD4QEgeVB/VscabebyspQi9z37S/DIC4+kF0XHdvOTrgiNSPpaTKTkWtHnT79PBQTRCfBQfWEVe2CYihstbhWUAItfmWl9eawijXgglXqolxZ7pD/cymd1cZ1FU1/txXd8K27+Cc/xw3peaH3C3CarXSu3dvhg8fjsVi4cknnyQzs2Nn1AohhBBCCCFEayWEB7Uq4AYY2z2ODKOh2qXDO3mzuwMugsl/D6hE/IIhaaz7y2l8dO0oQqyty3v2S/WtmuhvdIrHZNHHlXUeqzdUM2ZhJxhBtzvTHVu/PN9dLg7QbRKmpN4AHCzXx6kBjacGdB4DQOrml4mnmIpah2cBoGF5udViJkfT946vcGWxLvFcfV94Q+6g217R+HPHYffyVme6a2trefjhh5k3bx5Wq5U//elPTJ06lVdffZX7778fk8nEbbfd1hHHKoQQQgghhBCHVZDFxEfXjWZDbhkndYtr8+PYGjQdC1RcmI0+KRFsyNUz1+Oz6o2K6z5Jf6snKcI3aE6JqleBPPSPMORKvcwciNtRAEBeWQ01dS7P8/kYcAksf4GQg2voqe5ldW1ck+XlVpNCjhZHT/byifMkkrpfRz+Tn5DTYgTd8/8Ku5fAqOv1We7gbWinHT9zulud6X7wwQd59tlnycjIIDs7mwsvvJBrrrmGp556iieffJLs7GzuvvvujjhWIYQQQgghhDjs4sJsjM+Kb3WWvL3cObkH8eE2LhvZie6JzTelS4r07aKeGl0v6I7u7Am4wVuKnlfmzXQnNBwRmTIQZv5A6XnvsNDVn6o6JxU1TWS6zSr7jEx3ZyUPs6mJ12vig3BvDmScBNvnQWWB93OeTLer2a/zWNLqTPeHH37IG2+8wTnnnMP69evp378/DoeD3377rd1nBgohhBBCCCHEie7kHgmsuH9SyzcEOsWE+ryfFt30uDV3Kbq7XBz8ZLoB0oZgTXAC36JpkG8E6A0z3RaTyjqtC3bNTKpSwD5PT/QG3N3KHcYMd2u9Y1aMioDjqLy81Znuffv2MWSIPuy+b9++2Gw2brvtNgm4hRBCCCGEEOII65rgG3T3Sm46Mx5mM/sEzrGhVoKt/svggywq7kR/XlmN5/71WUwqvzr7oKBxtmkpmaVLmz9Yuz5L3CfoVo3nP47Ky1ud6XY6nVit3s34ZrOZsLDje66aEEIIIYQQQhwLbGYTgzpFsWZPCTazSkJ4ULO3T48J8XQ67xTbdFZcURRCrWbKax3klemZbn/l5bnEcZn9PkKVasbFj/X/YHuXw6rX4eBG44714klPefkJHHRrmsaMGTOw2fSyg5qaGq699lpCQ31XVD755JP2OUIhhBBCCCGEEAH76zl9mfPNJmaN69LibbMSwzxBd2ZcaLO3DQvSg+7S6jqg8cgwq0kvpF6m9QINTjE1UVhduhfWvuV931Iv2O82CULjIHVIi8d+rGh10H3llVf6vH/ZZZe128EIIYQQQgghhDg0/dIieWfWyIBuOyAtis/W5gIwtHNMs7eNCbWyv7TG8364zeLzeUuDxmkN3/d+okFwH1Lvefuer78dR1oddL/66qsdcRxCCCGEEEIIIQ6zcwam8MyP21AVhUm9E5q9bcMmazFhVp/3rWbfzLZZbSLTbW0YdLc86/xY1uqgWwghhBBCCCHE8SEuzMaiu09BofEebX+3rS8mxDfotjQoJ29yZFhQhP5vaALcth5M9TLmVUVQUwq2CAg9PoLxVncvF0IIIYQQQghx/AizmVsMuAHiwr1BdpBFbdTpvGHQ3fB9j8h0/d/Kg6A1mMe94J/wzED49ZkWj+dYIUG3EEIIIYQQQogWxdfLdMeGNp7nbWtUXt5Epjs4GjA+V7LH93MyMkwIIYQQQgghxImofnl5cmTjUWQBZ7oVBWIyoaYMassbfM4Iuo+jkWGS6RZCCCGEEEII0aJuCd552l3iG48XC3hPN8CFr+ldyqMzfD8uc7qFEEIIIYQQQpyIshLD/f7fLeDu5QDJA/S3hqS8XAghhBBCCCHEichqVnl2+mCWZxdxyfBOjT4f8Jzu5ngy3Y62HOJRSYJuIYQQQgghhBABOb1fMqf3S/b7uUaZ7qb2dDdHMe4j5eVCCCGEEEIIIYSXteGe7qa6lzcnZRAMmwXpw9vpqI48CbqFEEIIIYQQQhyygLuXN6fbRP3tOCLdy4UQQgghhBBCHLLG5eVtyHQfhyTTLYQQQgghhBDikDXKdDfXvbwpddX6/G6TBUJi2unIjqxjLtOdnZ3NzJkzyczMJDg4mK5du/LQQw9ht9uP9KEJIYQQQgghxAnLavbNbJvakule+zY8kQVf3NJOR3XkHXOZ7s2bN+NyuXj++efp1q0b69evZ9asWVRWVvL4448f6cMTQgghhBBCiBOS1WTyeb9NI8Pc3cs1Vzsc0dHhmAu6p0yZwpQpUzzvd+nShS1btvDss89K0C2EEEIIIYQQR0jDIDvIYmrils1QjPvIyLCjS2lpKTExTdf719bWUltb63m/rKzscByWEEIIIYQQQpwwLA0aqQWZ2xJ0H3+Z7mNuT3dD27dv59///jezZ89u8jZz5swhMjLS85aenn4Yj1AIIYQQQgghjn/153SrShvLy1UjUNeOn0z3URN033PPPSiK0uzb5s2bfe6Tk5PDlClTuPDCC5k1a1aTj33vvfdSWlrqedu7d29HfzlCCCGEEEIIcUKpPzIsyGJCUWRPNxxF5eV33HEHM2bMaPY2Xbp08fw/NzeXk08+mdGjR/PCCy80ez+bzYbNZmuPwxRCCCGEEEII4Uf9kWFt2s8N3qBb9nS3v/j4eOLj4wO6bU5ODieffDJDhgzh1VdfRW3L/DchhBBCCCGEEO3GJ9NtbmOMFp0JA6dDfM92Oqoj76gJugOVk5PDhAkT6Ny5M48//jj5+fmezyUlJR3BIxNCCCGEEEKIE1f9PdxtznSnD9PfjiPHXNA9b948tm/fzvbt20lLS/P5nKZpR+iohBBCCCGEEOLEFm6zeP7vcEls5nbM1WXPmDEDTdP8vgkhhBBCCCGEODKCrd7sdk1dG/dkOx1QWw61Fe10VEfeMRd0CyGEEEIIIYQ4ulXb2xh0b58Hc9LgjXPb94COIAm6hRBCCCGEEEK0rzZMC9Pv5x4Zdvx0L5egWwghhBBCCCFEu0gI10c1J0cGte0BFKNE/Tia0y1BtxBCCCGEEEKIdnHzxO50ignhkan92vYAipEidx0/Qfcx171cCCGEEEIIIcTR6bKRnblsZOe2P4CUlwshhBBCCCGEEB1ElfJyIYQQQgghhBCiY7gz3a7jJ9Mt5eVCCCGEEEIIIY4OoQnQ5zwITz7SR9JuJOgWQgghhBBCCHF0iM+CC1870kfRrqS8XAghhBBCCCGE6CASdAshhBBCCCGEODpomj4uzOk40kfSbiToFkIIIYQQQghxdDjwO/w1Gp5u45zvo5AE3UIIIYQQQgghjg4yp1sIIYQQQgghhOggnqBb5nQLIYQQQgghhBDtSzHp/x5Hc7ol6BZCCCGEEEIIcXSQ8nIhhBBCCCGEEKKDqEamW9OO7HG0Iwm6hRBCCCGEEEIcHRRF//c4Ki83H+kDEEIIIYQQQgghALCGQffJYAk60kfSbiToFkIIIYQQQghxdAhLgOkfHOmjaFdSXi6EEEIIIYQQQnQQCbqFEEIIIYQQQogOIkG3EEIIIYQQQoijQ2UBPJIIf4s/bjqYy55uIYQQQgghhBBHB0UFR43+f80FiunIHk87kEy3EEIIIYQQQoijg3tkGOhB93FAgm4hhBBCCCGEEEeH+pnt42RWtwTdQgghhBBCCCGODkq9EFUy3UIIIYQQQgghRDtS62W6Ncl0CyGEEEIIIYQQ7ad+pvs4KS+X7uVCCCGEEEIIIY4OqhkyTtKDb+X4yBFL0C2EEEIIIYQQ4uigmmDGl0f6KNrV8bF0IIQQQgghhBBCHIUk6BZCCCGEEEIIITqIBN1CCCGEEEIIIY4ej/eAxzpBWe6RPpJ2IXu6hRBCCCGEEEIcPWpKwFEDLseRPpJ2IZluIYQQQgghhBBHD3fXcs11ZI+jnUjQLYQQQgghhBDi6KGY9H+PkzndEnQLIYQQQgghhDh6eDLd2pE9jnYiQbcQQgghhBBCiKOH6g66JdN9xNXW1jJw4EAURWHt2rVH+nCEEEIIIYQQQhwqd6ZbysuPvD/96U+kpKQc6cMQQgghhBBCCNFeUgZB2jAw2470kbSLY3Zk2DfffMP333/Pxx9/zDfffHOkD0cIIYQQQgghRHu47OMjfQTt6pgMuvPy8pg1axaffvopISEhR/pwhBBCCCGEEEIIv465oFvTNGbMmMG1117L0KFDyc7ObvE+tbW11NbWet4vKyvrwCMUQgghhBBCCCF0R82e7nvuuQdFUZp927x5M//+978pLy/n3nvvDfix58yZQ2RkpOctPT29A78SIYQQQgghhBBtVlsONWXHTSM1RdOOjuFn+fn5FBYWNnubLl26MG3aNL744gsURfF83Ol0YjKZmD59Oq+//nqj+/nLdKenp1NaWkpERET7fRFCCCGEEEIIIU4IZWVlREZGthhXHjVBd6D27NnjUx6em5vL5MmT+eijjxgxYgRpaWktPkagL44QQgghhBBCCOFPoHHlMbenu1OnTj7vh4WFAdC1a9eAAm4hhBBCCCGEEOJwOWr2dAshhBBCCCGEEMebYy7T3VBGRgbHWIW8EEIIIYQQQogThGS6hRBCCCGEEEKIDiJBtxBCCCGEEEII0UEk6BZCCCGEEEIIITrIMb+nuy3ce8Drjx4TQgghhBBCCCEC5Y4nW+oxdkIG3eXl5QCkp6cf4SMRQgghhBBCCHEsKy8vJzIyssnPK9oJ2Prb5XKRm5tLeHg4iqIc6cMRASorKyM9PZ29e/c2O3xeiNaSc0t0BDmvREeQ80p0FDm3REc43s8rTdMoLy8nJSUFVW165/YJmelWVZW0tLQjfRiijSIiIo7LH1px5Mm5JTqCnFeiI8h5JTqKnFuiIxzP51VzGW43aaQmhBBCCCGEEEJ0EAm6hRBCCCGEEEKIDiJBtzhm2Gw2HnroIWw225E+FHGckXNLdAQ5r0RHkPNKdBQ5t0RHkPNKd0I2UhNCCCGEEEIIIQ4HyXQLIYQQQgghhBAdRIJuIYQQQgghhBCig0jQLYQQQgghhBBCdBAJusVR7e9//zujR48mJCSEqKiogO6jaRoPPvggycnJBAcHM2nSJLZt29axByqOKUVFRUyfPp2IiAiioqKYOXMmFRUVzd7nwIEDXH755SQlJREaGsrgwYP5+OOPD9MRi2NFW84tgCVLlnDKKacQGhpKREQE48aNo7q6+jAcsTgWtPW8Av1v4umnn46iKHz66acde6DimNLa86qoqIibbrqJHj16EBwcTKdOnbj55pspLS09jEctjkb//e9/ycjIICgoiBEjRrB8+fJmb//hhx/Ss2dPgoKC6NevH19//fVhOtIjR4JucVSz2+1ceOGFXHfddQHf5x//+AfPPPMMzz33HMuWLSM0NJTJkydTU1PTgUcqjiXTp09nw4YNzJs3jy+//JIFCxZwzTXXNHufK664gi1btvD555+zbt06zj//fKZNm8aaNWsO01GLY0Fbzq0lS5YwZcoUTjvtNJYvX86KFSu48cYbUVX5Ey10bTmv3J5++mkURengIxTHotaeV7m5ueTm5vL444+zfv16XnvtNb799ltmzpx5GI9aHG3ef/99br/9dh566CFWr17NgAEDmDx5MgcPHvR7+19//ZVLLrmEmTNnsmbNGqZOncrUqVNZv379YT7yw0wT4hjw6quvapGRkS3ezuVyaUlJSdo///lPz8dKSko0m82mvfvuux14hOJYsXHjRg3QVqxY4fnYN998oymKouXk5DR5v9DQUO2NN97w+VhMTIz24osvdtiximNLW8+tESNGaH/+858PxyGKY1BbzytN07Q1a9Zoqamp2v79+zVAmzt3bgcfrThWHMp5Vd8HH3ygWa1Wra6uriMOUxwDhg8frt1www2e951Op5aSkqLNmTPH7+2nTZumnXnmmT4fGzFihDZ79uwOPc4jTZbRxXFl165dHDhwgEmTJnk+FhkZyYgRI1iyZMkRPDJxtFiyZAlRUVEMHTrU87FJkyahqirLli1r8n6jR4/m/fffp6ioCJfLxXvvvUdNTQ0TJkw4DEctjgVtObcOHjzIsmXLSEhIYPTo0SQmJjJ+/HgWLVp0uA5bHOXa+jurqqqKSy+9lP/+978kJSUdjkMVx5C2nlcNlZaWEhERgdls7ojDFEc5u93OqlWrfK67VVVl0qRJTV53L1myxOf2AJMnTz7ur9Ml6BbHlQMHDgCQmJjo8/HExETP58SJ7cCBAyQkJPh8zGw2ExMT0+w58sEHH1BXV0dsbCw2m43Zs2czd+5cunXr1tGHLI4RbTm3du7cCcDDDz/MrFmz+Pbbbxk8eDATJ06UXhQCaPvvrNtuu43Ro0dz7rnndvQhimNQW8+r+goKCvjb3/4W8FYHcfwpKCjA6XS26rr7wIEDJ+R1ugTd4rC75557UBSl2bfNmzcf6cMUx5iOPq8eeOABSkpK+OGHH1i5ciW3334706ZNY926de34VYijUUeeWy6XC4DZs2dz1VVXMWjQIJ566il69OjBK6+80p5fhjjKdOR59fnnn/Pjjz/y9NNPt+9Bi6Pe4brGKisr48wzz6R37948/PDDh37gQhznpBZEHHZ33HEHM2bMaPY2Xbp0adNju0vo8vLySE5O9nw8Ly+PgQMHtukxxbEh0PMqKSmpUXMPh8NBUVFRkyWYO3bs4D//+Q/r16+nT58+AAwYMICFCxfy3//+l+eee65dvgZxdOrIc8v9e6p3794+H+/Vqxd79uxp+0GLo15Hnlc//vgjO3bsaDT14w9/+AMnnXQSP//88yEcuTiadeR55VZeXs6UKVMIDw9n7ty5WCyWQz1scYyKi4vDZDKRl5fn8/G8vLwmz6OkpKRW3f54IUG3OOzi4+OJj4/vkMfOzMwkKSmJ+fPne4LssrIyli1b1qoO6OLYE+h5NWrUKEpKSli1ahVDhgwB9AtUl8vFiBEj/N6nqqoKoFE3aZPJ5MlUiuNXR55bGRkZpKSksGXLFp+Pb926ldNPP/3QD14ctTryvLrnnnu4+uqrfT7Wr18/nnrqKc4+++xDP3hx1OrI8wr0a6rJkydjs9n4/PPPCQoKardjF8ceq9XKkCFDmD9/PlOnTgX0Cq758+dz4403+r3PqFGjmD9/PrfeeqvnY/PmzWPUqFGH4YiPoCPdyU2I5uzevVtbs2aN9pe//EULCwvT1qxZo61Zs0YrLy/33KZHjx7aJ5984nn/scce06KiorTPPvtM+/3337Vzzz1Xy8zM1Kqrq4/ElyCOQlOmTNEGDRqkLVu2TFu0aJHWvXt37ZJLLvF8ft++fVqPHj20ZcuWaZqmaXa7XevWrZt20kknacuWLdO2b9+uPf7445qiKNpXX311pL4McRRq7bmlaZr21FNPaREREdqHH36obdu2Tfvzn/+sBQUFadu3bz8SX4I4CrXlvGoI6V4uGmjteVVaWqqNGDFC69evn7Z9+3Zt//79njeHw3GkvgxxhL333nuazWbTXnvtNW3jxo3aNddco0VFRWkHDhzQNE3TLr/8cu2ee+7x3H7x4sWa2WzWHn/8cW3Tpk3aQw89pFksFm3dunVH6ks4LCToFke1K6+8UgMavf3000+e2wDaq6++6nnf5XJpDzzwgJaYmKjZbDZt4sSJ2pYtWw7/wYujVmFhoXbJJZdoYWFhWkREhHbVVVf5LOTs2rWr0Xm2detW7fzzz9cSEhK0kJAQrX///o1GiAnRlnNL0zRtzpw5WlpamhYSEqKNGjVKW7hw4WE+cnE0a+t5VZ8E3aKh1p5XP/30k99rMkDbtWvXkfkixFHh3//+t9apUyfNarVqw4cP15YuXer53Pjx47Urr7zS5/YffPCBlpWVpVmtVq1Pnz4nRAJD0TRNO9zZdSGEEEIIIYQQ4kQg3cuFEEIIIYQQQogOIkG3EEIIIYQQQgjRQSToFkIIIYQQQgghOogE3UIIIYQQQgghRAeRoFsIIYQQQgghhOggEnQLIYQQQgghhBAdRIJuIYQQQgghhBCig0jQLYQQQgghhBBCdBAJuoUQQgghhBBCiA4iQbcQQgghhBBCCNFBJOgWQgghBACFhYUkJCSQnZ0d0O0vvvhinnjiiY49KCGEEOIYp2iaph3pgxBCCCHE4XXbbbexe/duPvnkE8/Hbr/9dsrLy3nxxRcDeoz169czbtw4du3aRWRkZEcdqhBCCHFMk0y3EEIIcQJavnw5Q4cO9bxfVVXFyy+/zMyZMwN+jL59+9K1a1feeuutjjhEIYQQ4rggQbcQQghxArHb7VgsFn799Vfuv/9+FEVh5MiRfP3119hsNkaOHOm5rcvl4tFHH6V79+4EBQWRmJjIjBkzfB7v7LPP5r333jvMX4UQQghx7JCgWwghhDiBmM1mFi9eDMDatWvZv38/3377LQsXLmTIkCE+t50zZw7vvfceL7zwAlu2bGHu3LmMGzfO5zbDhw9n+fLl1NbWHravQQghhDiWmI/0AQghhBDi8FFVldzcXGJjYxkwYIDn47t37yYlJcXntt999x1nn302J598MgCdO3dm9OjRPrdJSUnBbrdz4MABOnfu3PFfgBBCCHGMkUy3EEIIcYJZs2aNT8ANUF1dTVBQkM/HzjnnHB577DEmT57MSy+9RHFxcaPHCg4OBvQ94UIIIYRoTIJuIYQQ4gSzdu3aRkF3XFxco6D6zjvvZNOmTUycOJGnnnqKbt26sWvXLp/bFBUVARAfH9+xBy2EEEIcoyToFkIIIU4w69atY+DAgT4fGzRoEBs3bmx026ysLP70pz+xatUqysvLG91m/fr1pKWlERcX15GHLIQQQhyzZE+3EEIIcYJxuVxs2bKF3NxcQkNDiYyMZPLkydx7770UFxcTHR3NP/7xD5KSkhg2bBiqqvL8888TGxvbaE/3woULOe20047QVyKEEEIc/STTLYQQQpxgHnnkEV577TVSU1N55JFHAOjXrx+DBw/mgw8+AKCmpoa///3vDB48mLFjx7Jz505+/PFHoqOjPY9TU1PDp59+yqxZs47I1yGEEEIcCxRN07QjfRBCCCGEOPK++uor7rrrLtavX4+qtrwu/+yzzzJ37ly+//77w3B0QgghxLFJysuFEEIIAcCZZ57Jtm3byMnJIT09vcXbWywW/v3vfx+GIxNCCCGOXZLpFkIIIYQQQgghOojs6RZCCCGEEEIIITqIBN1CCCGEEEIIIUQHkaBbCCGEEEIIIYToIBJ0CyGEEEIIIYQQHUSCbiGEEEIIIYQQooNI0C2EEEIIIYQQQnQQCbqFEEIIIYQQQogOIkG3EEIIIYQQQgjRQSToFkIIIYQQQgghOogE3UIIIYQQQgghRAf5fyWhnpV1VaWLAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real = modes_py[(2, 2)].real()\n", + "hp22_c_real = modes_c[(2, 2)].real()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"INSPIRAL-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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_py_real.sample_times,hp22_py_real,label='Python')\n", + "plt.plot(hp22_c_real.sample_times+0.019,hp22_c_real,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys([(2, 2), (2, -2)])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(modes_py.keys())\n", + "hp22 = modes_py[(2, 2)].real()\n", + "hc22 = modes_py[(2, 2)].imag()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"INSPIRAL-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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.sample_times,hp22,label=r\"Re [$h_{22}$]\")\n", + "plt.plot(hc22.sample_times,hc22,label=r\"Im [$h_{22}$]\")\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"$h$\")\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "173f050f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e8eed306b2162a2737384db1296a7a866b27195a Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 30 Apr 2026 12:34:19 +0530 Subject: [PATCH 11/36] Cleanup --- .gitignore | 15 + .ipynb_checkpoints/Untitled-checkpoint.ipynb | 6 - build/lib/esigmapy/__init__.py | 41 - build/lib/esigmapy/blend.py | 537 --- build/lib/esigmapy/generator.py | 1210 ------ build/lib/esigmapy/legacy.py | 125 - build/lib/esigmapy/python_codes/__init__.py | 1 - .../esigmapy/python_codes/esigma_go_terms.py | 3343 ----------------- .../python_codes/esigma_go_terms_draft.py | 1696 --------- .../python_codes/esigma_pn_inspiral.py | 2844 -------------- .../esigmapy/python_codes/esigma_pn_main.py | 501 --- .../esigmapy/python_codes/generator_python.py | 1211 ------ build/lib/esigmapy/surrogate/__init__.py | 4 - build/lib/esigmapy/surrogate/generator.py | 820 ---- build/lib/esigmapy/surrogate/surrogate.py | 556 --- build/lib/esigmapy/utils.py | 189 - esigmapy.egg-info/PKG-INFO | 174 - esigmapy.egg-info/SOURCES.txt | 23 - esigmapy.egg-info/dependency_links.txt | 1 - esigmapy.egg-info/entry_points.txt | 2 - esigmapy.egg-info/requires.txt | 7 - esigmapy.egg-info/top_level.txt | 1 - esigmapy/__pycache__/__init__.cpython-311.pyc | Bin 2853 -> 0 bytes esigmapy/__pycache__/blend.cpython-311.pyc | Bin 18716 -> 0 bytes .../__pycache__/generator.cpython-311.pyc | Bin 47601 -> 0 bytes esigmapy/__pycache__/legacy.cpython-311.pyc | Bin 7949 -> 0 bytes esigmapy/__pycache__/utils.cpython-311.pyc | Bin 7879 -> 0 bytes .../ESIGMA_tutorial-checkpoint.ipynb | 670 ---- .../Untitled-checkpoint.ipynb | 6 - .../esigma_python-checkpoint.ipynb | 359 -- 30 files changed, 15 insertions(+), 14327 deletions(-) create mode 100644 .gitignore delete mode 100644 .ipynb_checkpoints/Untitled-checkpoint.ipynb delete mode 100644 build/lib/esigmapy/__init__.py delete mode 100644 build/lib/esigmapy/blend.py delete mode 100644 build/lib/esigmapy/generator.py delete mode 100644 build/lib/esigmapy/legacy.py delete mode 100644 build/lib/esigmapy/python_codes/__init__.py delete mode 100644 build/lib/esigmapy/python_codes/esigma_go_terms.py delete mode 100644 build/lib/esigmapy/python_codes/esigma_go_terms_draft.py delete mode 100644 build/lib/esigmapy/python_codes/esigma_pn_inspiral.py delete mode 100644 build/lib/esigmapy/python_codes/esigma_pn_main.py delete mode 100644 build/lib/esigmapy/python_codes/generator_python.py delete mode 100644 build/lib/esigmapy/surrogate/__init__.py delete mode 100644 build/lib/esigmapy/surrogate/generator.py delete mode 100644 build/lib/esigmapy/surrogate/surrogate.py delete mode 100644 build/lib/esigmapy/utils.py delete mode 100644 esigmapy.egg-info/PKG-INFO delete mode 100644 esigmapy.egg-info/SOURCES.txt delete mode 100644 esigmapy.egg-info/dependency_links.txt delete mode 100644 esigmapy.egg-info/entry_points.txt delete mode 100644 esigmapy.egg-info/requires.txt delete mode 100644 esigmapy.egg-info/top_level.txt delete mode 100644 esigmapy/__pycache__/__init__.cpython-311.pyc delete mode 100644 esigmapy/__pycache__/blend.cpython-311.pyc delete mode 100644 esigmapy/__pycache__/generator.cpython-311.pyc delete mode 100644 esigmapy/__pycache__/legacy.cpython-311.pyc delete mode 100644 esigmapy/__pycache__/utils.cpython-311.pyc delete mode 100644 notebooks/.ipynb_checkpoints/ESIGMA_tutorial-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..966cc98 --- /dev/null +++ b/.gitignore @@ -0,0 +1,15 @@ +# Build and distribution folders +build/ +dist/ +*.egg-info/ + +# Python bytecode +__pycache__/ +*.pyc + +# Environments (if you use them locally) +venv/ +.env + +# ipynb folders +.ipynb_checkpoints/ diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb deleted file mode 100644 index 363fcab..0000000 --- a/.ipynb_checkpoints/Untitled-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/build/lib/esigmapy/__init__.py b/build/lib/esigmapy/__init__.py deleted file mode 100644 index 183475f..0000000 --- a/build/lib/esigmapy/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -from __future__ import absolute_import - -from . import blend, generator, legacy, utils -from .generator import * - - -def get_version_information(): - import os - - version_file = os.path.join( - os.path.dirname(os.path.dirname(__file__)), "esigmapy/.version" - ) - try: - with open(version_file, "r") as f: - return f.readline().rstrip() - except EnvironmentError: - print("No version information file '.version' found") - - -# pycbc wrapper -import inspect -_params = inspect.signature(get_imr_esigma_waveform).parameters.keys() -def pycbc_esigma(**params): - from pycbc.waveform.waveform import parse_mode_array - - # Pass all parameters the model suppports - pt = {p: params[p] for p in _params if p in params} - pt['f_ref'] = pt['f_lower'] if pt['f_ref'] == 0 else pt['f_ref'] - - gen_wav = get_imr_esigma_waveform - if 'skip_merger' in params: - gen_wav = get_inspiral_esigma_waveform - - modes = params.pop('mode_array', [(2,2), (2,-2)]) - modes = parse_mode_array({'mode_array':modes})['mode_array'] - - return gen_wav(pt.pop('mass1'), pt.pop('mass2'), - pt.pop('f_lower'), pt.pop('delta_t'), - **pt, modes_to_use=modes) - -__version__ = get_version_information() diff --git a/build/lib/esigmapy/blend.py b/build/lib/esigmapy/blend.py deleted file mode 100644 index d2be5ce..0000000 --- a/build/lib/esigmapy/blend.py +++ /dev/null @@ -1,537 +0,0 @@ -# Copyright (C) 2023 Kartikey Sharma, Prayush Kumar -# -"""Master function to hybridise any complex timeseries using the 'frequency' as a user input, -specifically to be used for gravitational waveform hybridisation, fine-tuned for a single mode. -""" - -import numpy as np -import scipy.optimize -from scipy.integrate import cumulative_trapezoid - - -def find_first_value_location_in_series(frq_timeseries, frq_desired): - if frq_desired < np.min(frq_timeseries): - raise Exception("Desired frequency out of bounds, lower than min frequency") - - if frq_desired > np.max(frq_timeseries): - raise Exception("Desired frequency out of bounds, higher than max frequency") - """ - We reverse the array and traverse it to find the location where the i_th value is more than - the desired value while the i+1_th value is less, hence locating the desired value somewhere - between those two points. We then choose the value closer to the value desired (among i and i+1) - and call it the location of the desired value. - """ - - for idx, f_value in enumerate(frq_timeseries): - if idx != len(frq_timeseries) - 1: - if ( - frq_timeseries[idx] <= frq_desired - and frq_timeseries[idx + 1] >= frq_desired - ): - fr1 = frq_timeseries[idx] - fr2 = frq_timeseries[idx + 1] - - if abs(frq_desired - fr1) <= abs(frq_desired - fr2): - final_idx = idx - else: - final_idx = idx + 1 - break - return final_idx - - -def find_last_value_location_in_series(frq_timeseries, frq_desired): - if frq_desired < np.min(frq_timeseries): - raise Exception( - f"""Desired value {frq_desired} out of bounds, lower than min value {np.min(frq_timeseries)}""" - ) - - if frq_desired > np.max(frq_timeseries): - raise Exception( - f"""Desired value {frq_desired} out of bounds, higher than max value {np.max(frq_timeseries)}""" - ) - """ - We reverse the array and traverse it to find the location where the i_th value is more than - the desired value while the i+1_th value is less, hence locating the desired value somewhere - between those two points. We then choose the value closer to the value desired (among i and i+1) - and call it the location of the desired value. - """ - - reversed_freq_timeseries = frq_timeseries[::-1] - final_idx = len(reversed_freq_timeseries) - 1 - - for idx, f_value in enumerate(reversed_freq_timeseries): - if idx != len(reversed_freq_timeseries) - 1: - if ( - reversed_freq_timeseries[idx] >= frq_desired - and reversed_freq_timeseries[idx + 1] <= frq_desired - ): - fr1 = reversed_freq_timeseries[idx] - fr2 = reversed_freq_timeseries[idx + 1] - - if abs(frq_desired - fr1) <= abs(frq_desired - fr2): - final_idx = idx - else: - final_idx = idx + 1 - break - return len(frq_timeseries) - 1 - final_idx - - -def mismatch_discrete(w1, w2, sample_indices_insp, sample_indices_mr): - w1_d = w1[sample_indices_insp] - w2_d = w2[sample_indices_mr] # can't give the same comb to w2 - w2sq = np.square(np.abs(w2_d)) - # w1sq = np.square(np.abs(w2_d)) # another normalising factor can be (w1sq + w2sq) / 2 - diff = np.abs(w1_d - w2_d) - diffsq = np.square(diff) - mm = 0.5 * (np.sum(diffsq) / np.sum(w2sq)) - return mm - - -def align_in_phase( - inspiral, - merger_ringdown, - sample_indices_insp, - sample_indices_mr, - t1_index_insp, - t2_index_insp, - t1_index_mr, - t2_index_mr, - m_mode=2, - align_merger_to_inspiral=True, -): - if len(inspiral) == 0: - raise IOError( - f"""You passed an inspiral waveform of zero length, to align - with merger-ringdown!""" - ) - if (t2_index_insp + 1 - t1_index_insp) < 1: - raise IOError( - f"""You have passed a very narrow window for the inspiral - waveform's hybridization. As per your input, the inspiral - waveform from index {t1_index_insp} to {t2_index_insp + 1} - should be used to attach merger-ringdown""" - ) - - # Function alignes the two waveforms using the phase, optimised over the attachment region - # m from l,m mode - def optfn_ph(phaseshift_correction): - if align_merger_to_inspiral: - phase_corrected_inspiral = inspiral - phase_corrected_merger_ringdown = merger_ringdown * np.exp( - 1j * m_mode * phaseshift_correction - ) - else: - phase_corrected_inspiral = inspiral * np.exp( - 1j * m_mode * phaseshift_correction - ) - phase_corrected_merger_ringdown = merger_ringdown - m_d = mismatch_discrete( - phase_corrected_inspiral[t1_index_insp : t2_index_insp + 1], - phase_corrected_merger_ringdown[t1_index_mr : t2_index_mr + 1], - sample_indices_insp, - sample_indices_mr, - ) - return m_d - - phase_optimizer = scipy.optimize.minimize(optfn_ph, 0) - phaseshift_required_for_alignment = phase_optimizer.x - - if align_merger_to_inspiral: - aligned = merger_ringdown * np.exp( - 1j * m_mode * phaseshift_required_for_alignment - ) - return (inspiral, aligned, phaseshift_required_for_alignment) - else: - aligned = inspiral * np.exp(1j * m_mode * phaseshift_required_for_alignment) - return (aligned, merger_ringdown, phaseshift_required_for_alignment) - - -def blend_series(x1, x2, t1_index_insp, t2_index_insp, t1_index_mr, t2_index_mr): - assert ( - t1_index_mr - t2_index_mr == t1_index_insp - t2_index_insp - ), "Inconsistent indices passed to blending function" - - # blending fn is an array - blfn_var = np.arange(t1_index_insp, t2_index_insp) - tau = np.square( - ( - np.sin( - (np.pi / 2) - * (blfn_var - t1_index_insp) - / (t2_index_insp - t1_index_insp) - ) - ) - ) - - x_hyb = (1 - tau) * x1[t1_index_insp:t2_index_insp] + tau * x2[ - t1_index_mr:t2_index_mr - ] - return x_hyb - - -def compute_amplitude(waveform): - amplitude = np.abs(waveform) - return amplitude - - -def compute_phase(waveform): - phase = np.unwrap(-np.angle(waveform)) - return phase - - -def compute_frequency(phase, delta_t): - frequency = np.gradient(phase, delta_t) / (2 * np.pi) - return frequency - - -def blend_modes( - inspiral_modes, - merger_ringdown_modes, - inspiral_orbital_frequency, - frq_attach, - frq_width=10.0, - delta_t=1.0 / 4096, - no_sp=8, - modes_to_blend=[(2, 2), (3, 3), (4, 4)], - mode_to_align_by=(2, 2), - blend_using_avg_orbital_frequency=True, - blend_aligning_merger_to_inspiral=True, - include_conjugate_modes=True, - verbose=False, -): - """blend inspiral and merger-ringdown modes - - Inputs - ------ - inspiral_modes: dict - Dictionary indexed by (l, m) containing numpy-like arrays of - complex-valued mode timeseries. - merger_ringdown_modes: dict - Dictionary indexed by (l, m) containing numpy-like arrays of - complex-valued mode timeseries. - - frq_attach: float - Frequency (Hz) at which to align the inspiral and merger-ringdown modes. - frq_width: {10.0, float} - Frequency (Hz) window around the central attachment frequency over which - hybridization of modes is performed. - delta_t: {1/4096, float} - Sample rate for timeseries (Hz) - no_sp: {8, int} - - modes_to_blend: {[(2, 2), (3, 3), (4, 4)], list} - List of modes as tuples of (l, m) values to blend - mode_to_align_by: {(2, 2), tuple} - One specific mode (l, m) value that is to be treated as baseline for - time/phase alignment. We recommend using only the (2, 2) mode for this. - blend_using_avg_orbital_frequency: (False, bool) - Flag that enables use of orbit averaged orbital frequency for all - calculations of inspiral to merger-ringdown attachment - blend_aligning_merger_to_inspiral: (True, bool) - Flag that ensures any phase difference between inspiral and merger-rd - modes is removed by phase shifting the merger-rd portion. If set to - False, the inspiral portion is phase shifted instead - include_conjugate_modes: {True, bool} - When set to True, we also consider (l, -m) modes in addition to (l, m) ones. - verbose: {True, bool} - Set this to True to enable logging output. - """ - if frq_width <= 0: - raise IOError( - f"""You are trying to blend over a frequency window of - negative length (= {frq_width}Hz). Fix this.""" - ) - if blend_using_avg_orbital_frequency: - if len(inspiral_orbital_frequency) != len(inspiral_modes[mode_to_align_by]): - raise IOError( - f"""You asked for hybridization using orbital frequency, but - the orbital frequency array and inspiral modes array have - different lengths: {len(inspiral_orbital_frequency)}, {len(inspiral_modes[mode_to_align_by])}""" - ) - modes_not_aligned_by = modes_to_blend.copy() - if mode_to_align_by in modes_not_aligned_by: - modes_not_aligned_by.remove(mode_to_align_by) - if include_conjugate_modes: - for el, em in modes_to_blend.copy(): - if (el, -em) not in modes_to_blend: - modes_to_blend.append((el, -em)) - for el, em in modes_not_aligned_by.copy(): - if (el, -em) not in modes_not_aligned_by: - modes_not_aligned_by.append((el, -em)) - - # Input checks - for lm in modes_to_blend: - if lm not in inspiral_modes or lm not in merger_ringdown_modes: - raise IOError( - "We cannot blend {} mode as its missing in the input inspiral modes" - " ({}) or the merger ringdown modes ({})".format( - lm, lm in inspiral_modes, lm in merger_ringdown_modes - ) - ) - if verbose: - print("Hybridizing the following modes: {}".format(modes_to_blend)) - print("By aligning {} mode".format(mode_to_align_by)) - print( - "..and inheriting the phase/time shifts for alignment of {} modes".format( - modes_not_aligned_by - ) - ) - - # Get amplitude and phase for all modes - phase_insp = {} - frq_insp = {} - phase_mr = {} - frq_mr = {} - - for el, em in modes_to_blend: - phase_insp[(el, em)] = compute_phase(inspiral_modes[(el, em)]) - frq_insp[(el, em)] = compute_frequency(phase_insp[(el, em)], delta_t) - - phase_mr[(el, em)] = compute_phase(merger_ringdown_modes[(el, em)]) - frq_mr[(el, em)] = compute_frequency(phase_mr[(el, em)], delta_t) - - if verbose: - print( - f"INSPIRAL mode ({el}, {em}) goes from {np.min(frq_insp[(el, em)])}Hz to" - f" {np.max(frq_insp[(el, em)])}Hz" - ) - print( - f"MERGER mode ({el}, {em}) goes from {np.min(frq_mr[(el, em)])}Hz to" - f" {np.max(frq_mr[(el, em)])}Hz" - ) - - """ first we need to find the attachment region, based on the frequency """ - - """ - We search left to right in merger-ringdown to avoid frequency fluctuations - after the merger, and right to left in inspiral to avoid frequency degeneracy - caused by eccentricity - """ - el, em = mode_to_align_by - - t1_index_mr = find_first_value_location_in_series( - frq_mr[(el, em)], frq_attach - frq_width / 2 - ) - - t2_index_mr = find_first_value_location_in_series( - frq_mr[(el, em)], frq_attach + frq_width / 2 - ) - """ - For eccentric inspiral, there will be multiple instances of the - same frequency. Pick the one having the highest index value (i.e. - the one at the rightmost occurance in time) - - """ - if blend_using_avg_orbital_frequency: - t2_index_insp = find_last_value_location_in_series( - inspiral_orbital_frequency, (frq_attach + frq_width / 2) / em - ) - if verbose > 1: - print( - f"""Hybridizing using orbital frequency. Frequency - {frq_attach + frq_width / 2}Hz found at {t2_index_insp}. - The same frequency would have been found at index - {find_last_value_location_in_series(frq_insp[(el, em)], - frq_attach + frq_width / 2)} of mode frequency evolution. - """ - ) - else: - t2_index_insp = find_last_value_location_in_series( - frq_insp[(el, em)], frq_attach + frq_width / 2 - ) - - # another way to define t2_index_mr is through number of points in the inspiral window - t1_index_insp = t2_index_insp - (t2_index_mr - t1_index_mr) - - if verbose > 4: - print( - f"""In the merger-ringdown waveform, the hybridization frequency window - [{frq_attach - frq_width / 2}, {frq_attach + frq_width / 2}] - was found at indices [{t1_index_mr}, {t2_index_mr}] - """ - ) - print( - f"""In the inspiral waveform, the same window is to be found at - indices [{t1_index_insp}, {t2_index_insp}.] - """ - ) - """ - Theoretically, we NEED a timeshift to align the waveforms in frequency. - Instead of shifting one of the two waveforms for alignment, we are defining - the time such that the frequencies are pre-aligned to the best of the - discrete interval errors. That is: - deltaT (timeshift) = t1_index_insp - t1_index_mr - The mathematical way is to optimise the difference in frequencies over the matching - region and using that to determine deltaT, hence arriving at t1_index_mr. - """ - - sample_indices_insp = ( - np.linspace(t1_index_insp, t2_index_insp, no_sp).astype(int) - t1_index_insp - ) - sample_indices_mr = ( - sample_indices_insp # since the attachment region in both has the same length - ) - """ alignment using corrective phase addition """ - - inspiral_modes_aligned = {} - amp_insp_aligned = {} - phase_insp_aligned = {} - frq_insp_aligned = {} - - merger_ringdown_modes_aligned = {} - amp_mr_aligned = {} - phase_mr_aligned = {} - frq_mr_aligned = {} - - ( - inspiral_modes_aligned[(el, em)], - merger_ringdown_modes_aligned[(el, em)], - phase_correction, - ) = align_in_phase( - inspiral_modes[(el, em)], - merger_ringdown_modes[(el, em)], - sample_indices_insp, - sample_indices_mr, - t1_index_insp, - t2_index_insp, - t1_index_mr, - t2_index_mr, - m_mode=em, - align_merger_to_inspiral=blend_aligning_merger_to_inspiral, - ) - - amp_insp_aligned[(el, em)] = compute_amplitude(inspiral_modes_aligned[(el, em)]) - phase_insp_aligned[(el, em)] = compute_phase(inspiral_modes_aligned[(el, em)]) - - amp_mr_aligned[(el, em)] = compute_amplitude( - merger_ringdown_modes_aligned[(el, em)] - ) - phase_mr_aligned[(el, em)] = compute_phase(merger_ringdown_modes_aligned[(el, em)]) - - for el, em in modes_not_aligned_by: - if blend_aligning_merger_to_inspiral: - inspiral_modes_aligned[(el, em)] = inspiral_modes[(el, em)] - merger_ringdown_modes_aligned[(el, em)] = merger_ringdown_modes[ - (el, em) - ] * np.exp(1j * em * phase_correction) - else: - inspiral_modes_aligned[(el, em)] = inspiral_modes[(el, em)] * np.exp( - 1j * em * phase_correction - ) - merger_ringdown_modes_aligned[(el, em)] = merger_ringdown_modes[(el, em)] - amp_insp_aligned[(el, em)] = compute_amplitude(inspiral_modes_aligned[(el, em)]) - phase_insp_aligned[(el, em)] = compute_phase(inspiral_modes_aligned[(el, em)]) - amp_mr_aligned[(el, em)] = compute_amplitude( - merger_ringdown_modes_aligned[(el, em)] - ) - phase_mr_aligned[(el, em)] = compute_phase( - merger_ringdown_modes_aligned[(el, em)] - ) - - """ - It would be same as frq_mr as the corrected phase factor will be canceled in the derivative, - defining frq_insp_aligned just for consistency - """ - frq_insp_aligned = frq_insp - frq_mr_aligned = frq_mr - - """ Performing attachment using the blending function """ - - amp_hyb_window = {} - amp_hyb_full = {} - frq_hyb_window = {} - phase_hyb_window = {} - phase_hyb_full = {} - hybrid_modes = {} - - for el, em in modes_to_blend: - amp_hyb_window[(el, em)] = blend_series( - amp_insp_aligned[(el, em)], - amp_mr_aligned[(el, em)], - t1_index_insp, - t2_index_insp, - t1_index_mr, - t2_index_mr, - ) - frq_hyb_window[(el, em)] = blend_series( - frq_insp_aligned[(el, em)], - frq_mr_aligned[(el, em)], - t1_index_insp, - t2_index_insp, - t1_index_mr, - t2_index_mr, - ) - """ Integrating frq_hyb to obtain phase_hyb and removing discontinuities, - compiling amp_hyb and phase_hyb to obtain the hybrid waveform. """ - - phase_hyb_window[(el, em)] = (2 * np.pi) * cumulative_trapezoid( - frq_hyb_window[(el, em)], dx=delta_t, initial=0 - ) - - """ Right now the phase is integrated only inside the hybrid window, - need to add constants to preserve phase continuity and compile full IMR phase """ - - def remove_phase_discontinuity(phase_insp_, phase_hyb_window_, phase_mr_): - if blend_aligning_merger_to_inspiral: - delta1 = phase_insp_[t1_index_insp] - phase_hyb_window_[0] - phase_hyb_1 = np.append( - phase_insp_[:t1_index_insp], phase_hyb_window_ + delta1 - ) - delta2 = phase_hyb_1[t2_index_insp - 1] - phase_mr_[t2_index_mr - 1] - phase_hyb_2 = np.append( - phase_hyb_1[: t2_index_insp - 1], phase_mr_[t2_index_mr - 1 :] + delta2 - ) - else: - delta1 = phase_hyb_window_[0] - phase_insp_[t1_index_insp] - phase_hyb_1 = np.append( - phase_insp_[:t1_index_insp] + delta1, phase_hyb_window_ - ) - delta2 = phase_mr_[t2_index_mr - 1] - phase_hyb_1[t2_index_insp - 1] - phase_hyb_2 = np.append( - phase_hyb_1[: t2_index_insp - 1] + delta2, phase_mr_[t2_index_mr - 1 :] - ) - return phase_hyb_2 - - for el, em in modes_to_blend: - phase_hyb_full[(el, em)] = remove_phase_discontinuity( - phase_insp_aligned[(el, em)], - phase_hyb_window[(el, em)], - phase_mr_aligned[(el, em)], - ) - - amp_hyb_full[(el, em)] = np.append( - np.concatenate( - [amp_insp_aligned[(el, em)][:t1_index_insp], amp_hyb_window[(el, em)]] - )[: t2_index_insp - 1], - amp_mr_aligned[(el, em)][t2_index_mr - 1 :], - ) - - hybrid_modes[(el, em)] = amp_hyb_full[(el, em)] * np.exp( - -1j * phase_hyb_full[(el, em)] - ) - - return ( - hybrid_modes, - t1_index_insp, - t1_index_mr, - t2_index_insp, - t2_index_mr, - frq_insp, - frq_mr, - frq_insp_aligned, - frq_hyb_window, - inspiral_modes_aligned, - merger_ringdown_modes_aligned, - sample_indices_insp, - sample_indices_mr, - amp_insp_aligned, - amp_hyb_window, - amp_mr_aligned, - amp_hyb_full, - phase_insp, - phase_insp_aligned, - phase_hyb_window, - phase_mr_aligned, - phase_hyb_full, - phase_correction, - ) diff --git a/build/lib/esigmapy/generator.py b/build/lib/esigmapy/generator.py deleted file mode 100644 index 81e326e..0000000 --- a/build/lib/esigmapy/generator.py +++ /dev/null @@ -1,1210 +0,0 @@ -# Copyright (C) 2020 Prayush Kumar, Akash Maurya, Kaushik Paul -# -"""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 - -ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 -ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 - - -def eccentricity_at_extremum_frequency( - 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 = ls.SimInspiralENIGMADynamics( - mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, False - ) - t, x, e, l, phi, phidot, r, rdot = retval[:8] - t.data.data *= lal.MTSUN_SI - - if verbose: - print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") - - omega = pt.TimeSeries(phidot.data.data, delta_t=t.data.data[1] - t.data.data[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.data.data - time_at_sensitive_freq).argmin() - e0 = e.data.data[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.data.data, x.data.data**1.5, "--", label="x ** {3/2}") - ax1.plot(t.data.data, phidot.data.data, 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.data.data, e.data.data, 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( - mass1, - mass2, - spin1z, - spin2z, - e0, - l0, - f_lower, - sample_rate, - f_reference, - show_figures=False, - verbose=False, -): - """ """ - itime = time.perf_counter() - retval = ls.SimInspiralENIGMADynamics( - mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, False - ) - t, x, e, l, phi, phidot, r, rdot = retval[:8] - t.data.data *= 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.data.data - x_reference).argmin() - e0 = e.data.data[idx_e0] - - if show_figures: - import matplotlib.pyplot as plt - - fig, (ax) = plt.subplots(1, 1, figsize=(10, 5), sharex=True) - ax.plot(t.data.data, x.data.data**1.5, "--", label="x ** {3/2}") - ax.plot(t.data.data, phidot.data.data, lw=0.5, label="omega") - ax.axhline(x_reference**1.5, color="c", lw=1) - ax.axvline(t.data.data[idx_e0], color="r", lw=1) - ax.set_xlabel("Time (s)") - ax.set_ylabel("Orbital angular velocity") - - ax2 = plt.twinx(ax) - ax2.plot(t.data.data, e.data.data, 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( - 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.data.data[-1] - mean_anomaly = l.data.data[-1] - f_start = f_lower - elif f_ref < f_lower: - itime = time.perf_counter() - f_start = f_ref - - retval = ls.SimInspiralENIGMADynamics( - mass1, - mass2, - spin1z, - spin2z, - eccentricity, - f_start, - mean_anomaly, - 1e-12, - 1 / delta_t, - False, - ) - - if f_ref < f_lower: - ref_idx = np.searchsorted( - (retval[1].data.data ** 1.5) / ((mass1 + mass2) * lal.MTSUN_SI * np.pi), - f_lower, - ) - new_len = len(retval[0].data.data) - ref_idx - for ii in range(8): - lal.ResizeREAL8TimeSeries(retval[ii], ref_idx, new_len) - - t, x, e, l, phi, phidot, r, rdot = retval[:8] - t.data.data *= ( - 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)] = ls.SimInspiralENIGMAModeFromDynamics( - el, - em, - t.data, - x.data, - phi.data, - phidot.data, - r.data, - rdot.data, - mass1, - mass2, - spin1z, - spin2z, - distance, - ) - - if return_pycbc_timeseries: - modes = { - k: pt.TimeSeries( - modes[k].data.data, - delta_t=delta_t, - epoch=-delta_t * (len(modes[k].data.data) - 1), - ) - for k in modes - } - else: - modes = {k: np.asarray(modes[k].data.data) 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}.data.data, delta_t=delta_t, epoch=-delta_t * len({name}.data.data))" - ) - return orbital_var_dict, modes - - for name in return_orbital_params: - exec(f"orbital_var_dict['{name}'] = {name}.data.data") - return (t.data.data - t.data.data[-1]), orbital_var_dict, modes - - if return_pycbc_timeseries: - return modes - return (t.data.data - t.data.data[-1]), modes - - -def get_inspiral_esigma_waveform( - 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( - 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_window_start(freq_, delta_t_, delta_phi_, direction="forward"): - """Integrates frequency backward/forward from the start/end until - `delta_phi_` radians of orbital phase has elapsed. - - Args: - freq_ (numpy.array): Frequency time series, uniformly sampled - delta_t_ (float64): Step size in time - delta_phi_ (float64): Phase shift in radians to integrate across - direction (str, optional): Direction of integration in time. - Defaults to "forward". - - Returns: - int: Index in frequency array where `delta_phi` is reached - """ - from scipy import integrate - - if direction == "backward": - for idx in range(len(freq_) - 2, 0, -1): - # this could be optimized to not repeat integration - if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): - return idx - elif direction == "forward": - for idx in range(1, len(freq_)): - # this could be optimized to not repeat integration - if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( - delta_phi_ - ): - return idx - - -def _get_transition_frequency_window( - orbital_phase, - orbital_freq, - delta_t, - f_mr_transition, # orbital frequency - num_hyb_orbits, - keep_f_mr_transition_at_center, - blend_using_avg_orbital_frequency, - failsafe, - verbose=False, -): - """ - This function first finds where the `orbital_freq` crosses the transition - frequency `f_mr_transition`, and finds the point in time that is - `num_hyb_orbits` orbits before that time. This marks the start of the - hybridization window, and the orbital frequency at that time is returned - - Parameters: - ----------- - orbital_phase -- Orbital phase evolution - orbital_freq -- Orbital frequency evolution - delta_t -- Waveform's time grid-spacing (s) - f_mr_transition -- Inspiral to merger transition frequency - (Hz). This should be the same (mode/orbital) - frequency as passed for `orbital_freq`. - num_hyb_orbits -- number of orbital cycles to blend over. - 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). - blend_using_avg_orbital_frequency -- If True, the orbit averaged - frequency during the inspiral is used to - blend modes, instead of the modes' - frequency. - failsafe -- If True, we make reasonable choices for the - user, if the inputs to this method lead - into exceptions. - verbose -- Verbosity flag - - Returns: - -------- - f_window_mr_transition -- Hybridization frequency window (in Hz). - """ - transition_idx = ( - len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) - ) # index at which orbital_freq becomes just smaller than transition orbital - # frequency, towards the end of waveform - - if verbose > 4: - print( - f"""Transition frequency found at index {transition_idx} - in the orbital frequency array of length {len(orbital_freq)}""" - ) - - if blend_using_avg_orbital_frequency: - # We integrate `orbital_freq` to obtain `orbital_phase` - from scipy import integrate - - orbital_phase = integrate.cumulative_trapezoid( - orbital_freq, dx=delta_t, initial=0 - ) - if len(orbital_phase) != len(orbital_freq): - raise RuntimeError( - f"""Something went wrong while integrating orbital frequency. -They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" - ) - - if keep_f_mr_transition_at_center: - # Orbital cycle based hybridization that keeps f_mr_transition at - # the hybridization frequency window's midpoint - if blend_using_avg_orbital_frequency: - window_start_idx = _get_window_start( - orbital_freq[: transition_idx + 1], - delta_t, - num_hyb_orbits * np.pi, - direction="backward", - ) - window_end_idx = transition_idx + _get_window_start( - orbital_freq[transition_idx:], - delta_t, - num_hyb_orbits * np.pi, - direction="forward", - ) - # FAILSAFE mode behavior - if window_start_idx is None: - if failsafe: - window_start_idx = 0 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform before the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or increase the -inspiral-to-merger transition frequency. `window_start_idx` is None.""" - ) - if window_end_idx is None: - if failsafe: - window_end_idx = len(orbital_freq) - 1 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform after the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or decrease the -inspiral-to-merger transition frequency. `window_end_idx` is None.""" - ) - else: - # phase is always a monotonically increasing function, - # hence using the more efficient np.searchsorted method - window_start_idx = -1 + np.searchsorted( - orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi - ) - window_end_idx = np.searchsorted( - orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi - ) - f_window_mr_transition = 2 * np.min( - [ - orbital_freq[window_end_idx] - f_mr_transition, - f_mr_transition - orbital_freq[window_start_idx], - ] - ) - elif not keep_f_mr_transition_at_center: - # Orbital cycle based hybridization that keeps f_mr_transition at - # the hybridization frequency window's right end - if blend_using_avg_orbital_frequency: - window_start_idx = _get_window_start( - orbital_freq[: transition_idx + 1], - delta_t, - 2 * num_hyb_orbits * np.pi, - direction="backward", - ) - if window_start_idx is None: - if failsafe: - window_start_idx = 0 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform before the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or increase the -inspiral-to-merger transition frequency. `window_start_idx` is None.""" - ) - else: - # Orbital cycle based hybridization that keeps f_mr_transition - # at the hybridization frequency window's end - window_start_idx = ( - np.searchsorted( - orbital_phase, - orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, - ) - - 1 - ) - f_window_mr_transition = ( - orbital_freq[transition_idx] - orbital_freq[window_start_idx] - ) - - if verbose > 4: - print( - f"""The transition is to happen in a window from - frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, - between indices: [{window_start_idx}, {transition_idx}]""" - ) - else: - raise RuntimeError( - """We should never reach here. Did you set a non-bool value for the - flag `keep_f_mr_transition_at_center`?""" - ) - - if verbose > 4: - print(f"""f_window_mr_transition = {f_window_mr_transition}""") - - return f_window_mr_transition - - -def get_imr_esigma_modes( - 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( - 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. Its 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( - 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( - 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/build/lib/esigmapy/legacy.py b/build/lib/esigmapy/legacy.py deleted file mode 100644 index c69e167..0000000 --- a/build/lib/esigmapy/legacy.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (C) 2023 Prayush Kumar -# -"""Legacy code to calibrate transition/attachment of inspiral and merger-ringdown -""" - - -class FitMOmegaIMRAttachmentNonSpinning: - called_once = False - - def __init__(self): - self.called_once = False - return - - @classmethod - def fit_quadratic_poly(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_quadratic_poly") - cls.called_once = True - assert len(coeffs) == 2, "{} coeffs passed!".format(len(coeffs)) - a1, a2 = coeffs - return (1.0 / 6**1.5) * (1.0 + a1 * eta + a2 * eta**2) - - @classmethod - def fit_cubic_poly(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_cubic_poly") - cls.called_once = True - assert len(coeffs) == 3, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3 = coeffs - return (1.0 / 6**1.5) * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) - - @classmethod - def fit_ratio_poly_44(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_poly_44") - cls.called_once = True - assert len(coeffs) == 6, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3, b1, b2, b3 = coeffs - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) - / (1.0 + b1 * eta + b2 * eta**2 + b3 * eta**3) - ) - - @classmethod - def fit_ratio_sqrt_poly_44(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_sqrt_poly_44") - cls.called_once = True - assert len(coeffs) == 6, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3, b1, b2, b3 = coeffs - s_eta = eta**0.5 - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * s_eta + a2 * s_eta**2 + a3 * s_eta**3) - / (1.0 + b1 * s_eta + b2 * s_eta**2 + b3 * s_eta**3) - ) - - @classmethod - def fit_ratio_sqrt_hyb1_poly_44(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_sqrt_hyb1_poly_44") - cls.called_once = True - assert len(coeffs) == 6, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3, b1, b2, b3 = coeffs - s_eta = eta**0.5 - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) - / (1.0 + b1 * eta + b2 * eta**2 + b3 * eta**3) - ) - - @classmethod - def fit_ratio_poly_43(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_poly_43") - cls.called_once = True - assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3, b1, b2 = coeffs - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * eta + a2 * eta**2 + a3 * eta**3) - / (1.0 + b1 * eta + b2 * eta**2) - ) - - @classmethod - def fit_ratio_sqrt_poly_43(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_sqrt_poly_43") - cls.called_once = True - assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3, b1, b2 = coeffs - s_eta = eta**0.5 - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * s_eta + a2 * s_eta**2 + a3 * s_eta**3) - / (1.0 + b1 * s_eta + b2 * s_eta**2) - ) - - @classmethod - def fit_ratio_sqrt_hyb1_poly_43(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_sqrt_hyb1_poly_43") - cls.called_once = True - assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) - a1, a2, a3, b1, b2 = coeffs - s_eta = eta**0.5 - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * eta * s_eta + a2 * eta**2 * s_eta + a3 * eta**3 * s_eta) - / (1.0 + b1 * eta + b2 * eta**2) - ) - - @classmethod - def fit_ratio_poly_34(cls, eta, coeffs): - if not cls.called_once: - logging.info("Using fit_ratio_poly_34") - cls.called_once = True - assert len(coeffs) == 5, "{} coeffs passed!".format(len(coeffs)) - a1, a2, b1, b2, b3 = coeffs - return ( - (1.0 / 6**1.5) - * (1.0 + a1 * eta + a2 * eta**2) - / (1.0 + b1 * eta + b2 * eta**2 + b3 * eta**3) - ) diff --git a/build/lib/esigmapy/python_codes/__init__.py b/build/lib/esigmapy/python_codes/__init__.py deleted file mode 100644 index 203562b..0000000 --- a/build/lib/esigmapy/python_codes/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# __init__.py \ No newline at end of file diff --git a/build/lib/esigmapy/python_codes/esigma_go_terms.py b/build/lib/esigmapy/python_codes/esigma_go_terms.py deleted file mode 100644 index e0057f2..0000000 --- a/build/lib/esigmapy/python_codes/esigma_go_terms.py +++ /dev/null @@ -1,3343 +0,0 @@ -""" -esigma_go_terms.py -==================== -Python translation of ESIGMA_GOTerms.c -""" - -import numpy as np -from dataclasses import dataclass - -# Mapping M_PI2 to pi^2 (standard in PN GW theory for this coefficient) -M_PI = np.pi -M_PI2 = M_PI ** 2 - -def Complex(real: float, imag: float) -> complex: - """Helper function to mimic the C-style Complex constructor.""" - return complex(real, imag) -def rect(r: float, phi: float) -> complex: - """Convert polar coordinates to rectangular form.""" - return r * np.exp(1j * phi) - -@dataclass -class KeplerVars: - # Basic orbital elements - pn_order: int - eta: float - Mtot: float - x: float - e: float - l: float - r: float - rDOT: float - PhiDOT: float - S1z: float - S2z: float - - # Powers of x - x2: float = 0.0; x2p5: float = 0.0; x3: float = 0.0; x3p5: float = 0.0 - x4: float = 0.0; x4p5: float = 0.0; x5: float = 0.0; x6: float = 0.0 - x7: float = 0.0; x8: float = 0.0; x9: float = 0.0 - - # Powers of e - e2: float = 0.0; e3: float = 0.0; e4: float = 0.0; e5: float = 0.0 - e6: float = 0.0; e7: float = 0.0; e8: float = 0.0; e9: float = 0.0 - - # Powers of r - r2: float = 0.0; r3: float = 0.0; r4: float = 0.0; r5: float = 0.0 - r6: float = 0.0; r7: float = 0.0; r8: float = 0.0; r9: float = 0.0 - - # Powers of rDOT - rDOT2: float = 0.0; rDOT3: float = 0.0; rDOT4: float = 0.0; rDOT5: float = 0.0 - rDOT6: float = 0.0; rDOT7: float = 0.0; rDOT8: float = 0.0; rDOT9: float = 0.0 - - # Powers of PhiDOT - PhiDOT2: float = 0.0; PhiDOT3: float = 0.0; PhiDOT4: float = 0.0; PhiDOT5: float = 0.0 - PhiDOT6: float = 0.0; PhiDOT7: float = 0.0; PhiDOT8: float = 0.0; PhiDOT9: float = 0.0 - - # Powers of S1z - S1z2: float = 0.0; S1z3: float = 0.0; S1z4: float = 0.0; S1z5: float = 0.0 - S1z6: float = 0.0; S1z7: float = 0.0; S1z8: float = 0.0; S1z9: float = 0.0 - S1z10: float = 0.0; S1z11: float = 0.0; S1z12: float = 0.0; S1z13: float = 0.0 - S1z14: float = 0.0; S1z15: float = 0.0; S1z16: float = 0.0 - - # Powers of S2z - S2z2: float = 0.0; S2z3: float = 0.0; S2z4: float = 0.0 - S2z5: float = 0.0; S2z6: float = 0.0 - - # Powers of eta - eta2: float = 0.0; eta3: float = 0.0; eta4: float = 0.0 - eta5: float = 0.0; eta6: float = 0.0 - - # Powers of Mtot - Mtot2: float = 0.0; Mtot3: float = 0.0; Mtot4: float = 0.0 - Mtot5: float = 0.0; Mtot6: float = 0.0 - - def compute_powers(self): - """Compute all power fields from the base values.""" - self.x2 = self.x**2; self.x2p5 = self.x**2.5; self.x3 = self.x**3 - self.x3p5 = self.x**3.5; self.x4 = self.x**4; self.x4p5 = self.x**4.5 - self.x5 = self.x**5; self.x6 = self.x**6; self.x7 = self.x**7 - self.x8 = self.x**8; self.x9 = self.x**9 - - self.e2 = self.e**2; self.e3 = self.e**3; self.e4 = self.e**4; self.e5 = self.e**5 - self.e6 = self.e**6; self.e7 = self.e**7; self.e8 = self.e**8; self.e9 = self.e**9 - - self.r2 = self.r**2; self.r3 = self.r**3; self.r4 = self.r**4; self.r5 = self.r**5 - self.r6 = self.r**6; self.r7 = self.r**7; self.r8 = self.r**8; self.r9 = self.r**9 - - self.rDOT2 = self.rDOT**2; self.rDOT3 = self.rDOT**3; self.rDOT4 = self.rDOT**4 - self.rDOT5 = self.rDOT**5; self.rDOT6 = self.rDOT**6; self.rDOT7 = self.rDOT**7 - self.rDOT8 = self.rDOT**8; self.rDOT9 = self.rDOT**9 - - self.PhiDOT2 = self.PhiDOT**2; self.PhiDOT3 = self.PhiDOT**3; self.PhiDOT4 = self.PhiDOT**4 - self.PhiDOT5 = self.PhiDOT**5; self.PhiDOT6 = self.PhiDOT**6; self.PhiDOT7 = self.PhiDOT**7 - self.PhiDOT8 = self.PhiDOT**8; self.PhiDOT9 = self.PhiDOT**9 - - self.S1z2 = self.S1z**2; self.S1z3 = self.S1z**3; self.S1z4 = self.S1z**4 - self.S1z5 = self.S1z**5; self.S1z6 = self.S1z**6; self.S1z7 = self.S1z**7 - self.S1z8 = self.S1z**8; self.S1z9 = self.S1z**9; self.S1z10 = self.S1z**10 - self.S1z11 = self.S1z**11; self.S1z12 = self.S1z**12; self.S1z13 = self.S1z**13 - self.S1z14 = self.S1z**14; self.S1z15 = self.S1z**15; self.S1z16 = self.S1z**16 - - self.S2z2 = self.S2z**2; self.S2z3 = self.S2z**3; self.S2z4 = self.S2z**4 - self.S2z5 = self.S2z**5; self.S2z6 = self.S2z**6 - - self.eta2 = self.eta**2; self.eta3 = self.eta**3; self.eta4 = self.eta**4 - self.eta5 = self.eta**5; self.eta6 = self.eta**6 - - self.Mtot2 = self.Mtot**2; self.Mtot3 = self.Mtot**3; self.Mtot4 = self.Mtot**4 - self.Mtot5 = self.Mtot**5; self.Mtot6 = self.Mtot**6 - -# All the hGO_l_m functions contain the 3PN non-spinning general orbit (GO) terms -# from Mishra et al. arXiv:1501.07096 and 3.5PN quasi-circular spinning -# corrections as given in Henry et al, arXiv:2209.00374v2 - -# The (2,2) mode has newly computed 4PN non-spinning quasi-circular piece from -# arXiv:2304.11185 - -# H22 - -def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, - PhiDOT: float, vpnorder: int, S1z: float, S2z: float, - x: float, params: KeplerVars) -> complex: - - # Note: In Python, `params` should be an object (like a dataclass or SimpleNamespace) - # so that attributes can be accessed via dot notation (e.g., params.PhiDOT2). - - # 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 * Nu) - - 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 - - - - if vpnorder == 0: - return (mass / r + params.PhiDOT2 * params.r2 + - Complex(0, 2) * PhiDOT * r * rDOT - params.rDOT2) - - elif vpnorder == 2: - return ((21 * params.Mtot2 * (-10 + Nu) - - 27 * (-1 + 3 * Nu) * params.r2 * combination_b * combination_a3 + - mass * r * - ((11 + 156 * Nu) * params.PhiDOT2 * params.r2 + - Complex(0, 10) * (5 + 27 * Nu) * PhiDOT * r * rDOT - - 3 * (15 + 32 * Nu) * params.rDOT2)) / - (42. * params.r2)) - - elif vpnorder == 3: - return ((params.Mtot2 * (Complex(0, -1) * rDOT * - ((3 + 3 * delta - 8 * Nu) * S1z + - (3 - 3 * delta - 8 * Nu) * S2z) + - PhiDOT * r * - ((-3 - 3 * delta + 5 * Nu) * S1z + - (-3 + 3 * delta + 5 * Nu) * S2z))) / - (3. * params.r2)) - # (<--This is the general orbit term) - # (This is the quasi-circular limit of the general orbit term-->) - # - ((-4 * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x2p5) / 3.) + - # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - Nu * (S1z + S2z)) * params.x2p5) / 3.) - # (<--This is Quentins quasi-circular term) - - elif vpnorder == 4: - return ((6 * params.Mtot3 * (3028 + 1267 * Nu + 158 * params.eta2) + - 9 * (83 - 589 * Nu + 1111 * params.eta2) * params.r3 * - combination_b2 * combination_a4 + - params.Mtot2 * r * - ((-11891 - 36575 * Nu + 13133 * params.eta2) * params.PhiDOT2 * - params.r2 + - Complex(0, 8) * (-773 - 3767 * Nu + 2852 * params.eta2) * - PhiDOT * r * rDOT - - 6 * (-619 + 2789 * Nu + 934 * params.eta2) * params.rDOT2) - - 3 * mass * params.r2 * - (2 * (-835 - 19 * Nu + 2995 * params.eta2) * params.PhiDOT4 * - params.r4 + - Complex(0, 6) * (-433 - 721 * Nu + 1703 * params.eta2) * - params.PhiDOT3 * params.r3 * rDOT + - 6 * (-33 + 1014 * Nu + 232 * params.eta2) * params.PhiDOT2 * - params.r2 * params.rDOT2 + - Complex(0, 4) * (-863 + 1462 * Nu + 2954 * params.eta2) * - PhiDOT * r * params.rDOT3 - - 3 * (-557 + 664 * Nu + 1712 * params.eta2) * params.rDOT4)) / - (1512. * params.r3) + - (3 * params.Mtot3 * - (S1z * (4 * Nu * S2z + (1 + delta - 2 * Nu) * S1z * kappa1) - - (-1 + delta + 2 * Nu) * params.S2z2 * kappa2)) / - (4. * params.r3)) - # This is where circular limit terms added and subtracted - # - ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + - # ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) - - elif vpnorder == 5: - return ((params.Mtot2 * Nu * - (2 * mass * (Complex(0, -702) * PhiDOT * r + rDOT) + - 3 * r * - (Complex(0, -316) * params.PhiDOT3 * params.r3 - - 847 * params.PhiDOT2 * params.r2 * rDOT + - Complex(0, 184) * PhiDOT * r * params.rDOT2 - - 122 * params.rDOT3))) / - (105. * params.r3) - # Henry et al. QC spin terms - # + ((2*(56*delta*Nu*(-S1z + S2z) + 101*Nu*(S1z + S2z) + 132*params.eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) - + - # Henry et al. ecc spin terms - ((params.Mtot2 * - (mass * - ((238 + delta * (238 - 141 * Nu) + Nu * (-181 + 474 * Nu)) * - PhiDOT * r * S1z + - Complex(0, 8) * - (55 + delta * (55 - 19 * Nu) + - 2 * Nu * (-50 + 43 * Nu)) * - rDOT * S1z + - (238 + delta * (-238 + 141 * Nu) + Nu * (-181 + 474 * Nu)) * - PhiDOT * r * S2z + - Complex(0, 8) * - (55 + delta * (-55 + 19 * Nu) + - 2 * Nu * (-50 + 43 * Nu)) * - rDOT * S2z) - - r * (PhiDOT * r * params.rDOT2 * - (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * Nu + - 188 * params.eta2) * - S1z) + - (18 * (-1 + delta) + 5 * (63 + 55 * delta) * Nu - - 188 * params.eta2) * - S2z) - - Complex(0, 2) * params.rDOT3 * - ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * Nu - - 4 * params.eta2) * - S1z + - (-27 + 27 * delta + 30 * Nu - 42 * delta * Nu - - 4 * params.eta2) * - S2z) + - Complex(0, 2) * params.PhiDOT2 * params.r2 * rDOT * - ((51 + 88 * Nu * (-3 + 5 * Nu) + - delta * (51 + 62 * Nu)) * - S1z + - (51 + 88 * Nu * (-3 + 5 * Nu) - - delta * (51 + 62 * Nu)) * - S2z) + - params.PhiDOT3 * params.r3 * - ((120 * (1 + delta) + (-483 + 83 * delta) * Nu + - 234 * params.eta2) * - S1z + - (120 + 3 * Nu * (-161 + 78 * Nu) - - delta * (120 + 83 * Nu)) * - S2z)))) / - (84. * params.r3))) - - elif vpnorder == 6: - return ((4 * params.Mtot4 * - (-8203424 + 2180250 * params.eta2 + 592600 * params.eta3 + - 15 * Nu * (-5503804 + 142065 * M_PI2)) - - 2700 * (-507 + 6101 * Nu - 25050 * params.eta2 + 34525 * params.eta3) * - params.r4 * combination_b3 * combination_a5 + - params.Mtot3 * r * - (params.PhiDOT2 * - (337510808 - 198882000 * params.eta2 + - 56294600 * params.eta3 + - Nu * (183074880 - 6392925 * M_PI2)) * - params.r2 + - Complex(0, 110) * PhiDOT * - (-5498800 - 785120 * params.eta2 + 909200 * params.eta3 + - 3 * Nu * (-1849216 + 38745 * M_PI2)) * - r * rDOT + - 2 * - (51172744 - 94929000 * params.eta2 - 5092400 * params.eta3 + - 45 * Nu * (2794864 + 142065 * M_PI2)) * - params.rDOT2) - - 20 * params.Mtot2 * params.r2 * - ((-986439 + 1873255 * Nu - 9961400 * params.eta2 + - 6704345 * params.eta3) * - params.PhiDOT4 * params.r4 + - Complex(0, 4) * - (-273687 - 978610 * Nu - 4599055 * params.eta2 + - 2783005 * params.eta3) * - params.PhiDOT3 * params.r3 * rDOT + - (-181719 + 19395325 * Nu + 8237980 * params.eta2 + - 2612735 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT2 + - Complex(0, 8) * - (-234312 + 1541140 * Nu + 1230325 * params.eta2 + - 1828625 * params.eta3) * - PhiDOT * r * params.rDOT3 - - 3 * - (-370268 + 1085140 * Nu + 2004715 * params.eta2 + - 1810425 * params.eta3) * - params.rDOT4) + - 300 * mass * params.r3 * - (4 * - (12203 - 36427 * Nu - 27334 * params.eta2 + - 149187 * params.eta3) * - params.PhiDOT6 * params.r6 + - Complex(0, 2) * - (44093 - 68279 * Nu - 295346 * params.eta2 + - 541693 * params.eta3) * - params.PhiDOT5 * params.r5 * rDOT + - 2 * - (27432 - 202474 * Nu + 247505 * params.eta2 + - 394771 * params.eta3) * - params.PhiDOT4 * params.r4 * params.rDOT2 + - Complex(0, 2) * - (97069 - 383990 * Nu - 8741 * params.eta2 + - 1264800 * params.eta3) * - params.PhiDOT3 * params.r3 * params.rDOT3 + - (-42811 + 53992 * Nu + 309136 * params.eta2 - - 470840 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT4 + - Complex(0, 2) * - (51699 - 252256 * Nu + 131150 * params.eta2 + - 681160 * params.eta3) * - PhiDOT * r * params.rDOT5 - - 3 * - (16743 - 75104 * Nu + 26920 * params.eta2 + - 207200 * params.eta3) * - params.rDOT6)) / - (3.3264e6 * params.r4) - # Henry et al. QC spin terms - # + (((4*(1 + delta)*(-7 + 9*kappa1) - 7*(9 + 17*delta)*Nu - 9*(15 + 7*delta)*kappa1*Nu + 12*(7 - 17*kappa1)*params.eta2)*params.S1z2 + 2*S1z*(Complex(0,-42)*(1 + delta - 2*Nu) - 84*(1 + delta - Nu)*M_PI + Nu*(-271 + 288*Nu)*S2z) + S2z*(12*(7 - 17*kappa2)*params.eta2*S2z + 4*(-1 + delta)*(Complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + Nu*(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 * - (params.Mtot3 * - (2 * mass * - (S1z * (Complex(0, 14) * (1 + delta - 2 * Nu) + - 42 * (1 + delta - 2 * Nu) * Nu * S1z + - kappa1 * - (438 + delta * (438 + 103 * Nu) + - Nu * (-773 + 108 * Nu)) * - S1z) + - 2 * - (Complex(0, -7) * (-1 + delta + 2 * Nu) + - (995 - 192 * Nu) * Nu * S1z) * - S2z - - (42 * Nu * (-1 + delta + 2 * Nu) + - kappa2 * (-438 + (773 - 108 * Nu) * Nu + - delta * (438 + 103 * Nu))) * - params.S2z2) + - r * (params.rDOT2 * - (Complex(0, 56) * (1 + delta - 2 * Nu) * S1z + - (-56 * (1 + delta - 2 * Nu) * Nu + - kappa1 * (291 * (1 + delta) - - 2 * (445 + 154 * delta) * Nu + - 24 * params.eta2)) * - params.S1z2 + - 4 * Nu * (-3 + 44 * Nu) * S1z * S2z + - S2z * (Complex(0, -56) * (-1 + delta + 2 * Nu) + - 56 * Nu * (-1 + delta + 2 * Nu) * S2z + - kappa2 * - (291 - 890 * Nu + 24 * params.eta2 + - delta * (-291 + 308 * Nu)) * - S2z)) + - params.PhiDOT2 * params.r2 * - (Complex(0, 196) * (1 + delta - 2 * Nu) * S1z + - (56 * Nu * (7 + 7 * delta + Nu) + - kappa1 * (-153 * (1 + delta) - - 2 * (62 + 215 * delta) * Nu + - 804 * params.eta2)) * - params.S1z2 + - 8 * (60 - 187 * Nu) * Nu * S1z * S2z + - S2z * (Complex(0, -196) * (-1 + delta + 2 * Nu) + - 56 * Nu * (7 - 7 * delta + Nu) * S2z + - kappa2 * - (-153 + 4 * Nu * (-31 + 201 * Nu) + - delta * (153 + 430 * Nu)) * - S2z)) + - 2 * PhiDOT * r * rDOT * - (Complex(0, -1) * - (117 * (1 + delta) * kappa1 - - 434 * (1 + delta) * Nu + - (-23 + 211 * delta) * kappa1 * Nu + - 2 * (14 - 195 * kappa1) * params.eta2) * - params.S1z2 + - 4 * S1z * - (35 * (1 + delta - 2 * Nu) + - Complex(0, 1) * (9 - 209 * Nu) * Nu * S2z) + - S2z * (-140 * (-1 + delta + 2 * Nu) + - Complex(0, 1) * - (-14 * Nu * (-31 + 31 * delta + 2 * Nu) + - kappa2 * (117 * (-1 + delta) + - (23 + 211 * delta) * Nu + - 390 * params.eta2)) * - S2z))))) / - params.r4)) - # + Henry et al. QC spinning hereditary terms - # (((-8 * M_PI * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x4) / 3.)) - - elif vpnorder == 7: - return ( - # Henry et al QC spin terms - # ((3318*params.eta3*(S1z + S2z) + Nu*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*params.S1z3 - 1008*params.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*params.S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*params.S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*params.eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*params.S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. - # + - # Henry et al. ecc+spin terms - ((params.Mtot2 * - (-3 * mass * r * - (Complex(0, -16) * params.rDOT3 * - (Complex(0, 12) * Nu * (-16703 + 4427 * Nu) + - 35 * Nu * - (4578 + Nu * (-4288 + 765 * Nu) + - delta * (3748 + 802 * Nu)) * - S1z + - 35 * - (delta * (942 - 2 * Nu * (1874 + 401 * Nu)) + - Nu * (4578 + Nu * (-4288 + 765 * Nu))) * - S2z - - 32970 * (S1z + delta * S1z + S2z)) + - 4 * PhiDOT * r * params.rDOT2 * - (-338520 * params.eta3 * (S1z + S2z) + - 48930 * (S1z + delta * S1z + S2z - delta * S2z) + - Nu * (Complex(0, 3420696) - - 35 * (14154 + 21167 * delta) * S1z - - 495390 * S2z + 740845 * delta * S2z) + - params.eta2 * (Complex(0, -612336) + - 245 * (3566 - 1565 * delta) * S1z + - 245 * (3566 + 1565 * delta) * S2z)) + - params.PhiDOT3 * params.r3 * - (2515380 * params.eta3 * (S1z + S2z) - - 5 * Nu * - (Complex(0, 1859936) + - 7 * (82329 + 37061 * delta) * S1z + - 7 * (82329 - 37061 * delta) * S2z) - - 128100 * (S1z + delta * S1z + S2z - delta * S2z) + - 4 * params.eta2 * - (Complex(0, 381348) + - 35 * (-18505 + 1777 * delta) * S1z - - 35 * (18505 + 1777 * delta) * S2z)) + - Complex(0, 8) * params.PhiDOT2 * params.r2 * rDOT * - (779100 * params.eta3 * (S1z + S2z) + - 5 * Nu * - (Complex(0, 828806) + - 7 * (4839 + 5971 * delta) * S1z + - 7 * (4839 - 5971 * delta) * S2z) - - 62475 * (S1z + delta * S1z + S2z - delta * S2z) + - params.eta2 * (Complex(0, -976002) + - 35 * (-29599 + 3109 * delta) * S1z - - 35 * (29599 + 3109 * delta) * S2z))) + - 3 * params.r2 * - (Complex(0, 4) * params.PhiDOT2 * params.r2 * params.rDOT3 * - (Complex(0, 2) * (65451 - 350563 * Nu) * Nu + - 105 * Nu * - (6020 + delta * (3114 + 411 * Nu) + - Nu * (-4513 + 9136 * Nu)) * - S1z + - 105 * - (-3 * delta * (-408 + Nu * (1038 + 137 * Nu)) + - Nu * (6020 + Nu * (-4513 + 9136 * Nu))) * - S2z - - 128520 * (S1z + delta * S1z + S2z)) + - PhiDOT * r * params.rDOT4 * - (Complex(0, -128) * Nu * (2487 + 18334 * Nu) - - 105 * Nu * - (8689 + 8 * Nu * (-687 + 1402 * Nu) + - delta * (2143 + 8212 * Nu)) * - S1z + - 105 * - (Nu * (-8689 + 8 * (687 - 1402 * Nu) * Nu) + - delta * (-1470 + Nu * (2143 + 8212 * Nu))) * - S2z + - 154350 * (S1z + delta * S1z + S2z)) - - Complex(0, 6) * params.rDOT5 * - (-11760 * params.eta3 * (S1z + S2z) + - 4 * params.eta2 * - (Complex(0, 57338) + - 35 * (569 + 301 * delta) * S1z + - 35 * (569 - 301 * delta) * S2z) - - 16 * Nu * - (Complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + - 35 * (72 - 71 * delta) * S2z) + - 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + - params.PhiDOT5 * params.r5 * - (2263380 * params.eta3 * (S1z + S2z) + - 3 * Nu * - (Complex(0, 653432) + - 35 * (13283 + 7839 * delta) * S1z + - 35 * (13283 - 7839 * delta) * S2z) - - 219240 * (S1z + delta * S1z + S2z - delta * S2z) + - 4 * params.eta2 * - (Complex(0, -291268) + - 105 * (-7669 + 165 * delta) * S1z - - 105 * (7669 + 165 * delta) * S2z)) + - Complex(0, 14) * params.PhiDOT4 * params.r4 * rDOT * - (385170 * params.eta3 * (S1z + S2z) + - 15 * Nu * - (Complex(0, 16914) + (8633 + 2267 * delta) * S1z + - 8633 * S2z - 2267 * delta * S2z) - - 18630 * (S1z + delta * S1z + S2z - delta * S2z) + - 2 * params.eta2 * - (Complex(0, -67904) + - 15 * (-13932 + 679 * delta) * S1z - - 15 * (13932 + 679 * delta) * S2z)) + - 6 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (-6720 * params.eta3 * (S1z + S2z) + - 5 * Nu * - (Complex(0, -241704) + - 7 * (4377 + 3083 * delta) * S1z + - 7 * (4377 - 3083 * delta) * S2z) - - 36960 * (S1z + delta * S1z + S2z - delta * S2z) + - params.eta2 * (Complex(0, -364384) + - 35 * (5059 - 4753 * delta) * S1z + - 35 * (5059 + 4753 * delta) * S2z))) + - 14 * params.Mtot2 * - (Complex(0, 30) * rDOT * - (15280 * params.eta3 * (S1z + S2z) - - 4 * params.eta2 * - (Complex(0, -111) + 3562 * S1z + 682 * delta * S1z + - 945 * kappa1 * params.S1z3 + - (3562 - 682 * delta + - 945 * (-2 + kappa1) * params.S1z2) * - S2z + - 945 * (-2 + kappa2) * S1z * params.S2z2 + - 945 * kappa2 * params.S2z3) + - Nu * (Complex(0, -27520) + - 378 * - (5 * (1 + delta) * kappa1 + - 3 * (3 + delta) * lambda1) * - params.S1z3 + - (29749 - 13605 * delta) * S2z - - 1512 * (1 + delta) * kappa1 * params.S1z2 * S2z - - 378 * - (5 * (-1 + delta) * kappa2 + - 3 * (-3 + delta) * lambda2) * - params.S2z3 + - S1z * (29749 + 13605 * delta + - 1512 * (-1 + delta) * kappa2 * - params.S2z2)) + - 2 * (-8009 * (1 + delta) * S1z - - 567 * (1 + delta) * lambda1 * params.S1z3 + - (-1 + delta) * S2z * - (8009 + 567 * lambda2 * params.S2z2))) + - PhiDOT * r * - (285840 * params.eta3 * (S1z + S2z) - - 30 * params.eta2 * - (Complex(0, 11504) + 1890 * kappa1 * params.S1z3 + - 23090 * S2z - 1823 * delta * S2z + - 1890 * (-2 + kappa1) * params.S1z2 * S2z + - 1890 * kappa2 * params.S2z3 + - S1z * (23090 + 1823 * delta + - 1890 * (-2 + kappa2) * params.S2z2)) + - 30 * (689 * (1 + delta) * S1z - - 1134 * (1 + delta) * lambda1 * params.S1z3 + - (-1 + delta) * S2z * - (-689 + 1134 * lambda2 * params.S2z2)) + - 2 * Nu * - (Complex(0, 415432) - 66840 * S2z + - 15 * (8 * (-557 + 1342 * delta) * S1z + - 189 * - (5 * (1 + delta) * kappa1 + - 6 * (3 + delta) * lambda1) * - params.S1z3 - - 10736 * delta * S2z - - 2457 * (1 + delta) * kappa1 * params.S1z2 * - S2z + - 2457 * (-1 + delta) * kappa2 * S1z * - params.S2z2 - - 189 * - (5 * (-1 + delta) * kappa2 + - 6 * (-3 + delta) * lambda2) * - params.S2z3)))))) / - (317520. * params.r4)) - # + Henry et al. QC spinning hereditary terms - # (2 * M_PI * (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x4p5) - ) - - else: - return complex(0, 0) - -# hQC_l_m() functions contain only the non-spinning hereditary terms at -# particular PN order. - -def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: KeplerVars) -> complex: - - EulerGamma = 0.5772156649015329 - x0 = 0.4375079683656479 - b0 = 2 * 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 * Nu) - - # keeping only the hereditary terms - # if vpnorder == 0: - # return (2 * x) - - # elif vpnorder == 2: - # return ((-5.095238095238095 + (55 * Nu) / 21.) * params.x2) - - if vpnorder == 3: - # return (4 * M_PI * params.x2p5) - return (complex(0, 0.6666666666666666) * params.x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) - - # elif vpnorder == 4: - # return ((-2.874338624338624 - (1069 * Nu) / 108. + (2047 * params.eta2) / 756.) * params.x3) - - elif vpnorder == 5: - # return ((complex(0,-48)*Nu - (214*M_PI)/21. + (68*Nu*M_PI)/21.)*params.x3p5) - # return ((2 * (-107 + 34 * Nu) * M_PI * params.x3p5) / 21.) # This is old implementation - return (complex(0, 0.015873015873015872) * (-107 + 34 * Nu) * params.x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) - - elif vpnorder == 6: - # return ((params.x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * Nu) * M_PI) - 13696 * np.log(16 * x))) / 1680.) # This is the old implementation. - - return (params.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) * Nu * S1z + complex(0, 5.333333333333333) * EulerGamma * Nu * S1z - - (8 * M_PI * S1z) / 3. + (8 * Nu * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - - complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * Nu * S2z + - complex(0, 5.333333333333333) * EulerGamma * Nu * S2z - (8 * M_PI * S2z) / 3. + (8 * Nu * M_PI * S2z) / 3. + - (912 * np.log(2)) / 35. - 64 * EulerGamma * np.log(2) + complex(0, 32) * M_PI * np.log(2) - - complex(0, 10.666666666666666) * S1z * np.log(2) + complex(0, 10.666666666666666) * Nu * S1z * np.log(2) - - complex(0, 10.666666666666666) * S2z * np.log(2) + complex(0, 10.666666666666666) * Nu * S2z * np.log(2) - - 64 * np.log(2) * np.log(2) + (88 * np.log(b0)) / 3. - 32 * EulerGamma * np.log(b0) + - complex(0, 16) * M_PI * np.log(b0) - complex(0, 5.333333333333333) * S1z * np.log(b0) + - complex(0, 5.333333333333333) * Nu * S1z * np.log(b0) - complex(0, 5.333333333333333) * S2z * np.log(b0) + - complex(0, 5.333333333333333) * Nu * S2z * np.log(b0) - 64 * np.log(2) * np.log(b0) - - 16 * np.log(b0) * np.log(b0) - (1712 * np.log(r0)) / 105. + (684 * np.log(x)) / 35. - - 48 * EulerGamma * np.log(x) + complex(0, 24) * M_PI * np.log(x) - complex(0, 8) * S1z * np.log(x) + - complex(0, 8) * Nu * S1z * np.log(x) - complex(0, 8) * S2z * np.log(x) + complex(0, 8) * Nu * S2z * np.log(x) - - 96 * np.log(2) * np.log(x) - 48 * np.log(b0) * np.log(x) - 36 * np.log(x) * np.log(x) - - complex(0, 0.4444444444444444) * delta * (S1z - S2z) * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + - 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x)))) - - elif vpnorder == 7: - # return (((-2173 - 4990 * Nu + 1120 * params.eta2) * M_PI * params.x4p5) / 378.) # This is the old implementation. - return (complex(0, 0.0004409171075837742) * params.x4p5 * (23903 - 26076 * EulerGamma + 57452 * Nu - - 59880 * EulerGamma * Nu - 18728 * params.eta2 + 13440 * EulerGamma * params.eta2 + - complex(0, 13038) * M_PI + complex(0, 29940) * Nu * M_PI - complex(0, 6720) * params.eta2 * M_PI - - 8316 * kappa1 * params.S1z2 - 8316 * delta * kappa1 * params.S1z2 + 9072 * EulerGamma * kappa1 * params.S1z2 + - 9072 * delta * EulerGamma * kappa1 * params.S1z2 + 16632 * kappa1 * Nu * params.S1z2 - - 18144 * EulerGamma * kappa1 * Nu * params.S1z2 - complex(0, 4536) * kappa1 * M_PI * params.S1z2 - - complex(0, 4536) * delta * kappa1 * M_PI * params.S1z2 + complex(0, 9072) * kappa1 * Nu * M_PI * params.S1z2 - - 33264 * Nu * S1z * S2z + 36288 * EulerGamma * Nu * S1z * S2z - complex(0, 18144) * Nu * M_PI * S1z * S2z - - 8316 * kappa2 * params.S2z2 + 8316 * delta * kappa2 * params.S2z2 + 9072 * EulerGamma * kappa2 * params.S2z2 - - 9072 * delta * EulerGamma * kappa2 * params.S2z2 + 16632 * kappa2 * Nu * params.S2z2 - - 18144 * EulerGamma * kappa2 * Nu * params.S2z2 - complex(0, 4536) * kappa2 * M_PI * params.S2z2 + - complex(0, 4536) * delta * kappa2 * M_PI * params.S2z2 + complex(0, 9072) * kappa2 * Nu * M_PI * params.S2z2 - - 52152 * np.log(2) - 119760 * Nu * np.log(2) + 26880 * params.eta2 * np.log(2) + - 18144 * kappa1 * params.S1z2 * np.log(2) + 18144 * delta * kappa1 * params.S1z2 * np.log(2) - - 36288 * kappa1 * Nu * params.S1z2 * np.log(2) + 72576 * Nu * S1z * S2z * np.log(2) + - 18144 * kappa2 * params.S2z2 * np.log(2) - 18144 * delta * kappa2 * params.S2z2 * np.log(2) - - 36288 * kappa2 * Nu * params.S2z2 * np.log(2) + 12 * (-2173 - 4990 * Nu + 1120 * params.eta2 + - 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - - 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(b0) + 18 * (-2173 - 4990 * Nu + - 1120 * params.eta2 + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - - 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(x))) - - # 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 * (params.x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * Nu) + - 12 * (628830397253 + 1854914893791 * Nu + 421984442880 * np.log(2)) + 415134720 * EulerGamma * (6099 - 40277 * Nu + complex(0, 70) * (107 - 13 * Nu) * M_PI + 280 * (-107 + 13 * Nu) * np.log(2)) + - 35 * (-28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) + - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2 - 955450349568 * Nu * np.log(2) + - 3321077760 * (-107 + 13 * Nu) * np.log(2) * np.log(2) - complex(0, 5930496) * M_PI * (6099 - 74773 * Nu + 280 * (-107 + 13 * Nu) * np.log(2))) - 5700491596800 * np.log(mass) + - 6966444925440 * np.log(x) + 69189120 * (420 * (-107 + 13 * Nu) * np.log(b0) * np.log(b0) + - 420 * (-107 + 13 * Nu) * np.log(mass) * np.log(mass) - 14 * np.log(mass) * (-11803 * Nu + - 60 * EulerGamma * (-107 + 13 * Nu) + complex(0, 30) * (107 - 13 * Nu) * M_PI + - 120 * (-107 + 13 * Nu) * np.log(2) + 90 * (-107 + 13 * Nu) * np.log(x)) + 14 * np.log(b0) * (5885 - 6420 * EulerGamma - 11803 * Nu + 780 * EulerGamma * Nu + complex(0, 3210) * M_PI - - complex(0, 390) * Nu * M_PI + 120 * (-107 + 13 * Nu) * np.log(2) + (6420 - 780 * Nu) * np.log(mass) + - 90 * (-107 + 13 * Nu) * np.log(x)) + 5 * np.log(x) * (-74149 * Nu + 252 * EulerGamma * (-107 + 13 * Nu) - - complex(0, 126) * (-107 + 13 * Nu) * M_PI + 504 * (-107 + 13 * Nu) * np.log(2) + - 189 * (-107 + 13 * Nu) * np.log(x)) + 84672 * Nu * np.log(x0))))) - - # return ((params.x5 * (276756480 * EulerGamma * (11449 + 19105 * Nu) - 12 * (846557506853 + 1008017482431 * Nu) + 35 * (28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) - complex(0, 3953664) * (11449 + 109657 * Nu) * M_PI - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2) + 138378240 * (11449 + 19105 * Nu) * np.log(16 * x))) / 7.62810048e10) - - else: - return complex(0, 0) - -def hl_2_m_2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, - PhiDOT: float, R: float, vpnorder: int, S1z: float, - S2z: float, x: float, params: KeplerVars) -> complex: - - if vpnorder < 0 or vpnorder > 8: - raise ValueError("Error in hl_2_m_2: Input PN order parameter should be between [0, 8].") - - else: - # Calculate the leading amplitude coefficient - amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms - waveform_modes = ( - hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + - hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) - ) - - # cpolar(r, theta) in C is equivalent to rect(r, theta) in Python - phase_factor = rect(1.0, -2 * Phi) - - return amplitude * waveform_modes * phase_factor - -def hl_2_m_min2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, - PhiDOT: float, R: float, vpnorder: int, S1z: float, - S2z: float, x: float, params: KeplerVars) -> complex: - - if vpnorder < 0 or vpnorder > 8: - raise ValueError("Error in hl_2_m_min2: Input PN order parameter should be between [0, 8].") - - else: - # Calculate the leading amplitude coefficient - amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Sum the GO and QC terms, then apply the complex conjugate for the m = -2 mode - waveform_modes = ( - hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + - hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) - ).conjugate() - - # Calculate the phase factor with positive 2 * Phi - phase_factor = rect(1.0, 2 * Phi) - - return amplitude * waveform_modes * phase_factor - -# H21 - -def hGO_2_m_1( - mass: float, - Nu: float, - r: float, - rDOT: float, - PhiDOT: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars, - ) -> complex: - - delta = np.sqrt(1 - 4 * Nu) - kappa1 = 1.0 - kappa2 = 1.0 - r0 = 2 * mass / np.exp(0.5) - - if vpnorder == 1: - return 0.6666666666666666j * delta * mass * PhiDOT - - elif vpnorder == 2: - return ( - (-0.5j * params.Mtot2 * - ((1 + delta) * S1z + (-1 + delta) * S2z)) - / params.r2 - ) - - elif vpnorder == 3: - return ( - (0.023809523809523808j * delta * mass * PhiDOT * - (4 * mass * (-9 + 11 * Nu) + - r * ((19 - 24 * Nu) * params.PhiDOT2 * params.r2 + - 2j * (83 + 2 * Nu) * PhiDOT * r * rDOT + - 2 * (-33 + 10 * Nu) * params.rDOT2))) - / r - ) - - elif vpnorder == 4: - return ( - (0.011904761904761904j * params.Mtot2 * - (2 * mass * - ((77 + 59 * Nu + 11 * delta * (7 + Nu)) * S1z + - (-77 - 59 * Nu + 11 * delta * (7 + Nu)) * S2z) + - r * (-2j * PhiDOT * r * rDOT * - (147 * (1 + delta) * S1z + (-83 + 13 * delta) * Nu * S1z + - 147 * (-1 + delta) * S2z + (83 + 13 * delta) * Nu * S2z) + - params.rDOT2 * - ((105 * (1 + delta) - 4 * (13 + 15 * delta) * Nu) * S1z + - (-105 + 15 * delta * (7 - 4 * Nu) + 52 * Nu) * S2z) + - 4 * params.PhiDOT2 * params.r2 * - ((-21 - 21 * delta + 66 * Nu + 4 * delta * Nu) * S1z + - (21 - 21 * delta - 66 * Nu + 4 * delta * Nu) * S2z)))) - / params.r3 - ) - - elif vpnorder == 5: - term1 = ( - (0.0013227513227513227j * delta * mass * PhiDOT * - (10 * params.Mtot2 * (31 - 205 * Nu + 111 * params.eta2) - - 2 * mass * r * - ((-197 + 5 * Nu + 660 * params.eta2) * params.PhiDOT2 * params.r2 + - 1j * (-3167 - 5278 * Nu + 201 * params.eta2) * PhiDOT * r * rDOT + - 8 * (202 + 587 * Nu - 177 * params.eta2) * params.rDOT2) + - 3 * params.r2 * - ((152 - 692 * Nu + 333 * params.eta2) * params.PhiDOT4 * params.r4 + - 2j * (308 - 1607 * Nu + 111 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT - - 3 * (75 - 560 * Nu + 68 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 - - 2j * (-265 + 526 * Nu + 18 * params.eta2) * PhiDOT * r * params.rDOT3 + - (-241 + 550 * Nu - 264 * params.eta2) * params.rDOT4))) - / params.r2 - ) - - # Henry et al. ecc spin terms - term2 = ( - (params.Mtot3 * - (-4 * rDOT * - (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + - kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) - - 1j * PhiDOT * r * - ((-((1 + delta) * (9 + kappa1)) + - 2 * (9 + (4 + 3 * delta) * kappa1) * Nu) * params.S1z2 - - 12 * delta * Nu * S1z * S2z + - (9 + kappa2 - 2 * (9 + 4 * kappa2) * Nu + - delta * (-9 - kappa2 + 6 * kappa2 * Nu)) * params.S2z2))) - / (6.0 * params.r3) - ) - - return term1 + term2 - - elif vpnorder == 6: - term1 = ( - (delta * params.Mtot2 * Nu * PhiDOT * - (mass * (195 * PhiDOT * r - 946j * rDOT) + - 9 * r * - (270 * params.PhiDOT3 * params.r3 - - 483j * params.PhiDOT2 * params.r2 * rDOT - - 580 * PhiDOT * r * params.rDOT2 + - 42j * params.rDOT3))) - / (315.0 * params.r2) - ) - - # Henry et al. ecc spin terms - term2 = ( - 0.00033068783068783067j * params.Mtot2 * - (3 * params.r2 * - (4j * PhiDOT * r * params.rDOT3 * - ((-315 * (1 + delta) + 2 * (251 + 463 * delta) * Nu + - 4 * (15 + delta) * params.eta2) * S1z + - (315 - 315 * delta - 502 * Nu + 926 * delta * Nu + - 4 * (-15 + delta) * params.eta2) * S2z) + - 12 * params.PhiDOT2 * params.r2 * params.rDOT2 * - ((189 * (1 + delta) - 2 * (521 + 293 * delta) * Nu + - 7 * (55 + 23 * delta) * params.eta2) * S1z + - (189 * (-1 + delta) + 2 * (521 - 293 * delta) * Nu + - 7 * (-55 + 23 * delta) * params.eta2) * S2z) + - params.rDOT4 * - ((567 * (1 + delta) - 16 * (77 + 64 * delta) * Nu + - 8 * (177 + 173 * delta) * params.eta2) * S1z + - (567 * (-1 + delta) + 16 * (77 - 64 * delta) * Nu + - 8 * (-177 + 173 * delta) * params.eta2) * S2z) - - 4j * params.PhiDOT3 * params.r3 * rDOT * - ((936 * (1 + delta) - 5 * (979 + 215 * delta) * Nu + - 2 * (1353 + 293 * delta) * params.eta2) * S1z + - (936 * (-1 + delta) + 5 * (979 - 215 * delta) * Nu + - 2 * (-1353 + 293 * delta) * params.eta2) * S2z) + - 4 * params.PhiDOT4 * params.r4 * - ((-252 * (1 + delta) + (1315 + 857 * delta) * Nu + - 4 * (-285 + 43 * delta) * params.eta2) * S1z + - (252 + 5 * Nu * (-263 + 228 * Nu) + - delta * (-252 + Nu * (857 + 172 * Nu))) * S2z)) - - 2 * mass * r * - (-1j * PhiDOT * r * rDOT * - ((2043 * (1 + delta) + (37 + 2597 * delta) * Nu + - (10635 + 139 * delta) * params.eta2) * S1z + - (2043 * (-1 + delta) + (-37 + 2597 * delta) * Nu + - (-10635 + 139 * delta) * params.eta2) * S2z) + - params.PhiDOT2 * params.r2 * - ((-765 - Nu * (667 + 7773 * Nu) + - delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S1z + - (765 + Nu * (667 + 7773 * Nu) + - delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S2z) + - 4 * params.rDOT2 * - ((-234 * (1 + delta) - 4 * (560 + 901 * delta) * Nu + - (483 + 1111 * delta) * params.eta2) * S1z + - (234 + 7 * (320 - 69 * Nu) * Nu + - delta * (-234 + Nu * (-3604 + 1111 * Nu))) * S2z)) + - 2 * params.Mtot2 * - (1134 * kappa1 * (-1 - delta + (3 + delta) * Nu) * params.S1z3 + - 1134 * (1 + delta) * (-2 + kappa1) * Nu * params.S1z2 * S2z + - S1z * - (-5661 - 5661 * delta - 17156 * Nu - 9172 * delta * Nu + - 231 * params.eta2 + 775 * delta * params.eta2 + - 1134 * (-1 + delta) * (-2 + kappa2) * Nu * params.S2z2) + - S2z * (5661 - 5661 * delta + 17156 * Nu - 9172 * delta * Nu - - 231 * params.eta2 + 775 * delta * params.eta2 + - 1134 * kappa2 * (1 - delta + (-3 + delta) * Nu) * - params.S2z2))) - ) / params.r4 - - - return term1 + term2 - - elif vpnorder == 7: - - spin_terms = ( - -23760 * params.Mtot3 * - (params.PhiDOT3 * params.r4 * - (-12j * (-35 + 107 * Nu) * S1z + - 5 * (126 - 462 * Nu + 80 * params.eta2 + - kappa1 * (-2 - 79 * Nu + 153 * params.eta2)) * params.S1z2 + - S2z * (-420j + 1284j * Nu + - 10 * (-63 + kappa2) * S2z + - 5 * (462 + 79 * kappa2) * Nu * S2z - - 5 * (80 + 153 * kappa2) * params.eta2 * S2z)) - - 1j * params.PhiDOT2 * params.r3 * rDOT * - (-24j * (-35 + 202 * Nu) * S1z + - 5 * (-14 * Nu * (3 + 58 * Nu) + - kappa1 * (-409 + 1096 * Nu + 6 * params.eta2)) * params.S1z2 + - S2z * (-840j + 2045 * kappa2 * S2z + - 10 * (406 - 3 * kappa2) * params.eta2 * S2z + - Nu * (4848j + 210 * S2z - 5480 * kappa2 * S2z))) - - 4j * r * params.rDOT3 * - (3j * (26 + 57 * Nu) * S1z + - 5 * (4 * params.eta2 + - kappa1 * (-7 + 49 * Nu + 15 * params.eta2)) * params.S1z2 - - S2z * (78j - 35 * kappa2 * S2z + - 5 * (4 + 15 * kappa2) * params.eta2 * S2z + - Nu * (171j + 245 * kappa2 * S2z))) + - 2j * mass * rDOT * - (-4j * (-78 + 769 * Nu) * S1z + - 5 * ((14 - 59 * Nu) * Nu + - kappa1 * (-70 - 77 * Nu + 18 * params.eta2)) * params.S1z2 + - S2z * (-312j + 350 * kappa2 * S2z + - 5 * (59 - 18 * kappa2) * params.eta2 * S2z + - Nu * (3076j - 70 * S2z + 385 * kappa2 * S2z))) + - PhiDOT * params.r2 * params.rDOT2 * - (6j * (-62 + 219 * Nu) * S1z - - 5 * (189 - 756 * Nu + 388 * params.eta2 + - kappa1 * (98 - 266 * Nu + 276 * params.eta2)) * params.S1z2 + - S2z * (372j + 945 * S2z + 490 * kappa2 * S2z + - 20 * (97 + 69 * kappa2) * params.eta2 * S2z - - 2 * Nu * (657j + 35 * (54 + 19 * kappa2) * S2z))) + - mass * PhiDOT * r * - (8j * (-61 + 480 * Nu) * S1z + - 5 * (-392 + 448 * Nu + 474 * params.eta2 + - kappa1 * (-11 + 150 * Nu + 58 * params.eta2)) * params.S1z2 - - S2z * (-488j - 5 * (392 + 11 * kappa2) * S2z + - 10 * (237 + 29 * kappa2) * params.eta2 * S2z + - 10 * Nu * (384j + (224 + 75 * kappa2) * S2z)))) - ) - - orbital_terms = ( - delta * mass * - (-240 * mass * PhiDOT * params.r3 * - ((197936 - 139360 * Nu - 367105 * params.eta2 + - 253245 * params.eta3) * params.PhiDOT4 * params.r4 + - 1j * (279236 - 483940 * Nu - 2817805 * params.eta2 + - 459180 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT - - 6 * (38627 + 89295 * Nu - 492740 * params.eta2 + - 75975 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 - - 1j * (-731008 + 2287930 * Nu + 981060 * params.eta2 + - 10275 * params.eta3) * PhiDOT * r * params.rDOT3 + - (-327667 + 436705 * Nu + 659790 * params.eta2 - - 438255 * params.eta3) * params.rDOT4) + - 900 * PhiDOT * params.r4 * - (2 * (-2594 + 27609 * Nu - 74032 * params.eta2 + - 25974 * params.eta3) * params.PhiDOT6 * params.r6 + - 4j * (-5730 + 58833 * Nu - 137842 * params.eta2 + - 17123 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + - 2 * (-114 - 41622 * Nu + 147569 * params.eta2 + - 4196 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + - 4j * (-9554 + 70788 * Nu - 156227 * params.eta2 + - 5810 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + - (17619 - 138450 * Nu + 322600 * params.eta2 - - 80816 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - - 2j * (8793 - 52230 * Nu + 69340 * params.eta2 + - 2536 * params.eta3) * PhiDOT * r * params.rDOT5 + - 2 * (3957 - 24534 * Nu + 42584 * params.eta2 - - 20800 * params.eta3) * params.rDOT6) - - 2 * params.Mtot3 * - (-23760j * rDOT * - (5 * (Nu * (-14 + 31 * Nu) + 7 * kappa1 * (10 + 31 * Nu)) * params.S1z2 + - 2 * S1z * (-156j + 155 * params.eta2 * S2z + - 2 * Nu * (613j + 390 * S2z)) + - S2z * (-312j + 350 * kappa2 * S2z + - 155 * params.eta2 * S2z + - Nu * (2452j + 35 * (-2 + 31 * kappa2) * S2z))) + - PhiDOT * r * - (8946400 * params.eta3 - - 8 * (6991786 + 724680j * S1z + - 7425 * (392 + 11 * kappa1) * params.S1z2 + - 724680j * S2z + - 7425 * (392 + 11 * kappa2) * params.S2z2) - - 3600 * params.eta2 * - (-628 + 33 * (-19 + 92 * kappa1) * params.S1z2 - - 7326 * S1z * S2z + - 33 * (-19 + 92 * kappa2) * params.S2z2) + - 15 * Nu * - (994455 * M_PI2 + - 8 * (-2249485 + - 7920 * (-21 + 8 * kappa1) * params.S1z2 + - 283536j * S2z + - 7920 * (-21 + 8 * kappa2) * params.S2z2 - - 1584 * S1z * (-179j + 170 * S2z))))) + - 3 * params.Mtot2 * r * - (31680j * params.rDOT3 * - (5 * (4 * params.eta2 + 7 * kappa1 * (-1 + 5 * Nu)) * params.S1z2 + - S1z * (78j + 40 * params.eta2 * S2z + - Nu * (327j + 420 * S2z)) + - S2z * (78j - 35 * kappa2 * S2z + - 20 * params.eta2 * S2z + - Nu * (327j + 175 * kappa2 * S2z))) - - 22 * PhiDOT * r * params.rDOT2 * - (2553200 * params.eta3 - - 24 * (268267 + 5580j * S1z + - 525 * (27 + 14 * kappa1) * params.S1z2 + - 5580j * S2z + - 525 * (27 + 14 * kappa2) * params.S2z2) - - 200 * params.eta2 * - (39445 + 72 * (-4 + 21 * kappa1) * params.S1z2 - - 3600 * S1z * S2z + - 72 * (-4 + 21 * kappa2) * params.S2z2) + - 25 * Nu * - (23247 * M_PI2 + - 8 * (-69259 + 1026j * S1z + - 126 * (27 + 5 * kappa1) * params.S1z2 + - 1026j * S2z + - 126 * (27 + 5 * kappa2) * params.S2z2))) + - params.PhiDOT3 * params.r3 * - (10071200 * params.eta3 + - 96 * (-421183 - 34650j * S1z + - 825 * (-63 + kappa1) * params.S1z2 - - 34650j * S2z + - 825 * (-63 + kappa2) * params.S2z2) - - 400 * params.eta2 * - (64177 + 792 * (-5 + 6 * kappa1) * params.S1z2 - - 17424 * S1z * S2z + - 792 * (-5 + 6 * kappa2) * params.S2z2) + - 15 * Nu * - (426195 * M_PI2 + - 8 * (-509635 + - 330 * (210 + 83 * kappa1) * params.S1z2 + - 29304j * S2z + - 330 * (210 + 83 * kappa2) * params.S2z2 - - 792 * S1z * (-37j + 70 * S2z)))) - - 2j * params.PhiDOT2 * params.r2 * rDOT * - (-8330400 * params.eta3 + - 8 * (-2810116 - 415800j * S1z + - 1012275 * kappa1 * params.S1z2 - - 415800j * S2z + - 1012275 * kappa2 * params.S2z2) + - 4800 * params.eta2 * - (13411 + 33 * (19 + 12 * kappa1) * params.S1z2 + - 462 * S1z * S2z + - 33 * (19 + 12 * kappa2) * params.S2z2) + - 5 * Nu * - (1278585 * M_PI2 - - 8 * (5139685 + - 990 * (-21 + 139 * kappa1) * params.S1z2 - - 313632j * S2z + - 990 * (-21 + 139 * kappa2) * params.S2z2 - - 3564 * S1z * (88j + 185 * S2z)))))) - ) - - log_term = ( - -13559040 * delta * params.Mtot3 * PhiDOT * r * - (2 * mass - 3 * params.PhiDOT2 * params.r3 + - 6j * PhiDOT * params.r2 * rDOT + - 6 * r * params.rDOT2) * - np.log(r / r0) - ) - - return ( - -1.0020843354176688e-7j * - (spin_terms + orbital_terms + log_term) - ) / params.r4 - - else: - return 0.0 + 0.0j - -def hQC_2_m_1( - mass: float, - Nu: float, - vpnorder: int, - x: float, - S1z: float, - S2z: float, - params: KeplerVars - ) -> complex: - - delta: float = np.sqrt(1.0 - 4.0 * Nu) - EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - r0: float = b0 - - # Common log terms to simplify the messy 4PN-7PN expressions - log_term_base = (-7.0 + 6.0 * EulerGamma - 3j * M_PI + np.log(64.0) + 6.0 * np.log(b0) + 9.0 * np.log(x)) - - if vpnorder == 4: - return (-2.0 * delta * params.x3 * log_term_base) / 9.0 - - elif vpnorder == 5: - spin_factor = ((1.0 + delta) * S1z + (-1.0 + delta) * S2z) - return (spin_factor * params.x3p5 * log_term_base) / 6.0 - - elif vpnorder == 6: - return -0.007936507936507936 * (delta * (-17.0 + 6.0 * Nu) * params.x4 * log_term_base) - - elif vpnorder == 7: - # Heavily nested 3.5PN (order 7) term - term1 = (params.x4p5 * ( - -98 * S1z + 84 * EulerGamma * S1z + 3017 * Nu * S1z - 2586 * EulerGamma * Nu * S1z - - 42j * M_PI * S1z + 1293j * Nu * M_PI * S1z + 98 * S2z - 84 * EulerGamma * S2z - - 3017 * Nu * S2z + 2586 * EulerGamma * Nu * S2z + 42j * M_PI * S2z - 1293j * Nu * M_PI * S2z + - 84 * S1z * np.log(2) - 2586 * Nu * S1z * np.log(2) - 84 * S2z * np.log(2) + 2586 * Nu * S2z * np.log(2) + - 84 * S1z * np.log(b0) - 2586 * Nu * S1z * np.log(b0) - 84 * S2z * np.log(b0) + 2586 * Nu * S2z * np.log(b0) + - 126 * S1z * np.log(x) - 3879 * Nu * S1z * np.log(x) - 126 * S2z * np.log(x) + 3879 * Nu * S2z * np.log(x) + - 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 * Nu * S1z) / 12.0 - (29 * EulerGamma * Nu * S1z) / 14.0 - - 0.16666666666666666j * M_PI * S1z + 1.0357142857142858j * Nu * M_PI * S1z - - (7 * S2z) / 18.0 + (EulerGamma * S2z) / 3.0 + (29 * Nu * S2z) / 12.0 - (29 * EulerGamma * Nu * S2z) / 14.0 - - 0.16666666666666666j * M_PI * S2z + 1.0357142857142858j * Nu * M_PI * S2z + - 1.7523809523809524j * np.log(2) - 2.6666666666666665j * EulerGamma * np.log(2) - - (4 * M_PI * np.log(2)) / 3.0 + (S1z * np.log(2)) / 3.0 - (29 * Nu * S1z * np.log(2)) / 14.0 + - (S2z * np.log(2)) / 3.0 - (29 * Nu * S2z * np.log(2)) / 14.0 - 1.3333333333333333j * np.log(2)**2 - - 1.3333333333333333j * np.log(b0)**2 - 1.3587301587301588j * np.log(r0) + - (np.log(b0) * (392j - 336j * EulerGamma - 168 * M_PI + 42 * S1z - 261 * Nu * S1z + 42 * S2z - 261 * Nu * S2z - 336j * np.log(2) - 504j * np.log(x))) / 126.0 + - 2.6285714285714286j * np.log(x) - 4j * EulerGamma * np.log(x) - 2 * M_PI * np.log(x) + - (S1z * np.log(x)) / 2.0 - (87 * Nu * S1z * np.log(x)) / 28.0 + (S2z * np.log(x)) / 2.0 - - (87 * Nu * S2z * np.log(x)) / 28.0 - 4j * np.log(2) * np.log(x) - 3j * np.log(x)**2 - ) - )) / 252.0 - return term1 - - else: - return 0.0 + 0.0j - -def hl_2_m_1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_2_m_1: Input PN order parameter should be between [0, 8]." - ) - - # Calculate the pre-factor: (4 * M * eta * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - go_component: complex = hGO_2_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_2_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - phase_factor: complex = np.exp(-1j * Phi) - - # Combine terms - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_2_m_min1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_2_m_min1: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # These are the same functions used for the m=1 mode - go_component: complex = hGO_2_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_2_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # In C: conj(hGO + hQC) - # In Python: use the .conjugate() method or np.conj() - amplitude_term: complex = (go_component + qc_component).conjugate() - - # In C: cpolar(1, 1 * Phi) -> exp(i * Phi) - # Note the sign change from the m=1 mode - phase_factor: complex = np.exp(1j * Phi) - - return pre_factor * amplitude_term * phase_factor - -# H33 - -def hGO_3_m_3( - mass: float, - Nu: float, - r: float, - rDOT: float, - PhiDOT: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars, - ) -> complex: - delta = np.sqrt(1 - 4 * Nu) - kappa1 = 1.0 - kappa2 = 1.0 - r0 = 2 * mass / np.exp(0.5) - - 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_d2 = combination_d ** 2 - combination_d5 = combination_d ** 5 - - combination_e = 1j * PhiDOT * r + rDOT - combination_e3 = combination_e ** 3 - - if vpnorder == 1: - return ( - (np.sqrt(0.11904761904761904) * delta * - (2 * r * combination_a3 + - mass * (-7j * PhiDOT * r + 4 * rDOT))) - / (2.0 * r) - ) - - elif vpnorder == 3: - return ( - (np.sqrt(0.11904761904761904) * delta * - (6 * (-5 + 19 * Nu) * params.r2 * combination_b4 * - (1j * PhiDOT * r + rDOT) + - 2 * params.Mtot2 * - (-3j * (-101 + 43 * Nu) * PhiDOT * r + - (-109 + 86 * Nu) * rDOT) + - 3 * mass * r * - (-12j * (1 + 4 * Nu) * params.PhiDOT3 * params.r3 + - 6 * (14 + 31 * Nu) * params.PhiDOT2 * params.r2 * rDOT + - 3j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 - - 4 * (8 + 17 * Nu) * params.rDOT3))) - / (36.0 * params.r2) - ) - - elif vpnorder == 4: - return ( - (-0.125j * np.sqrt(0.11904761904761904) * params.Mtot2 * - (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) + - r * (2 * params.rDOT2 * - (6 * (1 + delta) * S1z - 5 * (5 + 3 * delta) * Nu * S1z + - (-6 + delta * (6 - 15 * Nu) + 25 * Nu) * S2z) + - params.PhiDOT2 * params.r2 * - (-24 * (1 + delta) * S1z + (119 + 33 * delta) * Nu * S1z + - (24 - 119 * Nu + 3 * delta * (-8 + 11 * Nu)) * S2z) + - 2j * PhiDOT * r * rDOT * - (-18 * (1 + delta) * S1z + (77 + 39 * delta) * Nu * S1z + - (18 - 77 * Nu + 3 * delta * (-6 + 13 * Nu)) * S2z)))) - / params.r3 - ) - - elif vpnorder == 5: - term1 = ( - delta * - (30 * (183 - 1579 * Nu + 3387 * params.eta2) * params.r3 * - combination_c2 * combination_d5 + - 10 * params.Mtot3 * - (-1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r + - 4 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + - 2 * params.Mtot2 * r * - (-11j * (-5353 - 13493 * Nu + 4671 * params.eta2) * params.PhiDOT3 * params.r3 + - (-75243 - 142713 * Nu + 192821 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT + - 220j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 - - 10 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + - 3 * mass * params.r2 * - (2j * (-7633 + 9137 * Nu + 28911 * params.eta2) * params.PhiDOT5 * params.r5 - - 4 * (-8149 + 1576 * Nu + 43533 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT - - 2j * (-9297 - 19517 * Nu + 64839 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - - 32 * (-1288 + 3667 * Nu + 4056 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 - - 5j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 + - 20 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) - / (1584.0 * np.sqrt(210) * params.r3) - ) - - # Henry et al. ecc spin terms - term2 = ( - 0.125j * np.sqrt(1.0714285714285714) * params.Mtot3 * - (7 * PhiDOT * r + 2j * rDOT) * - (kappa1 * (-1 - delta + 2 * (2 + delta) * Nu) * params.S1z2 + - S2z * (-4 * delta * Nu * S1z + - kappa2 * (1 - delta + 2 * (-2 + delta) * Nu) * S2z)) - / params.r3 - ) - - return term1 + term2 - - elif vpnorder == 6: - term1 = ( - -(delta * params.Mtot2 * Nu * - (668 * params.Mtot2 + - 2 * mass * r * - (4081 * params.PhiDOT2 * params.r2 + - 297j * PhiDOT * r * rDOT - 452 * params.rDOT2) + - 5 * params.r2 * - (1329 * params.PhiDOT4 * params.r4 - - 2926j * params.PhiDOT3 * params.r3 * rDOT - - 384 * params.PhiDOT2 * params.r2 * params.rDOT2 - - 408j * PhiDOT * r * params.rDOT3 + - 200 * params.rDOT4))) - / (36.0 * np.sqrt(210) * params.r4) - ) - - # Henry et al. ecc spin terms - term2 = ( - -0.006944444444444444j * params.Mtot2 * - (10 * params.Mtot2 * - ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + - 8 * (12 + 47 * delta) * params.eta2) * S1z + - (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + - 8 * (-12 + 47 * delta) * params.eta2) * S2z) + - 2 * mass * r * - (2 * params.PhiDOT2 * params.r2 * - ((1320 * (1 + delta) - 2 * (4469 + 211 * delta) * Nu + - (8709 + 2777 * delta) * params.eta2) * S1z + - (1320 * (-1 + delta) + 8938 * Nu - 422 * delta * Nu + - (-8709 + 2777 * delta) * params.eta2) * S2z) + - 3j * PhiDOT * r * rDOT * - ((2000 * (1 + delta) - (9147 + 3173 * delta) * Nu + - (8911 + 5273 * delta) * params.eta2) * S1z + - (2000 * (-1 + delta) + (9147 - 3173 * delta) * Nu + - (-8911 + 5273 * delta) * params.eta2) * S2z) + - 10 * params.rDOT2 * - ((-105 * (1 + delta) + (541 + 77 * delta) * Nu - - 2 * (462 + 247 * delta) * params.eta2) * S1z + - (105 + Nu * (-541 + 924 * Nu) + - delta * (-105 + (77 - 494 * Nu) * Nu)) * S2z)) - - 3 * params.r2 * - (-3 * params.PhiDOT2 * params.r2 * params.rDOT2 * - ((480 * (1 + delta) - (1711 + 1889 * delta) * Nu + - 2 * (-1161 + 757 * delta) * params.eta2) * S1z + - (480 * (-1 + delta) + (1711 - 1889 * delta) * Nu + - 2 * (1161 + 757 * delta) * params.eta2) * S2z) + - 2 * params.PhiDOT4 * params.r4 * - ((350 * (1 + delta) - 4 * (404 + 461 * delta) * Nu + - (883 + 769 * delta) * params.eta2) * S1z + - (350 * (-1 + delta) + 4 * (404 - 461 * delta) * Nu + - (-883 + 769 * delta) * params.eta2) * S2z) + - 2j * params.PhiDOT3 * params.r3 * rDOT * - ((660 * (1 + delta) - (4061 + 2899 * delta) * Nu + - (2643 + 4789 * delta) * params.eta2) * S1z + - (660 * (-1 + delta) + (4061 - 2899 * delta) * Nu + - (-2643 + 4789 * delta) * params.eta2) * S2z) + - 10 * params.rDOT4 * - ((-30 * (1 + delta) + (187 + 101 * delta) * Nu - - 2 * (159 + 61 * delta) * params.eta2) * S1z + - (30 + Nu * (-187 + 318 * Nu) + - delta * (-30 + (101 - 122 * Nu) * Nu)) * S2z) + - 2j * PhiDOT * r * params.rDOT3 * - ((90 + Nu * (-1321 + 5118 * Nu) + - delta * (90 + Nu * (-319 + 714 * Nu))) * S1z + - (-90 + (1321 - 5118 * Nu) * Nu + - delta * (90 + Nu * (-319 + 714 * Nu))) * S2z))) - / (np.sqrt(210) * params.r4) - ) - - return term1 + term2 - - elif vpnorder == 7: - M_PI2 = M_PI ** 2 - - spin_terms = ( - 504504 * params.Mtot3 * - (2 * mass * - (rDOT * - (S1z * (108 - 498 * Nu + - 5j * (-24 - 5 * kappa1 + 3 * (76 + kappa1) * Nu + - 4 * (-111 + 13 * kappa1) * params.eta2) * S1z) + - 6 * (-18 + 83 * Nu) * S2z - - 5j * (-24 - 5 * kappa2 + 3 * (76 + kappa2) * Nu + - 4 * (-111 + 13 * kappa2) * params.eta2) * params.S2z2) + - PhiDOT * r * - (S1z * (-3j * (-99 + 581 * Nu) + - 5 * (-24 + 399 * kappa1 + 48 * (7 - 19 * Nu) * Nu + - kappa1 * Nu * (-1629 + 188 * Nu)) * S1z) + - 3j * (-99 + 581 * Nu) * S2z - - 5 * (-24 + 399 * kappa2 + 48 * (7 - 19 * Nu) * Nu + - kappa2 * Nu * (-1629 + 188 * Nu)) * params.S2z2)) + - r * (3 * PhiDOT * r * params.rDOT2 * - (S1z * (216j + 545 * kappa1 * S1z + - 40 * (45 + 8 * kappa1) * params.eta2 * S1z - - 30 * Nu * (50j + 20 * S1z + 73 * kappa1 * S1z)) + - 12j * (-18 + 125 * Nu) * S2z - - 5 * (109 * kappa2 - 6 * (20 + 73 * kappa2) * Nu + - 8 * (45 + 8 * kappa2) * params.eta2) * params.S2z2) + - 2 * params.rDOT3 * - (S1z * (-54 + 145j * kappa1 * S1z - - 30j * Nu * (11j + 2 * Nu * S1z + - kappa1 * (17 + 8 * Nu) * S1z)) + - 6 * (9 - 55 * Nu) * S2z + - 5j * (12 * params.eta2 + - kappa2 * (-29 + 6 * Nu * (17 + 8 * Nu))) * params.S2z2) + - 6 * params.PhiDOT2 * params.r2 * rDOT * - (S1z * (297 - 465 * Nu + - 5j * (6 * (20 - 87 * Nu) * Nu + - kappa1 * (-50 + 3 * Nu * (47 + 76 * Nu))) * S1z) + - 3 * (-99 + 155 * Nu) * S2z - - 5j * (6 * (20 - 87 * Nu) * Nu + - kappa2 * (-50 + 3 * Nu * (47 + 76 * Nu))) * params.S2z2) + - params.PhiDOT3 * params.r3 * - (3j * (531 - 1295 * Nu) * S1z + - 10 * (-33 * kappa1 + 6 * (30 + 13 * kappa1) * Nu + - 4 * (-96 + 67 * kappa1) * params.eta2) * params.S1z2 + - S2z * (-1593j + 330 * kappa2 * S2z + - 5 * Nu * (777j - - 4 * (90 + 39 * kappa2 - 192 * Nu + - 134 * kappa2 * Nu) * S2z))))) - ) - - orbital_terms = ( - delta * - (-17640 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * - params.r4 * combination_b6 * combination_e3 + - 168 * params.Mtot2 * params.r2 * - (1j * (-7508635 + 7 * Nu * (-1318438 + Nu * (-10231834 + 9667755 * Nu))) * - params.PhiDOT5 * params.r5 + - 7 * (1235591 + Nu * (884445 + (23935218 - 26913443 * Nu) * Nu)) * - params.PhiDOT4 * params.r4 * rDOT - - 1j * (8961149 + 7 * Nu * (-31755709 + Nu * (-11134798 + 22187331 * Nu))) * - params.PhiDOT3 * params.r3 * params.rDOT2 - - (-36806435 + 7 * Nu * (33178545 + Nu * (24565078 + 22873537 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT3 - - 5j * (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * - PhiDOT * r * params.rDOT4 + - 5 * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * - params.rDOT5) + - 1764 * mass * params.r3 * - (-2j * (239087 + Nu * (-1206515 + Nu * (422631 + 3979375 * Nu))) * - params.PhiDOT7 * params.r7 + - 2 * (621284 + Nu * (-2279907 + 2 * Nu * (-1180187 + 5876531 * Nu))) * - params.PhiDOT6 * params.r6 * rDOT + - 2j * (39270 + Nu * (1235486 - 5319747 * Nu + 4406349 * params.eta2)) * - params.PhiDOT5 * params.r5 * params.rDOT2 + - 8 * (349111 + 4 * Nu * (-519370 + 33 * Nu * (10939 + 42635 * Nu))) * - params.PhiDOT4 * params.r4 * params.rDOT3 + - 2j * (1212607 + 3 * Nu * (-2012698 - 67827 * Nu + 7955628 * params.eta2)) * - params.PhiDOT3 * params.r3 * params.rDOT4 + - 4 * (201135 + 2 * Nu * (-773107 + Nu * (1214819 + 1157652 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT5 + - 5j * (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * - PhiDOT * r * params.rDOT6 - - 40 * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * - params.rDOT7) + - 2 * params.Mtot4 * - (4 * rDOT * - (269279500 * params.eta3 + - 2 * (-174108226 + - 63063 * S1z * (108j + 5 * (24 + 5 * kappa1) * S1z) + - 63063 * S2z * (108j + 5 * (24 + 5 * kappa2) * S2z)) - - 21 * Nu * - (103100846 + 1846845 * M_PI2 + 1693692j * S2z - - 6006 * (S1z * (-282j + 5 * (-180 + 7 * kappa1) * S1z) - - 980 * S1z * S2z + - 5 * (-180 + 7 * kappa2) * params.S2z2)) - - 2940 * params.eta2 * - (-122855 + - 4719 * ((-6 + 7 * kappa1) * params.S1z2 - - 26 * S1z * S2z + - (-6 + 7 * kappa2) * params.S2z2))) + - 1j * PhiDOT * r * - (-1176172480 * params.eta3 + - 8 * (-74084729 + - 189189 * S1z * (99j + 5 * (-8 + 133 * kappa1) * S1z) + - 189189 * S2z * (99j + 5 * (-8 + 133 * kappa2) * S2z)) + - 35280 * params.eta2 * - (56255 + - 429 * ((-22 + 65 * kappa1) * params.S1z2 - - 174 * S1z * S2z + - (-22 + 65 * kappa2) * params.S2z2)) - - 147 * Nu * - (-65012788 + 4485195 * M_PI2 + 3943368j * S2z + - 10296 * (S1z * (383j + 5 * (-96 + 277 * kappa1) * S1z) - - 3220 * S1z * S2z + - 5 * (-96 + 277 * kappa2) * params.S2z2)))) + - params.Mtot3 * r * - (-12j * PhiDOT * r * params.rDOT2 * - (-1035895280 * params.eta3 - - 2 * (-547993687 + - 63063 * S1z * (216j + 545 * kappa1 * S1z) + - 63063 * S2z * (216j + 545 * kappa2 * S2z)) + - 77 * Nu * - (42451610 + 1511055 * M_PI2 + 1749384j * S2z + - 6552 * (S1z * (267j + 25 * (6 + 11 * kappa1) * S1z) - - 5 * S1z * S2z + - 25 * (6 + 11 * kappa2) * params.S2z2)) + - 490 * params.eta2 * - (-5802767 + - 5148 * ((-6 + 23 * kappa1) * params.S1z2 - - 58 * S1z * S2z + - (-6 + 23 * kappa2) * params.S2z2))) + - 4 * params.rDOT3 * - (-1359334480 * params.eta3 - - 4 * (-150254558 + - 63063 * S1z * (54j + 145 * kappa1 * S1z) + - 63063 * S2z * (54j + 145 * kappa2 * S2z)) + - 231 * Nu * - (8490448 + 503685 * M_PI2 + 242424j * S2z + - 2184 * (S1z * (111j + 110 * kappa1 * S1z) + - 70 * S1z * S2z + - 110 * kappa2 * params.S2z2)) + - 11760 * params.eta2 * - (-312980 + - 429 * ((3 + 25 * kappa1) * params.S1z2 - - 44 * S1z * S2z + - (3 + 25 * kappa2) * params.S2z2))) + - 6 * params.PhiDOT2 * params.r2 * rDOT * - (2368900688 * params.eta3 + - 8 * (-812986529 + - 63063 * S1z * (297j + 250 * kappa1 * S1z) + - 63063 * S2z * (297j + 250 * kappa2 * S2z)) - - 1176 * params.eta2 * - (2423171 + - 4290 * ((-3 + 41 * kappa1) * params.S1z2 - - 88 * S1z * S2z + - (-3 + 41 * kappa2) * params.S2z2)) + - 539 * Nu * - (-24139772 + 647595 * M_PI2 + 120744j * S2z - - 936 * (S1z * (-129j + 600 * S1z + 205 * kappa1 * S1z) - - 460 * S1z * S2z + - 5 * (120 + 41 * kappa2) * params.S2z2))) + - 1j * params.PhiDOT3 * params.r3 * - (-4538040136 * params.eta3 - - 88 * (259018351 + - 17199 * S1z * (-531j + 110 * kappa1 * S1z) + - 17199 * S2z * (-531j + 110 * kappa2 * S2z)) + - 2352 * params.eta2 * - (7332973 + - 12870 * ((5 + 23 * kappa1) * params.S1z2 - - 36 * S1z * S2z + - (5 + 23 * kappa2) * params.S2z2)) + - 21 * Nu * - (49864815 * M_PI2 + - 8 * (-88128538 - 2099097j * S2z + - 9009 * (S1z * (-233j + 40 * (15 + kappa1) * S1z) + - 360 * S1z * S2z + - 40 * (15 + kappa2) * params.S2z2)))))) - ) - - log_term = ( - 74954880 * delta * params.Mtot3 * - (22j * mass * PhiDOT * r + - 59j * params.PhiDOT3 * params.r4 + 8 * mass * rDOT + - 66 * params.PhiDOT2 * params.r3 * rDOT + - 24j * PhiDOT * params.r2 * params.rDOT2 - - 4 * r * params.rDOT3) * - np.log(r / r0) - ) - - return ( - 1j * (spin_terms + orbital_terms + log_term) - / (2.4216192e7 * np.sqrt(210) * params.r4) - ) - - else: - return 0.0 + 0.0j - -def hQC_3_m_3( - mass: float, - Nu: float, - vpnorder: int, - x: float, - S1z: float, - S2z: float, - params: KeplerVars - ) -> complex: - - delta: float = np.sqrt(1.0 - 4.0 * Nu) - EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - r0: float = b0 - - if vpnorder == 4: - return ( - (3.0 * np.sqrt(0.04285714285714286) * delta * params.x3 * ( - -97.0 + 60.0 * EulerGamma - 30.0j * M_PI + - 60.0 * np.log(2.0) + 60.0 * np.log(3.0) + - 60.0 * np.log(b0) + 90.0 * np.log(x) - )) / 4.0 - ) - - elif vpnorder == 6: - numerator_6 = (delta * params.x4 * ( - 188568.0 - 116640.0 * EulerGamma - 70537.0 * Nu + 43740.0 * EulerGamma * Nu + - 58320.0j * M_PI - 21870.0j * Nu * M_PI - - 116640.0 * np.log(2.0) + 43740.0 * Nu * np.log(2.0) - - 116640.0 * np.log(3.0) + 43740.0 * Nu * np.log(3.0) + - 14580.0 * (-8.0 + 3.0 * Nu) * np.log(b0) + - 21870.0 * (-8.0 + 3.0 * Nu) * np.log(x) - )) - 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 * np.log(b0) * ( - -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * M_PI + - 5.0 * (-4.0 - 4.0 * delta + 19.0 * Nu + 5.0 * delta * Nu) * S1z + - 20.0 * S2z - 20.0 * delta * S2z - 95.0 * Nu * S2z + 25.0 * delta * Nu * S2z + - 120.0j * delta * np.log(2.0) + 120.0j * delta * np.log(3.0) + - 180.0j * delta * np.log(x) - ) - - numerator_7 = (params.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 * Nu * S1z - 643223.0 * delta * Nu * S1z + 1508220.0 * EulerGamma * Nu * S1z + - 396900.0 * delta * EulerGamma * Nu * S1z + 158760.0j * M_PI * S1z + 158760.0j * delta * M_PI * S1z - - 754110.0j * Nu * M_PI * S1z - 198450.0j * delta * Nu * M_PI * S1z - - 513324.0 * S2z + 513324.0 * delta * S2z + 317520.0 * EulerGamma * S2z - - 317520.0 * delta * EulerGamma * S2z + 2439857.0 * Nu * S2z - 643223.0 * delta * Nu * S2z - - 1508220.0 * EulerGamma * Nu * S2z + 396900.0 * delta * EulerGamma * Nu * S2z - - 158760.0j * M_PI * S2z + 158760.0j * delta * M_PI * S2z + - 754110.0j * Nu * M_PI * S2z - 198450.0j * delta * Nu * M_PI * S2z - - 2490264.0j * delta * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + - 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - - 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * Nu * S1z * np.log(2.0) + - 396900.0 * delta * Nu * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - - 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * Nu * S2z * np.log(2.0) + - 396900.0 * delta * Nu * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - - 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + - 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - - 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + - 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - - 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * Nu * S2z * np.log(3.0) + - 396900.0 * delta * Nu * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + - 952560.0j * delta * (np.log(3.0)**2) + 952560.0j * delta * (np.log(b0)**2) + - 589680.0j * delta * np.log(r0) - 3735396.0j * delta * np.log(x) + - 2857680.0j * delta * EulerGamma * np.log(x) + 1428840.0 * delta * M_PI * np.log(x) - - 476280.0 * S1z * np.log(x) - 476280.0 * delta * S1z * np.log(x) + - 2262330.0 * Nu * S1z * np.log(x) + 595350.0 * delta * Nu * S1z * np.log(x) + - 476280.0 * S2z * np.log(x) - 476280.0 * delta * S2z * np.log(x) - - 2262330.0 * Nu * S2z * np.log(x) + 595350.0 * delta * Nu * S2z * np.log(x) + - 2857680.0j * delta * np.log(2.0) * np.log(x) + - 2857680.0j * delta * np.log(3.0) * np.log(x) + - 2143260.0j * delta * (np.log(x)**2) + - log_b0_term - )) - return numerator_7 / (2352.0 * np.sqrt(210.0)) - - else: - return 0.0 + 0.0j - -def hl_3_m_3( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R - # Note: This specific pre-factor is identical across several modes in the C source - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - go_component: complex = hGO_3_m_3( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_3( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) - # The higher 'm' index means the phase evolves 3 times faster than the orbit - phase_factor: complex = np.exp(-3j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_3_m_min3( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R - # Note: This specific pre-factor is identical across several modes in the C source - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # We assume hGO_3_m_3 and hQC_3_m_3 are defined elsewhere in your package - go_component: complex = hGO_3_m_3( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_3( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - # In C: conj(hGO + hQC) - # In Python: use the .conjugate() method or np.conj() - amplitude_term: complex = (go_component + qc_component).conjugate() - - # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) - # The higher 'm' index means the phase evolves 3 times faster than the orbit - phase_factor: complex = np.exp(3j * Phi) - - return pre_factor * amplitude_term * phase_factor - -# H32 - -def hGO_3_m_2( - mass: float, - Nu: float, - r: float, - rDOT: float, - PhiDOT: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars, - ) -> complex: - delta = np.sqrt(1 - 4 * Nu) - kappa1 = 1.0 - kappa2 = 1.0 - - if vpnorder == 2: - return ( - -(np.sqrt(0.7142857142857143) * mass * (-1 + 3 * Nu) * PhiDOT * - (4 * PhiDOT * r + 1j * rDOT)) - / 6.0 - ) - - elif vpnorder == 3: - return ( - (np.sqrt(0.7142857142857143) * params.Mtot2 * Nu * - (4 * PhiDOT * r + 1j * rDOT) * (S1z + S2z)) - / (3.0 * params.r2) - ) - - elif vpnorder == 4: - return ( - -(mass * PhiDOT * - (2 * mass * - ((167 - 925 * Nu + 1615 * params.eta2) * PhiDOT * r + - 5j * (-82 + 239 * Nu + 55 * params.eta2) * rDOT) - - 3 * r * - (2 * (-13 - 25 * Nu + 355 * params.eta2) * params.PhiDOT3 * params.r3 - - 60j * (-8 + 25 * Nu + params.eta2) * params.PhiDOT2 * params.r2 * rDOT + - 12 * (-23 + 70 * Nu + 10 * params.eta2) * PhiDOT * r * params.rDOT2 + - 5j * (-13 + 38 * Nu + 10 * params.eta2) * params.rDOT3))) - / (108.0 * np.sqrt(35) * r) - ) - - elif vpnorder == 5: - term1 = ( - (params.Mtot2 * Nu * PhiDOT * - (7j * mass + - r * (49j * params.PhiDOT2 * params.r2 + - 90 * PhiDOT * r * rDOT - 6j * params.rDOT2))) - / (4.0 * np.sqrt(35) * params.r2) - ) - - # Henry et al. ecc spin terms - term2 = ( - (np.sqrt(0.7142857142857143) * params.Mtot2 * - (2j * mass * rDOT * - ((-12 + Nu * (97 + 4 * Nu) + delta * (-12 + 5 * Nu)) * S1z + - (-12 + delta * (12 - 5 * Nu) + Nu * (97 + 4 * Nu)) * S2z) + - 4 * mass * PhiDOT * r * - (-((12 + delta * (12 - 23 * Nu) + Nu * (53 + 8 * Nu)) * S1z) - - (12 + Nu * (53 + 8 * Nu) + delta * (-12 + 23 * Nu)) * S2z) - - 3 * r * - (16 * params.eta2 * PhiDOT * r * - (4 * params.PhiDOT2 * params.r2 - - 2j * PhiDOT * r * rDOT + params.rDOT2) * - (S1z + S2z) + - 30j * params.PhiDOT2 * params.r2 * rDOT * - (S1z + delta * S1z + S2z - delta * S2z) + - Nu * (-4 * params.PhiDOT3 * params.r3 * - ((5 + 17 * delta) * S1z + (5 - 17 * delta) * S2z) - - 1j * params.PhiDOT2 * params.r2 * rDOT * - ((189 + 17 * delta) * S1z + (189 - 17 * delta) * S2z) + - 20 * PhiDOT * r * params.rDOT2 * - (-((-3 + delta) * S1z) + (3 + delta) * S2z) - - 4j * params.rDOT3 * - ((-4 + delta) * S1z - (4 + delta) * S2z))))) - / (72.0 * params.r3) - ) - - return term1 + term2 - - elif vpnorder == 6: - term1 = ( - -(mass * PhiDOT * - (4 * params.Mtot2 * - (2 * (5377 + 6438 * Nu - 79866 * params.eta2 + 37348 * params.eta3) * - PhiDOT * r - - 5j * (-4115 + 18399 * Nu - 20276 * params.eta2 + 7 * params.eta3) * - rDOT) - - 4 * mass * r * - ((4599 - 15737 * Nu + 36259 * params.eta2 + 108563 * params.eta3) * - params.PhiDOT3 * params.r3 - - 1j * (-34053 + 59698 * Nu + 192949 * params.eta2 + 16193 * params.eta3) * - params.PhiDOT2 * params.r2 * rDOT + - (-59058 + 77983 * Nu + 322468 * params.eta2 - 4264 * params.eta3) * - PhiDOT * r * params.rDOT2 + - 5j * (-3387 + 8518 * Nu + 8968 * params.eta2 + 884 * params.eta3) * - params.rDOT3) + - 3 * params.r2 * - (4 * (-710 + 3892 * Nu - 10655 * params.eta2 + 24000 * params.eta3) * - params.PhiDOT5 * params.r5 + - 11j * (-1484 + 11693 * Nu - 25006 * params.eta2 + 428 * params.eta3) * - params.PhiDOT4 * params.r4 * rDOT + - 4 * (4161 - 25618 * Nu + 29489 * params.eta2 + 22078 * params.eta3) * - params.PhiDOT3 * params.r3 * params.rDOT2 + - 44j * (-151 + 1067 * Nu - 2419 * params.eta2 + 57 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT3 + - 4 * (2041 - 11680 * Nu + 19334 * params.eta2 + 3368 * params.eta3) * - PhiDOT * r * params.rDOT4 + - 5j * (477 - 2624 * Nu + 3862 * params.eta2 + 1160 * params.eta3) * - params.rDOT5))) - / (4752.0 * np.sqrt(35) * params.r2) - ) - - # Henry et al. ecc spin terms - term2 = ( - (np.sqrt(0.7142857142857143) * params.Mtot3 * - (2 * mass * - (2j * (1 + delta - 2 * Nu) * S1z + - Nu * ((1 + delta) * (-6 + kappa1) + 12 * Nu) * params.S1z2 + - 8 * Nu * (-1 + 3 * Nu) * S1z * S2z + - S2z * (-2j * (-1 + delta + 2 * Nu) + - Nu * (-6 - delta * (-6 + kappa2) + kappa2 + 12 * Nu) * S2z)) + - r * (4 * params.rDOT2 * - (2j * (1 + delta - 2 * Nu) * S1z + - Nu * ((1 + delta) * (-2 + kappa1) + 4 * Nu) * params.S1z2 + - 8 * params.eta2 * S1z * S2z + - S2z * (-2j * (-1 + delta + 2 * Nu) + - Nu * (-2 - delta * (-2 + kappa2) + kappa2 + 4 * Nu) * S2z)) + - 2 * params.PhiDOT2 * params.r2 * - (14j * (1 + delta - 2 * Nu) * S1z + - (6 * (1 + delta) * kappa1 - - (26 + 23 * kappa1 + delta * (26 + 11 * kappa1)) * Nu + - 4 * (1 + 9 * kappa1) * params.eta2) * params.S1z2 - - 64 * params.eta2 * S1z * S2z + - S2z * (-14j * (-1 + delta + 2 * Nu) + - 2 * Nu * (-13 + 13 * delta + 2 * Nu) * S2z + - kappa2 * (6 + delta * (-6 + 11 * Nu) + - Nu * (-23 + 36 * Nu)) * S2z)) + - PhiDOT * r * rDOT * - (40 * (1 + delta - 2 * Nu) * S1z - - 1j * (8 * Nu * (-2 - 2 * delta + Nu) + - kappa1 * (3 + delta * (3 + 11 * Nu) + - Nu * (5 + 18 * Nu))) * params.S1z2 + - 20j * (-3 + Nu) * Nu * S1z * S2z + - S2z * (-40 * (-1 + delta + 2 * Nu) - - 1j * (8 * Nu * (-2 + 2 * delta + Nu) + - kappa2 * (3 - delta * (3 + 11 * Nu) + - Nu * (5 + 18 * Nu))) * S2z))))) - / (24.0 * params.r4) - ) - - return term1 + term2 - - elif vpnorder == 7: - return ( - -0.000014029180695847363 * - (params.Mtot2 * - (3 * params.r2 * - (-120 * params.eta3 * - (3565 * params.PhiDOT5 * params.r5 + - 2321j * params.PhiDOT4 * params.r4 * rDOT + - 8244 * params.PhiDOT3 * params.r3 * params.rDOT2 - - 869j * params.PhiDOT2 * params.r2 * params.rDOT3 - - 56 * PhiDOT * r * params.rDOT4 - - 120j * params.rDOT5) * - (S1z + S2z) + - 2475 * params.PhiDOT2 * params.r2 * - (6 * params.PhiDOT3 * params.r3 + - 77j * params.PhiDOT2 * params.r2 * rDOT - - 72 * PhiDOT * r * params.rDOT2 + - 6j * params.rDOT3) * - (S1z + delta * S1z + S2z - delta * S2z) - - 3 * Nu * - (22j * params.PhiDOT4 * params.r4 * rDOT * - (36322j + 5 * (2993 + 3893 * delta) * S1z + - 5 * (2993 - 3893 * delta) * S2z) - - 25j * params.rDOT5 * - ((1053 + 443 * delta) * S1z + (1053 - 443 * delta) * S2z) + - 44j * params.PhiDOT2 * params.r2 * params.rDOT3 * - (-5444j + 5 * (1424 + 849 * delta) * S1z + - 7120 * S2z - 4245 * delta * S2z) - - 20 * PhiDOT * r * params.rDOT4 * - (-1782j + (5963 + 2969 * delta) * S1z + - 5963 * S2z - 2969 * delta * S2z) + - 4 * params.PhiDOT5 * params.r5 * - (-86889j + 10 * (2063 + 225 * delta) * S1z + - 20630 * S2z - 2250 * delta * S2z) + - 4 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (234861j + 40 * (-1824 + 97 * delta) * S1z - - 40 * (1824 + 97 * delta) * S2z)) + - 2 * params.eta2 * - (params.PhiDOT5 * params.r5 * - (-1549757j + 300 * (1448 + 1311 * delta) * S1z + - 300 * (1448 - 1311 * delta) * S2z) + - 11j * params.PhiDOT4 * params.r4 * rDOT * - (329548j + 15 * (4113 + 1411 * delta) * S1z + - 61695 * S2z - 21165 * delta * S2z) + - 22j * params.PhiDOT2 * params.r2 * params.rDOT3 * - (-23971j + 15 * (3829 + 243 * delta) * S1z + - 57435 * S2z - 3645 * delta * S2z) + - 150j * params.rDOT5 * - ((-503 + 92 * delta) * S1z - (503 + 92 * delta) * S2z) + - 10 * PhiDOT * r * params.rDOT4 * - (4565j + 6 * (-6327 + 991 * delta) * S1z - - 6 * (6327 + 991 * delta) * S2z) + - 21 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (161403j + 10 * (-1897 + 1471 * delta) * S1z - - 10 * (1897 + 1471 * delta) * S2z))) - - 6 * mass * r * - (60 * params.eta3 * - (2417 * params.PhiDOT3 * params.r3 + - 7258j * params.PhiDOT2 * params.r2 * rDOT - - 4381 * PhiDOT * r * params.rDOT2 + - 480j * params.rDOT3) * - (S1z + S2z) - - 165 * - (1161 * params.PhiDOT3 * params.r3 - - 536j * params.PhiDOT2 * params.r2 * rDOT - - 2412 * PhiDOT * r * params.rDOT2 - - 270j * params.rDOT3) * - (S1z + delta * S1z + S2z - delta * S2z) + - 2 * Nu * - (1j * params.PhiDOT2 * params.r2 * rDOT * - (1015784j + 5 * (30849 + 88721 * delta) * S1z + - 5 * (30849 - 88721 * delta) * S2z) + - params.PhiDOT3 * params.r3 * - (-173371j + 5 * (61569 + 10789 * delta) * S1z + - 307845 * S2z - 53945 * delta * S2z) - - 5 * PhiDOT * r * params.rDOT2 * - (-115368j + (177417 + 52307 * delta) * S1z + - 177417 * S2z - 52307 * delta * S2z) + - 100j * params.rDOT3 * - ((-1545 + 181 * delta) * S1z - (1545 + 181 * delta) * S2z)) + - params.eta2 * - (20 * PhiDOT * r * params.rDOT2 * - (-11187j - 48074 * S1z + 11057 * delta * S1z - - 48074 * S2z - 11057 * delta * S2z) + - 725j * params.rDOT3 * - (-73 * S1z + 31 * delta * S1z - 73 * S2z - 31 * delta * S2z) + - params.PhiDOT3 * params.r3 * - (603141j - 543040 * S1z + 404620 * delta * S1z - - 20 * (27152 + 20231 * delta) * S2z) + - 1j * params.PhiDOT2 * params.r2 * rDOT * - (-1798104j - 648485 * S1z + 105755 * delta * S1z - - 5 * (129697 + 21151 * delta) * S2z))) + - 10 * params.Mtot2 * - (24 * params.eta3 * - (6981 * PhiDOT * r + 1600j * rDOT) * - (S1z + S2z) - - 66 * (2027 * PhiDOT * r + 380j * rDOT) * - (S1z + delta * S1z + S2z - delta * S2z) + - 30j * Nu * rDOT * - (297 * (1 + delta) * kappa1 * params.S1z3 + - 297 * (1 + delta) * kappa1 * params.S1z2 * S2z + - S1z * (17261 - 1641 * delta - - 297 * (-1 + delta) * kappa2 * params.S2z2) + - S2z * (17261 + 1641 * delta - - 297 * (-1 + delta) * kappa2 * params.S2z2)) + - 8 * Nu * PhiDOT * r * - (-7315j - - 4455 * (1 + delta) * kappa1 * params.S1z3 + - 3 * (3881 - 7757 * delta) * S2z - - 4455 * (1 + delta) * kappa1 * params.S1z2 * S2z + - 4455 * (-1 + delta) * kappa2 * params.S2z3 + - 3 * S1z * - (3881 + 7757 * delta + - 1485 * (-1 + delta) * kappa2 * params.S2z2)) + - 3 * params.eta2 * - (-5j * rDOT * - (S1z * (18793 + 223 * delta + 1188 * kappa1 * params.S1z2) + - (18793 - 223 * delta + 1188 * (-2 + kappa1) * params.S1z2) * S2z + - 1188 * (-2 + kappa2) * S1z * params.S2z2 + - 1188 * kappa2 * params.S2z3) + - 4 * PhiDOT * r * - (-4939j - 23359 * S1z - 5563 * delta * S1z + - 5940 * kappa1 * params.S1z3 + - (-23359 + 5563 * delta + - 5940 * (-2 + kappa1) * params.S1z2) * S2z + - 5940 * (-2 + kappa2) * S1z * params.S2z2 + - 5940 * kappa2 * params.S2z3))))) - / (np.sqrt(35) * params.r4) - ) - - else: - return 0.0 + 0.0j - -def hQC_3_m_2( - mass: float, - Nu: float, - vpnorder: int, - x: float, - S1z: float, - S2z: float, - params: KeplerVars - ) -> 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 - b0: float = 2.0 * mass / np.exp(0.5) - EulerGamma: float = 0.5772156649015329 - - # Pre-calculating the shared log term using np.log - common_log_term: complex = ( - -10.0 + 6.0 * EulerGamma - 3.0j * np.pi + - np.log(4096.0) + 6.0 * np.log(b0) + 9.0 * np.log(x) - ) - - if vpnorder == 5: - return ( - -0.4444444444444444j * np.sqrt(0.7142857142857143) * (-1.0 + 3.0 * Nu) * params.x3p5 * common_log_term - ) - - elif vpnorder == 6: - return ( - 0.8888888888888888j * np.sqrt(0.7142857142857143) * Nu * (S1z + S2z) * params.x4 * common_log_term - ) - - elif vpnorder == 7: - numerator = ( - -0.024691358024691357j * (193.0 - 680.0 * Nu + 230.0 * params.eta2) * params.x4p5 * common_log_term - ) - return numerator / np.sqrt(35.0) - - else: - return 0.0 + 0.0j - -def hl_3_m_2( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package - go_component: complex = hGO_3_m_2( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_2( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) - phase_factor: complex = np.exp(-2j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_3_m_min2( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package - go_component: complex = hGO_3_m_2( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_2( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - amplitude_term: complex = (go_component + qc_component).conjugate() - - # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) - phase_factor: complex = np.exp(2j * Phi) - - return pre_factor * amplitude_term * phase_factor - -# H31 - -def hGO_3_m_1( - mass: float, - Nu: float, - r: float, - rDOT: float, - PhiDOT: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars, - ) -> complex: - delta = np.sqrt(1 - 4 * Nu) - kappa1 = 1.0 - kappa2 = 1.0 - r0 = 2 * mass / np.exp(0.5) - - 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 - - if vpnorder == 1: - return ( - delta * (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 * Nu) * params.r2 * combination_b2 * combination_a3 + - 2 * params.Mtot2 * - (1j * (-101 + 43 * Nu) * PhiDOT * r + - (109 - 86 * Nu) * rDOT) + - 3 * mass * r * - (-4j * (-9 + 14 * Nu) * params.PhiDOT3 * params.r3 + - 6 * (2 + 9 * Nu) * params.PhiDOT2 * params.r2 * rDOT - - 1j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 + - 4 * (8 + 17 * Nu) * params.rDOT3)) - / (36.0 * np.sqrt(14) * params.r2) - ) - - elif vpnorder == 4: - return ( - 0.041666666666666664j * params.Mtot2 * - (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) - - r * (2 * params.rDOT2 * - (-6 * (1 + delta) * S1z + 5 * (5 + 3 * delta) * Nu * S1z + - 6 * S2z - 6 * delta * S2z + - 5 * (-5 + 3 * delta) * Nu * S2z) + - params.PhiDOT2 * params.r2 * - ((24 + 24 * delta - 87 * Nu + 31 * delta * Nu) * S1z + - (-24 + 24 * delta + 87 * Nu + 31 * delta * Nu) * S2z) + - 2j * PhiDOT * r * rDOT * - ((6 + 6 * delta - 31 * Nu + 35 * delta * Nu) * S1z + - (-6 + 6 * delta + 31 * Nu + 35 * delta * Nu) * S2z))) - / (np.sqrt(14) * params.r3) - ) - - elif vpnorder == 5: - term1 = ( - delta * - (-18j * (183 - 1579 * Nu + 3387 * params.eta2) * params.r3 * - combination_b3 * combination_a4 + - 2 * params.Mtot3 * - (1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r - - 12 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + - 2 * params.Mtot2 * r * - (-1j * (-8641 - 59189 * Nu + 31959 * params.eta2) * params.PhiDOT3 * params.r3 + - (-32635 - 29345 * Nu + 29541 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT - - 44j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 + - 6 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + - 3 * mass * params.r2 * - (2j * (-2479 - 4505 * Nu + 16785 * params.eta2) * params.PhiDOT5 * params.r5 + - 4 * (817 + 1220 * Nu - 7449 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT + - 6j * (-1679 + 1469 * Nu + 12233 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - - 32 * (-460 + 421 * Nu + 2514 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 + - 1j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 - - 12 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) - / (4752.0 * np.sqrt(14) * params.r3) - ) - - # Henry et al. ecc spin terms - term2 = ( - params.Mtot3 * - (kappa1 * - (-1j * (-13 - 13 * delta + 68 * Nu + 42 * delta * Nu) * PhiDOT * r - - 34 * (1 + delta) * rDOT + 4 * (26 + 9 * delta) * Nu * rDOT) * - params.S1z2 + - S2z * (12 * delta * Nu * (7j * PhiDOT * r - 6 * rDOT) * S1z + - kappa2 * - (-1j * (13 - 13 * delta - 68 * Nu + 42 * delta * Nu) * PhiDOT * r + - 2 * (17 - 17 * delta - 52 * Nu + 18 * delta * Nu) * rDOT) * - S2z)) - / (24.0 * np.sqrt(14) * params.r3) - ) - - return term1 + term2 - - elif vpnorder == 6: - term1 = ( - delta * params.Mtot2 * Nu * - (668 * params.Mtot2 - - 2 * mass * r * - (727 * params.PhiDOT2 * params.r2 - - 99j * PhiDOT * r * rDOT + 452 * params.rDOT2) + - params.r2 * - (-499 * params.PhiDOT4 * params.r4 + - 1534j * params.PhiDOT3 * params.r3 * rDOT + - 3072 * params.PhiDOT2 * params.r2 * params.rDOT2 - - 680j * PhiDOT * r * params.rDOT3 + - 1000 * params.rDOT4)) - / (180.0 * np.sqrt(14) * params.r4) - ) - - # Henry et al. ecc spin terms - term2 = ( - 0.0023148148148148147j * params.Mtot2 * - (2 * params.Mtot2 * - ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + - 8 * (12 + 47 * delta) * params.eta2) * S1z + - (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + - 8 * (-12 + 47 * delta) * params.eta2) * S2z) + - 3 * params.r2 * - (2 * params.rDOT4 * - ((30 + Nu * (-187 + 318 * Nu) + - delta * (30 + Nu * (-101 + 122 * Nu))) * S1z + - (-30 + (187 - 318 * Nu) * Nu + - delta * (30 + Nu * (-101 + 122 * Nu))) * S2z) + - 2 * params.PhiDOT4 * params.r4 * - ((90 - Nu * (28 + 579 * Nu) + - delta * (90 + Nu * (-800 + 551 * Nu))) * S1z + - (-90 + Nu * (28 + 579 * Nu) + - delta * (90 + Nu * (-800 + 551 * Nu))) * S2z) + - 2j * PhiDOT * r * params.rDOT3 * - ((186 - Nu * (745 + 354 * Nu) + - delta * (186 + Nu * (-191 + 554 * Nu))) * S1z + - (-186 + Nu * (745 + 354 * Nu) + - delta * (186 + Nu * (-191 + 554 * Nu))) * S2z) + - 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * - ((32 + Nu * (-451 + 230 * Nu) + - delta * (32 + Nu * (691 + 626 * Nu))) * S1z + - (-32 + (451 - 230 * Nu) * Nu + - delta * (32 + Nu * (691 + 626 * Nu))) * S2z) + - 2j * params.PhiDOT3 * params.r3 * rDOT * - ((-12 + Nu * (-341 + 315 * Nu) + - delta * (-12 + Nu * (-91 + 1213 * Nu))) * S1z + - (12 + (341 - 315 * Nu) * Nu + - delta * (-12 + Nu * (-91 + 1213 * Nu))) * S2z)) - - 2 * mass * r * - (2 * params.PhiDOT2 * params.r2 * - ((-312 * (1 + delta) + 2 * (827 - 923 * delta) * Nu + - 5 * (-201 + 131 * delta) * params.eta2) * S1z + - (-312 * (-1 + delta) - 2 * (827 + 923 * delta) * Nu + - 5 * (201 + 131 * delta) * params.eta2) * S2z) + - 2 * params.rDOT2 * - ((105 + Nu * (-541 + 924 * Nu) + - delta * (105 + Nu * (-77 + 494 * Nu))) * S1z + - (-105 + (541 - 924 * Nu) * Nu + - delta * (105 + Nu * (-77 + 494 * Nu))) * S2z) + - 1j * PhiDOT * r * rDOT * - ((1104 - 7 * Nu * (439 + 597 * Nu) + - delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S1z + - (-1104 + 7 * Nu * (439 + 597 * Nu) + - delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S2z))) - / (np.sqrt(14) * params.r4) - ) - - return term1 + term2 - - elif vpnorder == 7: - M_PI2 = np.pi ** 2 - - spin_terms = ( - 1513512 * params.Mtot3 * - (2 * mass * - (PhiDOT * r * - (1j * (-97 + 631 * Nu) * S1z + - 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + - 3 * kappa1 * (-39 + Nu * (149 + 4 * Nu))) * params.S1z2 + - S2z * (97j - 631j * Nu - - 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + - 3 * kappa2 * (-39 + Nu * (149 + 4 * Nu))) * S2z)) + - rDOT * - (2 * (-18 + 83 * Nu) * S1z - - 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + - kappa1 * (155 + Nu * (-373 + 164 * Nu))) * params.S1z2 + - S2z * (36 - 166 * Nu + - 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + - kappa2 * (155 + Nu * (-373 + 164 * Nu))) * S2z))) + - r * (2 * params.rDOT3 * - (S1z * (18 - 110 * Nu - - 5j * (69 * kappa1 - 214 * kappa1 * Nu + - 4 * (5 + 4 * kappa1) * params.eta2) * S1z) + - 2 * (-9 + 55 * Nu) * S2z + - 5j * (69 * kappa2 - 214 * kappa2 * Nu + - 4 * (5 + 4 * kappa2) * params.eta2) * params.S2z2) + - params.PhiDOT3 * params.r3 * - (S1z * (255j - 1403j * Nu + - 10 * (28 * (3 - 8 * Nu) * Nu + - kappa1 * (51 + 2 * Nu * (-91 + 118 * Nu))) * S1z) + - 1j * (-255 + 1403 * Nu) * S2z - - 10 * (28 * (3 - 8 * Nu) * Nu + - kappa2 * (51 + 2 * Nu * (-91 + 118 * Nu))) * params.S2z2) + - 2 * params.PhiDOT2 * params.r2 * rDOT * - (S1z * (255 - 1079 * Nu + - 5j * (2 * (60 - 361 * Nu) * Nu + - kappa1 * (6 + Nu * (-47 + 164 * Nu))) * S1z) + - (-255 + 1079 * Nu) * S2z - - 5j * (2 * (60 - 361 * Nu) * Nu + - kappa2 * (6 + Nu * (-47 + 164 * Nu))) * params.S2z2) + - PhiDOT * r * params.rDOT2 * - (4j * (-114 + 781 * Nu) * S1z + - 5 * (-213 * kappa1 - 72 * Nu + 278 * kappa1 * Nu + - 8 * (7 + 44 * kappa1) * params.eta2) * params.S1z2 + - S2z * (456j + 1065 * kappa2 * S2z + - 2 * Nu * (-1562j - 5 * (-36 + 139 * kappa2 + - 4 * (7 + 44 * kappa2) * Nu) * S2z))))) - ) - - orbital_terms = ( - delta * - (52920 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * - params.r4 * combination_b4 * combination_a5 + - 840 * params.Mtot2 * params.r2 * - ((-2555489 + 7 * Nu * (820078 + Nu * (-6623390 + 4948497 * Nu))) * - params.PhiDOT5 * params.r5 + - 1j * (3537631 + 7 * Nu * (-2817653 + Nu * (-7052042 + 4017147 * Nu))) * - params.PhiDOT4 * params.r4 * rDOT + - 3 * (-1428997 + 7 * Nu * (-1230747 + Nu * (-237418 + 4061717 * Nu))) * - params.PhiDOT3 * params.r3 * params.rDOT2 + - 1j * (-5153011 + 7 * Nu * (-2375327 + 9 * Nu * (218846 + 1640185 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT3 + - (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * - PhiDOT * r * params.rDOT4 + - 3j * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * - params.rDOT5) - - 8820 * mass * params.r3 * - (2 * (111737 + Nu * (-366573 + Nu * (-618923 + 2278593 * Nu))) * - params.PhiDOT7 * params.r7 + - 2j * (101844 + Nu * (-273675 - 871630 * Nu + 2069774 * params.eta2)) * - params.PhiDOT6 * params.r6 * rDOT + - 2 * (341322 + Nu * (-1429938 + Nu * (-1206083 + 7690681 * Nu))) * - params.PhiDOT5 * params.r5 * params.rDOT2 + - 8j * (90241 + 2 * Nu * (-206022 + Nu * (-62113 + 1003558 * Nu))) * - params.PhiDOT4 * params.r4 * params.rDOT3 + - 2 * (410547 + Nu * (-2269686 + Nu * (762091 + 8400052 * Nu))) * - params.PhiDOT3 * params.r3 * params.rDOT4 + - 4j * (217935 + 2 * Nu * (-573699 + 5 * Nu * (18671 + 445748 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT5 + - (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * - PhiDOT * r * params.rDOT6 + - 24j * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * - params.rDOT7) + - 2 * params.Mtot4 * - (-4178597424j * rDOT + - 84j * rDOT * - (38468500 * params.eta3 + - 648648j * (S1z + S2z) - - 90090 * ((-24 + 155 * kappa1) * params.S1z2 + - (-24 + 155 * kappa2) * params.S2z2) - - 420 * params.eta2 * - (-122855 + - 3003 * ((2 + 11 * kappa1) * params.S1z2 - - 18 * S1z * S2z + - (2 + 11 * kappa2) * params.S2z2)) + - 3 * Nu * - (-103100846 - 1846845 * M_PI2 - - 564564j * S2z + - 6006 * (S1z * (-94j + 5 * (-52 + 63 * kappa1) * S1z) - - 20 * S1z * S2z + - 5 * (-52 + 63 * kappa2) * params.S2z2))) + - PhiDOT * r * - (1176172480 * params.eta3 + - 8 * (74084729 - - 189189 * S1z * (97j + 5 * (-8 + 117 * kappa1) * S1z) - - 189189 * S2z * (97j + 5 * (-8 + 117 * kappa2) * S2z)) - - 176400 * params.eta2 * - (11251 + - 429 * ((2 + 13 * kappa1) * params.S1z2 - - 22 * S1z * S2z + - (2 + 13 * kappa2) * params.S2z2)) + - 147 * Nu * - (-65012788 + 4485195 * M_PI2 + - 4499352j * S2z + - 10296 * (S1z * (437j + 15 * (-32 + 71 * kappa1) * S1z) - - 3860 * S1z * S2z + - 15 * (-32 + 71 * kappa2) * params.S2z2)))) - - 3 * params.Mtot3 * r * - (-4j * params.rDOT3 * - (601018232 - 1359334480 * params.eta3 - - 756756 * S1z * (6j + 115 * kappa1 * S1z) - - 756756 * S2z * (6j + 115 * kappa2 * S2z) + - 231 * Nu * - (8490448 + 503685 * M_PI2 + - 80808j * S2z + - 2184 * (S1z * (37j + 190 * kappa1 * S1z) + - 70 * S1z * S2z + - 190 * kappa2 * params.S2z2)) + - 58800 * params.eta2 * - (-62596 + - 429 * ((-1 + 5 * kappa1) * params.S1z2 - - 12 * S1z * S2z + - (-1 + 5 * kappa2) * params.S2z2))) - - 14j * params.PhiDOT2 * params.r2 * rDOT * - (-229522160 * params.eta3 + - 8 * (48303859 + - 135135 * S1z * (-17j + 2 * kappa1 * S1z) + - 135135 * S2z * (-17j + 2 * kappa2 * S2z)) + - 2520 * params.eta2 * - (100913 + - 286 * ((-31 + 5 * kappa1) * params.S1z2 - - 72 * S1z * S2z + - (-31 + 5 * kappa2) * params.S2z2)) + - 7 * Nu * - (125038052 + 2374515 * M_PI2 + - 5858424j * S2z - - 10296 * (S1z * (-569j + 25 * (-24 + 7 * kappa1) * S1z) + - 700 * S1z * S2z + - 25 * (-24 + 7 * kappa2) * params.S2z2))) + - 4 * PhiDOT * r * params.rDOT2 * - (-1095987374 + 1035895280 * params.eta3 + - 378378 * S1z * (152j + 355 * kappa1 * S1z) + - 378378 * S2z * (152j + 355 * kappa2 * S2z) - - 490 * params.eta2 * - (-5802767 + - 5148 * ((2 + 23 * kappa1) * params.S1z2 - - 42 * S1z * S2z + - (2 + 23 * kappa2) * params.S2z2)) - - 77 * Nu * - (42451610 + 1511055 * M_PI2 + - 3623256j * S2z - - 6552 * (S1z * (-553j + 5 * (18 + 37 * kappa1) * S1z) + - 965 * S1z * S2z + - 5 * (18 + 37 * kappa2) * params.S2z2))) + - 7 * params.PhiDOT3 * params.r3 * - (512893080 * params.eta3 - - 136 * (-2089567 + - 135135 * S1z * (1j + 2 * kappa1 * S1z) + - 135135 * S2z * (1j + 2 * kappa2 * S2z)) - - 560 * params.eta2 * - (2457671 + - 2574 * ((11 + 53 * kappa1) * params.S1z2 - - 84 * S1z * S2z + - (11 + 53 * kappa2) * params.S2z2)) + - 3 * Nu * - (16621605 * M_PI2 + - 8 * (27468722 + - 2681679j * S2z + - 3003 * (S1z * (893j - 840 * S1z + 800 * kappa1 * S1z) - - 3160 * S1z * S2z + - 40 * (-21 + 20 * kappa2) * params.S2z2)))))) - ) - - log_term = ( - 74954880 * delta * params.Mtot3 * - (mass * (-22j * PhiDOT * r - 24 * rDOT) + - 3 * r * - (7j * params.PhiDOT3 * params.r3 + - 14 * params.PhiDOT2 * params.r2 * rDOT - - 8j * PhiDOT * r * params.rDOT2 + - 4 * params.rDOT3)) * - np.log(r / r0) - ) - - return ( - 1j * (spin_terms + orbital_terms + log_term) - / (3.6324288e8 * np.sqrt(14) * params.r4) - ) - - else: - return 0.0 + 0.0j - -def hQC_3_m_1( - mass: float, - Nu: float, - vpnorder: int, - x: float, - S1z: float, - S2z: float, - params: KeplerVars - ) -> complex: - - delta: float = np.sqrt(1.0 - 4.0 * Nu) - EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - r0: float = b0 - - if vpnorder == 4: - return ( - -0.005555555555555556 * ( - delta * params.x3 * ( - -97.0 + 60.0 * EulerGamma - 30.0j * np.pi + - 60.0 * np.log(2.0) + 60.0 * np.log(b0) + 90.0 * np.log(x) - ) - ) / np.sqrt(14.0) - ) - - elif vpnorder == 6: - return ( - (delta * params.x4 * ( - -1552.0 + 960.0 * EulerGamma - 6487.0 * Nu + 420.0 * EulerGamma * Nu - - 480.0j * np.pi - 210.0j * Nu * np.pi + 960.0 * np.log(2.0) + - 420.0 * Nu * np.log(2.0) + 60.0 * (16.0 + 7.0 * Nu) * np.log(b0) + - 90.0 * (16.0 + 7.0 * Nu) * np.log(x) - )) / (1080.0 * np.sqrt(14.0)) - ) - - elif vpnorder == 7: - numerator_7 = ( - -9.44822373393802e-6 * ( - params.x4p5 * ( - 53132.0j * delta - 92232.0j * delta * EulerGamma + 35280.0j * delta * EulerGamma**2 - - 46116.0 * delta * np.pi + 35280.0 * delta * EulerGamma * np.pi - 2940.0j * delta * np.pi**2 + - 57036.0 * S1z + 57036.0 * delta * S1z - 35280.0 * EulerGamma * S1z - 35280.0 * delta * EulerGamma * S1z - - 114513.0 * Nu * S1z - 143031.0 * delta * Nu * S1z + 97020.0 * EulerGamma * Nu * S1z + - 114660.0 * delta * EulerGamma * Nu * S1z + 17640.0j * np.pi * S1z + 17640.0j * delta * np.pi * S1z - - 48510.0j * Nu * np.pi * S1z - 57330.0j * delta * Nu * np.pi * S1z - 57036.0 * S2z + - 57036.0 * delta * S2z + 35280.0 * EulerGamma * S2z - 35280.0 * delta * EulerGamma * S2z + - 114513.0 * Nu * S2z - 143031.0 * delta * Nu * S2z - 97020.0 * EulerGamma * Nu * S2z + - 114660.0 * delta * EulerGamma * Nu * S2z - 17640.0j * np.pi * S2z + 17640.0j * delta * np.pi * S2z + - 48510.0j * Nu * np.pi * S2z - 57330.0j * delta * Nu * np.pi * S2z - 92232.0j * delta * np.log(2.0) + - 70560.0j * delta * EulerGamma * np.log(2.0) + 35280.0 * delta * np.pi * np.log(2.0) - - 35280.0 * S1z * np.log(2.0) - 35280.0 * delta * S1z * np.log(2.0) + 97020.0 * Nu * S1z * np.log(2.0) + - 114660.0 * delta * Nu * S1z * np.log(2.0) + 35280.0 * S2z * np.log(2.0) - 35280.0 * delta * S2z * np.log(2.0) - - 97020.0 * Nu * S2z * np.log(2.0) + 114660.0 * delta * Nu * S2z * np.log(2.0) + - 35280.0j * delta * np.log(2.0)**2 - 2490264.0j * delta * np.log(3.0) + - 1905120.0j * delta * EulerGamma * np.log(3.0) + 952560.0 * delta * np.pi * np.log(3.0) - - 317520.0 * S1z * np.log(3.0) - 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + - 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - 317520.0 * delta * S2z * np.log(3.0) - - 1508220.0 * Nu * S2z * np.log(3.0) + 396900.0 * delta * Nu * S2z * np.log(3.0) + - 1905120.0j * delta * np.log(2.0) * np.log(3.0) + 952560.0j * delta * np.log(3.0)**2 + - 35280.0j * delta * np.log(b0)**2 + 21840.0j * delta * np.log(r0) - 138348.0j * delta * np.log(x) + - 105840.0j * delta * EulerGamma * np.log(x) + 52920.0 * delta * np.pi * np.log(x) - 52920.0 * S1z * np.log(x) - - 52920.0 * delta * S1z * np.log(x) + 145530.0 * Nu * S1z * np.log(x) + 171990.0 * delta * Nu * S1z * np.log(x) + - 52920.0 * S2z * np.log(x) - 52920.0 * delta * S2z * np.log(x) - 145530.0 * Nu * S2z * np.log(x) + - 171990.0 * delta * Nu * S2z * np.log(x) + 105840.0j * delta * np.log(2.0) * np.log(x) + - 79380.0j * delta * np.log(x)**2 + - 588.0 * np.log(b0) * ( - -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * np.pi + - 15.0 * (-4.0 - 4.0 * delta + 11.0 * Nu + 13.0 * delta * Nu) * S1z + - 60.0 * S2z - 60.0 * delta * S2z - 165.0 * Nu * S2z + 195.0 * delta * Nu * S2z + - 120.0j * delta * np.log(2.0) + 180.0j * delta * np.log(x) - ) - ) - ) - ) - return numerator_7 / np.sqrt(14.0) - - else: - return 0.0 + 0.0j - -def hl_3_m_1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - # Maintaining the logic of the C-source error check - raise ValueError( - "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module - go_component: complex = hGO_3_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -1 * Phi) -> exp(-i * Phi) - phase_factor: complex = np.exp(-1j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_3_m_min1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - # Maintaining the logic of the C-source error check - raise ValueError( - "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module - go_component: complex = hGO_3_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - amplitude_factor = (go_component + qc_component).conjugate() - - # cpolar(1, 1 * Phi) -> exp(i * Phi) - phase_factor: complex = np.exp(1j * Phi) - - return pre_factor * amplitude_factor * phase_factor - -# H44 - -def hGO_4_m_4( - mass: float, - Nu: float, - r: float, - rDOT: float, - PhiDOT: float, - vpnorder: int, - S1z: float, - S2z: float, - params: KeplerVars, - ) -> complex: - delta = np.sqrt((1 - 4 * Nu) + 0j) - 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 - - if vpnorder == 2: - return ( - np.sqrt(0.7142857142857143) * (-1 + 3 * Nu) * - (7 * params.Mtot2 + 6 * params.r2 * combination_a4 + - 3 * mass * r * - (17 * params.PhiDOT2 * params.r2 + - 18j * PhiDOT * r * rDOT - 6 * params.rDOT2)) - / (36.0 * params.r2) - ) - - elif vpnorder == 4: - return ( - (40 * params.Mtot3 * (314 - 987 * Nu + 195 * params.eta2) - - 60 * (23 - 159 * Nu + 291 * params.eta2) * params.r3 * - combination_b * combination_a5 + - params.Mtot2 * r * - ((53143 - 199660 * Nu + 127500 * params.eta2) * params.PhiDOT2 * params.r2 + - 24j * (967 - 4615 * Nu + 5935 * params.eta2) * PhiDOT * r * rDOT - - 10 * (290 - 2033 * Nu + 4365 * params.eta2) * params.rDOT2) - - 3 * mass * params.r2 * - ((613 - 920 * Nu + 6420 * params.eta2) * params.PhiDOT4 * params.r4 - - 8j * (-976 + 1745 * Nu + 3150 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + - 2 * (-6141 + 8980 * Nu + 31500 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 4j * (-1853 + 1730 * Nu + 13230 * params.eta2) * PhiDOT * r * params.rDOT3 - - 20 * (-83 + 30 * Nu + 762 * params.eta2) * params.rDOT4)) - / (1584.0 * np.sqrt(35) * params.r3) - ) - - elif vpnorder == 5: - term1 = ( - params.Mtot2 * Nu * - (6 * mass * (-43j * PhiDOT * r + 9 * rDOT) + - r * (-734j * params.PhiDOT3 * params.r3 + - 129 * params.PhiDOT2 * params.r2 * rDOT + - 156j * PhiDOT * r * params.rDOT2 - - 26 * params.rDOT3)) - / (24.0 * np.sqrt(35) * params.r3) - ) - - # Henry et al. ecc spin terms - term2 = ( - params.Mtot2 * - (-3j * params.PhiDOT2 * params.r3 * rDOT * - ((-250 + 1221 * Nu - 1512 * params.eta2 + - delta * (-250 + 849 * Nu)) * S1z + - (-250 + delta * (250 - 849 * Nu) + 1221 * Nu - - 1512 * params.eta2) * S2z) - - 2 * mass * PhiDOT * r * - ((-130 + 757 * Nu - 1224 * params.eta2 + - delta * (-130 + 513 * Nu)) * S1z + - (-130 + delta * (130 - 513 * Nu) + 757 * Nu - - 1224 * params.eta2) * S2z) - - 2j * mass * rDOT * - ((-100 + 577 * Nu - 864 * params.eta2 + - delta * (-100 + 333 * Nu)) * S1z + - (-100 + delta * (100 - 333 * Nu) + 577 * Nu - - 864 * params.eta2) * S2z) - - 6 * params.PhiDOT3 * params.r4 * - ((-65 + 263 * Nu - 291 * params.eta2 + - delta * (-65 + 282 * Nu)) * S1z + - (-65 + delta * (65 - 282 * Nu) + 263 * Nu - - 291 * params.eta2) * S2z) + - 12 * PhiDOT * params.r2 * params.rDOT2 * - ((-40 + 201 * Nu - 252 * params.eta2 + - delta * (-40 + 129 * Nu)) * S1z + - (-40 + delta * (40 - 129 * Nu) + 201 * Nu - - 252 * params.eta2) * S2z) + - 6j * r * params.rDOT3 * - ((-20 + 107 * Nu - 144 * params.eta2 + - delta * (-20 + 63 * Nu)) * S1z + - (-20 + delta * (20 - 63 * Nu) + 107 * Nu - - 144 * params.eta2) * S2z)) - / (72.0 * np.sqrt(35) * params.r3) - ) - - return term1 + term2 - - elif vpnorder == 6: - term1 = ( - (10 * params.Mtot4 * - (-4477296 + 12734393 * Nu - 6895 * params.eta2 + 1043805 * params.eta3) + - 3150 * (-367 + 4337 * Nu - 17462 * params.eta2 + 23577 * params.eta3) * - params.r4 * combination_b2 * combination_a6 + - 2 * params.Mtot3 * r * - ((-36967579 + 245501977 * Nu - 459916170 * params.eta2 + - 150200680 * params.eta3) * params.PhiDOT2 * params.r2 + - 4j * (7571073 - 10780154 * Nu - 56898800 * params.eta2 + - 43665510 * params.eta3) * PhiDOT * r * rDOT - - 10 * (1283609 - 5800627 * Nu + 3725295 * params.eta2 + - 4771935 * params.eta3) * params.rDOT2) - - params.Mtot2 * params.r2 * - ((-28258134 + 3245207 * Nu + 144051250 * params.eta2 + - 136991820 * params.eta3) * params.PhiDOT4 * params.r4 - - 24j * (2371982 - 7733376 * Nu - 7948185 * params.eta2 + - 9074870 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + - 7 * (6557973 - 50558069 * Nu + 59901380 * params.eta2 + - 104752320 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 168j * (52044 - 1084807 * Nu + 1849450 * params.eta2 + - 4171730 * params.eta3) * PhiDOT * r * params.rDOT3 - - 35 * (1083 - 1246819 * Nu + 2524240 * params.eta2 + - 5995845 * params.eta3) * params.rDOT4) - - 105 * mass * params.r3 * - ((116396 - 551405 * Nu + 560658 * params.eta2 + - 293036 * params.eta3) * params.PhiDOT6 * params.r6 + - 2j * (158192 - 670661 * Nu + 177718 * params.eta2 + - 2163976 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + - (-393665 + 1322392 * Nu + 1589680 * params.eta2 - - 8622660 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 - - 8j * (-23048 + 209397 * Nu - 487057 * params.eta2 + - 260396 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 - - (630647 - 3391000 * Nu + 2501958 * params.eta2 + - 7664096 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - - 2j * (218975 - 1037408 * Nu + 148970 * params.eta2 + - 3699480 * params.eta3) * PhiDOT * r * params.rDOT5 + - 10 * (10233 - 44864 * Nu - 13050 * params.eta2 + - 203280 * params.eta3) * params.rDOT6)) - / (1.44144e6 * np.sqrt(35) * params.r4) - ) - - # Henry et al. ecc spin terms - term2 = ( - np.sqrt(0.7142857142857143) * params.Mtot3 * (-1 + 3 * Nu) * - (12 * mass + - r * (53 * params.PhiDOT2 * params.r2 + - 26j * PhiDOT * r * rDOT - 8 * params.rDOT2)) * - (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + - S2z * (4 * Nu * S1z + kappa2 * S2z - delta * kappa2 * S2z - - 2 * kappa2 * Nu * S2z)) - / (48.0 * params.r4) - ) - - return term1 + term2 - - elif vpnorder == 7: - # Henry et al. ecc+spin terms - return ( - params.Mtot2 * - (14 * params.Mtot2 * - (120 * params.eta3 * - (24635 * PhiDOT * r + 18657j * rDOT) * - (S1z + S2z) - - 60 * (10039 * PhiDOT * r + 7706j * rDOT) * - (S1z + delta * S1z + S2z - delta * S2z) + - 5 * Nu * - (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 * params.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 * params.r2 * - (1680 * params.eta3 * - (2833 * params.PhiDOT5 * params.r5 + - 18796j * params.PhiDOT4 * params.r4 * rDOT - - 13185 * params.PhiDOT3 * params.r3 * params.rDOT2 + - 1186j * params.PhiDOT2 * params.r2 * params.rDOT3 - - 5863 * PhiDOT * r * params.rDOT4 - - 2100j * params.rDOT5) * - (S1z + S2z) - - 1050 * - (454 * params.PhiDOT5 * params.r5 + - 1195j * params.PhiDOT4 * params.r4 * rDOT - - 1950 * params.PhiDOT3 * params.r3 * params.rDOT2 - - 442j * params.PhiDOT2 * params.r2 * params.rDOT3 - - 384 * PhiDOT * r * params.rDOT4 - - 184j * params.rDOT5) * - (S1z + delta * S1z + S2z - delta * S2z) - - 6 * params.eta2 * - (2 * params.PhiDOT5 * params.r5 * - (-2459811j + 35 * (26517 + 10223 * delta) * S1z + - (928095 - 357805 * delta) * S2z) - - 10j * params.rDOT5 * - (35291j + 28 * (2183 + 1155 * delta) * S1z + - (61124 - 32340 * delta) * S2z) + - 1j * params.PhiDOT2 * params.r2 * params.rDOT3 * - (917901j + 1120 * (-191 + 1616 * delta) * S1z - - 1120 * (191 + 1616 * delta) * S2z) + - 5 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (85426j + 7 * (-148363 + 4365 * delta) * S1z - - 7 * (148363 + 4365 * delta) * S2z) - - 4 * PhiDOT * r * params.rDOT4 * - (280067j + 70 * (5844 + 4817 * delta) * S1z - - 70 * (-5844 + 4817 * delta) * S2z) + - 1j * params.PhiDOT4 * params.r4 * rDOT * - (10375501j + 70 * (65831 + 22871 * delta) * S1z - - 70 * (-65831 + 22871 * delta) * S2z)) + - Nu * (-40j * params.rDOT5 * - (12203j + 42 * (843 + 656 * delta) * S1z - - 42 * (-843 + 656 * delta) * S2z) + - 4j * params.PhiDOT2 * params.r2 * params.rDOT3 * - (-266071j + 210 * (-2279 + 1010 * delta) * S1z - - 210 * (2279 + 1010 * delta) * S2z) - - 16 * PhiDOT * r * params.rDOT4 * - (58753j + 105 * (2030 + 1947 * delta) * S1z - - 105 * (-2030 + 1947 * delta) * S2z) + - params.PhiDOT5 * params.r5 * - (-8997592j + 105 * (39959 + 15835 * delta) * S1z - - 105 * (-39959 + 15835 * delta) * S2z) - - 10 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (254228j + 21 * (65293 + 34551 * delta) * S1z - - 21 * (-65293 + 34551 * delta) * S2z) + - 2j * params.PhiDOT4 * params.r4 * rDOT * - (12351083j + 105 * (43193 + 37913 * delta) * S1z - - 105 * (-43193 + 37913 * delta) * S2z))) + - mass * r * - (2520 * params.eta3 * - (8036 * params.PhiDOT3 * params.r3 + - 41814j * params.PhiDOT2 * params.r2 * rDOT - - 30537 * PhiDOT * r * params.rDOT2 - - 9064j * params.rDOT3) * - (S1z + S2z) - - 210 * - (11849 * params.PhiDOT3 * params.r3 + - 31868j * params.PhiDOT2 * params.r2 * rDOT + - 3572 * PhiDOT * r * params.rDOT2 + - 1508j * params.rDOT3) * - (S1z + delta * S1z + S2z - delta * S2z) - - 12 * params.eta2 * - (1j * params.PhiDOT2 * params.r2 * rDOT * - (-1150397j + 35 * (154765 + 157449 * delta) * S1z + - 5416775 * S2z - 5510715 * delta * S2z) - - PhiDOT * r * params.rDOT2 * - (2306552j + 35 * (4931 + 110733 * delta) * S1z + - 172585 * S2z - 3875655 * delta * S2z) + - params.PhiDOT3 * params.r3 * - (12461121j + 35 * (18331 + 87381 * delta) * S1z + - 641585 * S2z - 3058335 * delta * S2z) - - 25j * params.rDOT3 * - (36676j + 35 * (137 + 1053 * delta) * S1z + - 4795 * S2z - 36855 * delta * S2z)) + - Nu * (-200j * params.rDOT3 * - (-2501j + 42 * (-308 + 51 * delta) * S1z - - 42 * (308 + 51 * delta) * S2z) + - 2 * PhiDOT * r * params.rDOT2 * - (-1951984j - 105 * (-37907 + 14661 * delta) * S1z + - 105 * (37907 + 14661 * delta) * S2z) + - 8j * params.PhiDOT2 * params.r2 * rDOT * - (-10005028j + 105 * (40991 + 31689 * delta) * S1z - - 105 * (-40991 + 31689 * delta) * S2z) + - params.PhiDOT3 * params.r3 * - (88418488j + 105 * (57793 + 266391 * delta) * S1z - - 105 * (-57793 + 266391 * delta) * S2z)))) - / (332640.0 * np.sqrt(35) * params.r4) - ) - - else: - return 0.0 + 0.0j - -def hQC_4_m_4( - mass: float, - Nu: float, - vpnorder: int, - x: float, - S1z: float, - S2z: float, - params: KeplerVars - ) -> complex: - EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - - if vpnorder == 5: - # Pre-factor: Complex(0, 0.07407407407407407) / sqrt(35) - return ( - (0.07407407407407407j * params.x3p5 * ( - 1888.0 - 960.0 * EulerGamma - 5661.0 * Nu + 2880.0 * EulerGamma * Nu + - 480.0j * np.pi - 1440.0j * Nu * np.pi - 2880.0 * np.log(2.0) + - 8640.0 * Nu * np.log(2.0) + 960.0 * (-1.0 + 3.0 * Nu) * np.log(b0) + - 1440.0 * (-1.0 + 3.0 * Nu) * np.log(x) - )) / np.sqrt(35.0) - ) - - elif vpnorder == 7: - # Pre-factor: Complex(0, 1.0020843354176687e-6) / sqrt(35) - return ( - (1.0020843354176687e-6j * params.x4p5 * ( - -752360448.0 + 382556160.0 * EulerGamma + 2620364605.0 * Nu - - 1333248000.0 * EulerGamma * Nu - 898312500.0 * params.eta2 + - 458035200.0 * EulerGamma * params.eta2 - 191278080.0j * np.pi + - 666624000.0j * Nu * np.pi - 229017600.0j * params.eta2 * np.pi + - 1147668480.0 * np.log(2.0) - 3999744000.0 * Nu * np.log(2.0) + - 1374105600.0 * params.eta2 * np.log(2.0) + - 215040.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(b0) + - 322560.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(x) - )) / np.sqrt(35.0) - ) - - else: - return 0.0 + 0.0j - -def hl_4_m_4( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure - go_component: complex = hGO_4_m_4( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params - ) - qc_component: complex = hQC_4_m_4( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -4 * Phi) -> exp(-i * 4 * Phi) - phase_factor: complex = np.exp(-4j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_4_m_min4( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure - go_component: complex = hGO_4_m_4( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params - ) - qc_component: complex = hQC_4_m_4( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - amplitude_factor = (go_component + qc_component).conjugate() - - # cpolar(1, 4 * Phi) -> exp(i * 4 * Phi) - phase_factor: complex = np.exp(4j * Phi) - - return pre_factor * amplitude_factor * phase_factor - -# H43 - -def hGO_4_m_3( - mass: float, - Nu: float, - r: float, - rDOT: float, - PhiDOT: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars, - ) -> complex: - delta = np.sqrt(1 - 4 * Nu) - kappa1 = 1.0 - kappa2 = 1.0 - - if vpnorder == 3: - return ( - 0.16666666666666666j * delta * mass * (-1 + 2 * Nu) * PhiDOT * - (4 * mass + - r * (23 * params.PhiDOT2 * params.r2 + - 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) - / (np.sqrt(70) * r) - ) - - elif vpnorder == 4: - return ( - -0.041666666666666664j * np.sqrt(0.35714285714285715) * - params.Mtot2 * Nu * - (4 * mass + - r * (23 * params.PhiDOT2 * params.r2 + - 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) * - ((-1 + delta) * S1z + S2z + delta * S2z) - / params.r3 - ) - - elif vpnorder == 5: - return ( - 0.0012626262626262627j * delta * mass * PhiDOT * - (2 * params.Mtot2 * (972 - 2293 * Nu + 1398 * params.eta2) + - 2 * mass * r * - ((1788 - 9077 * Nu + 13416 * params.eta2) * params.PhiDOT2 * params.r2 + - 3j * (-2796 + 5299 * Nu + 1622 * params.eta2) * PhiDOT * r * rDOT - - 2 * (-1200 + 2545 * Nu + 162 * params.eta2) * params.rDOT2) - - 3 * params.r2 * - ((-524 - 489 * Nu + 6392 * params.eta2) * params.PhiDOT4 * params.r4 + - 4j * (796 - 1864 * Nu + 133 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + - 42 * (-51 + 94 * Nu + 56 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 4j * (-229 + 366 * Nu + 358 * params.eta2) * PhiDOT * r * params.rDOT3 - - 4 * (-43 + 62 * Nu + 80 * params.eta2) * params.rDOT4)) - / (np.sqrt(70) * params.r2) - ) - - elif vpnorder == 6: - term1 = ( - delta * params.Mtot2 * Nu * PhiDOT * - (6 * mass * (181 * PhiDOT * r - 89j * rDOT) + - r * (4847 * params.PhiDOT3 * params.r3 - - 7338j * params.PhiDOT2 * params.r2 * rDOT - - 408 * PhiDOT * r * params.rDOT2 + - 112j * params.rDOT3)) - / (180.0 * np.sqrt(70) * params.r2) - ) - - # Henry et al. ecc spin terms - term2 = ( - -0.0006313131313131314j * params.Mtot2 * - (2 * params.Mtot2 * - ((-440 + 6801 * Nu - 1428 * params.eta2 + - delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S1z + - (440 - 6801 * Nu + 1428 * params.eta2 + - delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S2z) - - 2 * mass * r * - (-3j * PhiDOT * r * rDOT * - (delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S1z - - 5 * (264 - 311 * Nu + 823 * params.eta2) * S1z + - delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S2z + - 5 * (264 - 311 * Nu + 823 * params.eta2) * S2z) - - 2 * params.rDOT2 * - ((220 + 1659 * Nu - 1512 * params.eta2 + - delta * (220 - 3067 * Nu + 240 * params.eta2)) * S1z + - (-220 - 1659 * Nu + 1512 * params.eta2 + - delta * (220 - 3067 * Nu + 240 * params.eta2)) * S2z) + - 2 * params.PhiDOT2 * params.r2 * - ((1826 - 19530 * Nu + 20145 * params.eta2 + - delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S1z + - (-1826 + 19530 * Nu - 20145 * params.eta2 + - delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S2z)) - - 3 * params.r2 * - (3080j * params.PhiDOT3 * params.r3 * rDOT * - ((1 + delta) * S1z + (-1 + delta) * S2z) + - 2 * params.eta2 * - (129 * params.PhiDOT2 * params.r2 * params.rDOT2 * - (41 * S1z + 23 * delta * S1z - 41 * S2z + 23 * delta * S2z) - - 2 * params.rDOT4 * - (149 * S1z + 75 * delta * S1z - 149 * S2z + 75 * delta * S2z) + - 2j * PhiDOT * r * params.rDOT3 * - (925 * S1z + 491 * delta * S1z - 925 * S2z + 491 * delta * S2z) - - 1j * params.PhiDOT3 * params.r3 * rDOT * - (-2105 * S1z + 753 * delta * S1z + 2105 * S2z + 753 * delta * S2z) + - params.PhiDOT4 * params.r4 * - (11617 * S1z + 4847 * delta * S1z - 11617 * S2z + 4847 * delta * S2z)) + - Nu * (16 * params.PhiDOT4 * params.r4 * - (-413 * S1z + 127 * delta * S1z + 413 * S2z + 127 * delta * S2z) - - 2 * params.rDOT4 * - (-301 * S1z + 213 * delta * S1z + 301 * S2z + 213 * delta * S2z) + - 2j * PhiDOT * r * params.rDOT3 * - (-1625 * S1z + 1009 * delta * S1z + 1625 * S2z + 1009 * delta * S2z) + - 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * - (-2587 * S1z + 1267 * delta * S1z + 2587 * S2z + 1267 * delta * S2z) - - 2j * params.PhiDOT3 * params.r3 * rDOT * - (3635 * S1z + 7981 * delta * S1z - 3635 * S2z + 7981 * delta * S2z)))) - / (np.sqrt(70) * params.r4) - ) - - return term1 + term2 - - elif vpnorder == 7: - # Henry et al. ecc+spin terms - spin_terms = ( - 5005 * params.Mtot2 * - (-24 * PhiDOT * params.r2 * params.rDOT2 * - (1j * (-11 + 48 * Nu) * S1z + - 6 * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + - S2z * (11j - 48j * Nu - - 6 * (-5 + 4 * kappa2) * params.eta2 * S2z)) + - 4 * r * params.rDOT3 * - ((-11 + 93 * Nu) * S1z - - 6j * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + - S2z * (11 - 93 * Nu + - 6j * (-5 + 4 * kappa2) * params.eta2 * S2z)) + - 4 * mass * rDOT * - ((22 - 111 * Nu) * S1z + - 6j * (15 + 8 * kappa1) * params.eta2 * params.S1z2 + - S2z * (-22 + 111 * Nu - - 6j * (15 + 8 * kappa2) * params.eta2 * S2z)) + - 6j * params.PhiDOT2 * params.r3 * rDOT * - (1j * (-121 + 963 * Nu) * S1z + - 6 * (-55 * params.eta2 + - kappa1 * (-5 + 30 * Nu + 4 * params.eta2)) * params.S1z2 + - S2z * (121j + 30 * kappa2 * S2z - - 6 * (-55 + 4 * kappa2) * params.eta2 * S2z - - 9 * Nu * (107j + 20 * kappa2 * S2z))) + - 2 * mass * PhiDOT * r * - (-1j * (-121 + 633 * Nu) * S1z + - 6 * (180 * params.eta2 + - kappa1 * (9 - 54 * Nu + 28 * params.eta2)) * params.S1z2 - - S2z * (121j + 54 * kappa2 * S2z + - 24 * (45 + 7 * kappa2) * params.eta2 * S2z - - 3 * Nu * (211j + 108 * kappa2 * S2z))) + - params.PhiDOT3 * params.r4 * - (-1j * (-649 + 4767 * Nu) * S1z + - 6 * (820 * params.eta2 + - kappa1 * (75 - 450 * Nu + 364 * params.eta2)) * params.S1z2 - - S2z * (649j + 450 * kappa2 * S2z + - 24 * (205 + 91 * kappa2) * params.eta2 * S2z - - 3 * Nu * (1589j + 900 * kappa2 * S2z)))) - ) - - orbital_terms = ( - delta * - (2 * mass * PhiDOT * params.r3 * - (10 * (234744 - 1010534 * Nu + 1024443 * params.eta2 + - 5451096 * params.eta3) * params.PhiDOT4 * params.r4 - - 3j * (-2426804 + 1512854 * Nu + 4994115 * params.eta2 + - 610960 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + - (-30341028 + 23936528 * Nu + 89326545 * params.eta2 + - 19329660 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 21j * (-668008 + 803028 * Nu + 1908955 * params.eta2 + - 540370 * params.eta3) * PhiDOT * r * params.rDOT3 - - 14 * (-172143 + 155683 * Nu + 680580 * params.eta2 + - 111840 * params.eta3) * params.rDOT4) - - 105 * PhiDOT * params.r4 * - ((-8280 + 24681 * Nu - 151973 * params.eta2 + - 624074 * params.eta3) * params.PhiDOT6 * params.r6 + - 2j * (-32208 + 248485 * Nu - 524074 * params.eta2 + - 24546 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + - 2 * (48924 - 239802 * Nu + 137447 * params.eta2 + - 358156 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + - 4j * (174 + 24488 * Nu - 102039 * params.eta2 + - 44882 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + - 3 * (10455 - 56490 * Nu + 84504 * params.eta2 + - 54016 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 + - 2j * (11175 - 52698 * Nu + 57436 * params.eta2 + - 60808 * params.eta3) * PhiDOT * r * params.rDOT5 - - 6 * (829 - 3726 * Nu + 3480 * params.eta2 + - 4640 * params.eta3) * params.rDOT6) + - params.Mtot2 * r * - (20020 * params.rDOT3 * (S1z + S2z) * - (-11 + 71 * Nu + 30j * params.eta2 * (S1z + S2z)) - - 52 * PhiDOT * r * params.rDOT2 * - (129150 * params.eta3 + - 7 * Nu * (31961 + 8580j * S1z + 8580j * S2z) - - 3j * (32671j + 8470 * S1z + 8470 * S2z) - - 35 * params.eta2 * - (33313 + 1980 * params.S1z2 + 3960 * S1z * S2z + 1980 * params.S2z2)) + - params.PhiDOT3 * params.r3 * - (-10566168 - 70869960 * params.eta3 + - 3248245j * S1z + 2252250 * kappa1 * params.S1z2 + - 3248245j * S2z + 2252250 * kappa2 * params.S2z2 - - 7 * Nu * - (4818166 + 2480335j * S1z + 1287000 * kappa1 * params.S1z2 + - 2480335j * S2z + 1287000 * kappa2 * params.S2z2) + - 3850 * params.eta2 * - (38873 + 78 * (7 + 30 * kappa1) * params.S1z2 - - 3588 * S1z * S2z + - 78 * (7 + 30 * kappa2) * params.S2z2)) - - 2j * params.PhiDOT2 * params.r2 * rDOT * - (6531280 * params.eta3 + - 3 * (5382288 + 605605j * S1z + 150150 * kappa1 * params.S1z2 + - 605605j * S2z + 150150 * kappa2 * params.S2z2) + - 210 * params.eta2 * - (322144 + 2145 * (1 + 4 * kappa1) * params.S1z2 - - 12870 * S1z * S2z + - 2145 * (1 + 4 * kappa2) * params.S2z2) - - 21 * Nu * - (2826484 + 85800 * kappa1 * params.S1z2 + - 515515j * S2z + 85800 * kappa2 * params.S2z2 - - 5005 * S1z * (-103j + 60 * S2z)))) - - 26 * params.Mtot3 * - (770 * rDOT * - (-90j * params.eta2 * params.S1z2 + - S2z * (-22 + 67 * Nu - 90j * params.eta2 * S2z) + - S1z * (-22 + Nu * (67 + 300j * S2z) - 180j * params.eta2 * S2z)) + - PhiDOT * r * - (-38076 + 174720 * params.eta3 - - 46585j * S1z - 20790 * kappa1 * params.S1z2 - - 46585j * S2z - 20790 * kappa2 * params.S2z2 - - 1260 * params.eta2 * - (158 + 33 * (5 + 2 * kappa1) * params.S1z2 + - 198 * S1z * S2z + - 33 * (5 + 2 * kappa2) * params.S2z2) + - 7 * Nu * - (6188 + 11880 * kappa1 * params.S1z2 + - 21505j * S2z + 11880 * kappa2 * params.S2z2 + - 55 * S1z * (391j + 744 * S2z))))) - ) - - return ( - -1.3875013875013875e-6j * mass * - (spin_terms + orbital_terms) - / (np.sqrt(70) * params.r4) - ) - - else: - return Complex(0.0, 0.0) \ No newline at end of file diff --git a/build/lib/esigmapy/python_codes/esigma_go_terms_draft.py b/build/lib/esigmapy/python_codes/esigma_go_terms_draft.py deleted file mode 100644 index 9c674c3..0000000 --- a/build/lib/esigmapy/python_codes/esigma_go_terms_draft.py +++ /dev/null @@ -1,1696 +0,0 @@ -import numpy as np -from dataclasses import dataclass - -# Storing constants -M_PI = np.pi -M_PI2 = M_PI ** 2 -LOG2 = np.log(2) - -#---------------------------------------------------- -def rect(r: float, phi: float) -> complex: - """Convert polar coordinates to rectangular form.""" - return r * np.exp(1j * phi) -#---------------------------------------------------- - -@dataclass -class CommonVars: - 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): - """Similarly for QC functions""" - def decorator(func): - H_QC_LM_FUNCS[(l, m)] = func - return func - return decorator - -# 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 + np.log(64.0) + 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) - - -# H33 - -# def hGO_3_m_3( -# total_mass: float, -# eta: float, -# r: float, -# rDOT: float, -# PhiDOT: float, -# vpnorder: int, -# S1z: float, -# S2z: float, -# x: float, -# ) -> complex: -# delta = np.sqrt(1 - 4 * eta) -# kappa1 = 1.0 -# kappa2 = 1.0 -# r0 = 2 * total_mass / np.exp(0.5) - -# 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) - -# def hQC_3_m_3( -# 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 - -# 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 * np.log(2.0) + 60.0 * np.log(3.0) + -# 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 * np.log(2.0) + 43740.0 * eta * np.log(2.0) - -# 116640.0 * np.log(3.0) + 43740.0 * eta * np.log(3.0) + -# 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 * np.log(2.0) + 120.0j * delta * np.log(3.0) + -# 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 * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + -# 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - -# 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * eta * S1z * np.log(2.0) + -# 396900.0 * delta * eta * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - -# 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * eta * S2z * np.log(2.0) + -# 396900.0 * delta * eta * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - -# 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + -# 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - -# 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * eta * S1z * np.log(3.0) + -# 396900.0 * delta * eta * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - -# 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * eta * S2z * np.log(3.0) + -# 396900.0 * delta * eta * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + -# 952560.0j * delta * (np.log(3.0)**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 * np.log(2.0) * logx + -# 2857680.0j * delta * np.log(3.0) * logx + -# 2143260.0j * delta * (logx**2) + -# log_b0_term -# )) -# return numerator_7 / (2352.0 * np.sqrt(210.0)) - -# else: -# return complex(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, m)) - QC_func = H_QC_LM_FUNCS.get((l, 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: 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/build/lib/esigmapy/python_codes/esigma_pn_inspiral.py b/build/lib/esigmapy/python_codes/esigma_pn_inspiral.py deleted file mode 100644 index eb58620..0000000 --- a/build/lib/esigmapy/python_codes/esigma_pn_inspiral.py +++ /dev/null @@ -1,2844 +0,0 @@ -""" -esigma_pn_inspiral.py -==================== -Python translation of ESIGMA_PNInspiral.c - -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 = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant -LOG2 = 0.693147180559945309417232121458 -LOG3 = 1.09861228866810969139524523692 -LOG4 = 1.38629436111989061883446424292 -LOG5 = 1.60943791243410037460075933323 -REAL8_FAIL_NAN = float('nan') - -# ------ 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 - e2 = e * e - pre = 192.0 * np.sqrt(ef) / (985.0 * e2) - return pre * (np.sqrt(ef) * phi_e(e) - phi_e_tilde(e)) - - -def psi_e_rad(e: float) -> float: - ef = 1.0 - e * e - e2 = e * e - pf1 = 18816.0 / (55691.0 * e2 * np.sqrt(ef)) - pf2 = 16382.0 * np.sqrt(ef) / (55691.0 * e2) - b1 = np.sqrt(ef) * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) - b2 = np.sqrt(ef) * psi_e(e) - psi_e_tilde(e) - return pf1 * b1 + pf2 * b2 - - -def zed_e_rad(e: float) -> float: - ef = 1.0 - e * e - e2 = e * e - pf1 = 924.0 / (19067.0 * e2 * np.sqrt(ef)) - pf2 = 12243.0 * np.sqrt(ef) / (76268.0 * e2) - b1 = -ef * np.sqrt(ef) * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) - b2 = np.sqrt(ef) * zed_e(e) - zed_e_tilde(e) - return pf1 * b1 + pf2 * b2 - - -def kappa_e_rad(e: float) -> float: - ef = 1.0 - e * e - e2 = e * e - den = 769.0 / 96.0 - 3059665.0 * LOG2 / 700566.0 + 8190315.0 * LOG3 / 1868176.0 - pf = np.sqrt(ef) / e2 - b1 = np.sqrt(ef) * 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)""" - 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) - e0 = 2.0 * eta * 96.0 / 15.0 - return (num / den) if e else e0 - -## --------- 1PN terms ------------ - -def x_dot_1pn(e: float, eta: float) -> float: - """Eq. (A27)""" - 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) - e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) - return (num / den) if e else e0 - -## --------- 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)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - ef = 1.0 - e2 - M = m1 + m2 - if e: - 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 * ef**5 * M**4) - else: - 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) - - -def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: - """Eq. (A28)""" - pre = eta * x**6 * np.sqrt(x) - if e: - 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) -> float: - """Eq. (A29)""" - - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e4 * e4 - - ef = 1.0 - e2 - eta2 = eta * eta - - # Small-e limit - e0_lim = eta * (68206.0 / 2835.0 + - 27322.0 * eta / 315.0 + - 1888.0 * eta2 / 45.0) - - if e: return e0_lim - else: - 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 - 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**2 - pre = 64.0 * eta / 5.0 - - if e: - 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) - else: - return pre * ( - ((1 + 80*kappa1)*m1*m1*S1z*S1z + - 158*m1*m2*S1z*S2z + - (1 + 80*kappa2)*m2*m2*S2z*S2z) - ) / (16.0 * M2) - -## --------- 2.5PN terms ------------ - -def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: - """See Huerta et al.""" - ef = 1.0 - e*e - ef2 = ef * ef - e2 = e * e - pre = eta * x**7 * np.sqrt(x) - - if 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)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e6 * e2 - ef = 1.0 - e2 - ef_sqrt = np.sqrt(ef) - M2 = (m1 + m2)**2 - M4 = M2 * M2 - M6 = M4 * M2 - pre = 64.0 * eta / 5.0 - - if e: - 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 - ) - -## --------- 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 e: - 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.)""" - - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e4 * e4 - e10 = e8 * e2 - - ef = 1.0 - e2 - 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 - - 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)""" - 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 - M2 = (m1 + m2)**2 - M4 = M2 * M2 - pre = 64.0 * eta / 5.0 - - if e: - 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 - - 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 - - M = m1 + m2 - M2 = M * M - M4 = M2 * M2 - M6 = M4 * 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) - ) - - # --- 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 e 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 e 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 e 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 e else val_e0 - -## --------- 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 e 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 e else val_e0 - - -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 e 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 e 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 not e: - 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 not e: - 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 not e: - return 0.0 - e2 = e * e - e4 = e2 * e2 - ef = 1.0 - e2 - M = m1 + m2 - pre = e / (ef**4) * (m1*m2) / (90.0 * M**4) - 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 not e: - return 0.0 - pre = 32.0 * eta * e * x**7 / 5.0 - return pre * (-985.0 * np.pi * phi_e_rad(e) / 48.0) - -## ---------- 2PN ------------- - -def e_dot_2pn(e: float, eta: float) -> float: - e_2_pn = 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) - - if e != 0.0: - 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) - ) - else: - e_2_pn = 0.0 - - 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 not e: - 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 e == 0.0: - 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 not e: - return 0.0 - pre = 32.0 * eta * e * x**4 * x**4 * 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 not e: - 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 e == 0.0: - 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 not e: - 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) - - -import math - -def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: - if e == 0.0: - 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 not e: - 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 not e: - 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 - - 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) * np.sqrt(x) + - 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 * np.sqrt(x) + - rel_sep_3pn(e, u, eta) * x * x + - rel_sep_3pn_SS(e, u, m1, m2, S1z, S2z) * x * x) - - - -# ============ 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) - - -# ================== 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 - xsqx = x * np.sqrt(x) - x5 = x**5 - - # ------------------------- - # 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) * x2 * np.sqrt(x) - - 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) * x3 * np.sqrt(x) - inst += x_dot_3_5_pn(e, eta) * x3 * np.sqrt(x) - inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) - inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) - - 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) - - if radiation_pn_order >= 9: - inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * np.sqrt(x) - - 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 - xsqx = x * np.sqrt(x) - - # ------------------------- - # 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) * x2 * np.sqrt(x) - - if radiation_pn_order >= 6: - inst += e_dot_3pn(e, eta, x) * x3 - inst += e_dot_3_5pn(e, eta) * x3 * np.sqrt(x) - 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 += 0.0 # placeholder for higher order terms, not implemented - - 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 e != 0: - 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/build/lib/esigmapy/python_codes/esigma_pn_main.py b/build/lib/esigmapy/python_codes/esigma_pn_main.py deleted file mode 100644 index 652e5cd..0000000 --- a/build/lib/esigmapy/python_codes/esigma_pn_main.py +++ /dev/null @@ -1,501 +0,0 @@ -import numpy as np -from scipy.integrate import ode -from scipy.interpolate import CubicSpline -import math -from .esigma_pn_inspiral import * -from .esigma_go_terms_draft import * -import lal - -# Constants (LAL equivalents) -LAL_PI = math.pi -LAL_MTSUN_SI = 4.925491025543576e-6 # Solar mass in seconds -RadiationPNOrderDefault = 8 # Default radiation reaction PN order (3PN) - -import os -import numpy as np -# 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 = 3 -LAL_MRSUN_SI = 1.476625061404649e3 # solar mass in metres - - -# # ------------------------------------------------------------------ # -# # Helper: populate powers of orbital variables -# # (mirrors PopulateKeplerParams + kepler_vars struct) -# # ------------------------------------------------------------------ # -# @dataclass -# class KeplerVars: -# eta: float = 0.0 -# eta2: float = 0.0 -# eta3: float = 0.0 -# eta4: float = 0.0 -# eta5: float = 0.0 -# eta6: float = 0.0 - -# Mtot: float = 0.0 -# Mtot2: float = 0.0 -# Mtot3: float = 0.0 -# Mtot4: float = 0.0 -# Mtot5: float = 0.0 -# Mtot6: float = 0.0 - -# S1z: float = 0.0 -# S1z2: float = 0.0 -# S1z3: float = 0.0 -# S1z4: float = 0.0 -# S1z5: float = 0.0 -# S1z6: float = 0.0 -# S1z7: float = 0.0 -# S1z8: float = 0.0 -# S1z9: float = 0.0 -# S1z10: float = 0.0 -# S1z11: float = 0.0 -# S1z12: float = 0.0 -# S1z13: float = 0.0 -# S1z14: float = 0.0 -# S1z15: float = 0.0 -# S1z16: float = 0.0 - -# S2z: float = 0.0 -# S2z2: float = 0.0 -# S2z3: float = 0.0 -# S2z4: float = 0.0 -# S2z5: float = 0.0 -# S2z6: float = 0.0 - - -# def _build_kepler_vars(eta: float, total_mass: float, -# S1z: float, S2z: float) -> KeplerVars: -# """Pre-compute and cache all integer powers used by the mode functions.""" -# kv = KeplerVars() -# kv.eta = eta -# kv.eta2 = eta**2; kv.eta3 = eta**3; kv.eta4 = eta**4 -# kv.eta5 = eta**5; kv.eta6 = eta**6 - -# kv.Mtot = total_mass -# kv.Mtot2 = total_mass**2; kv.Mtot3 = total_mass**3 -# kv.Mtot4 = total_mass**4; kv.Mtot5 = total_mass**5 -# kv.Mtot6 = total_mass**6 - -# kv.S1z = S1z -# for p in range(2, 17): -# setattr(kv, f"S1z{p}", S1z**p) - -# kv.S2z = S2z -# for p in range(2, 7): -# setattr(kv, f"S2z{p}", S2z**p) - -# return kv - - -# ------------------------------------------------------------------ # -# 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 = { - "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] - # ------------------------------------------------------------------ # - u0 = pn_kepler_equation(eta, x_init, e_init, mean_anom_init) - r0 = separation(u0, eta, x_init, e_init, mass1, mass2, S1z, S2z) - y0_dot = eccentric_x_model_odes(0.0, [x_init, e_init, mean_anom_init, 0.0], params) - phi_dot0 = y0_dot[3] - - y0 = np.array([x_init, e_init, mean_anom_init, 0.0]) - - # ------------------------------------------------------------------ # - # ODE integration (RK45 via scipy) - # ------------------------------------------------------------------ # - def rhs(t, y): - return eccentric_x_model_odes(t, y, params) - - solver = ode(rhs).set_integrator( - "dopri5", # RK4(5) Dormand-Prince ≈ gsl rkf45 - rtol=ode_eps, - atol=0.0, - nsteps=10_000, - max_hnil=0, - first_step=2 * LAL_PI / phi_dot0 / 100, - ) - solver.set_initial_value(y0, 0.0) - - MAX_SAMPLES = 2048 * 16384 - - t_list = [0.0] - x_list = [x_init] - e_list = [e_init] - l_list = [mean_anom_init] - phi_list = [0.0] - phi_dot_list = [phi_dot0] - r_list = [r0] - u_list = [u0] - - bad_number = False - - while solver.successful(): - solver.integrate(solver.t + dt) - - y = solver.y - - # NaN / Inf guard - if not np.all(np.isfinite(y)): - bad_number = True - break - - t_list.append(solver.t) - x_list.append(y[0]) - e_list.append(y[1]) - l_list.append(y[2]) - phi_list.append(y[3]) - - yd = eccentric_x_model_odes(solver.t, y, params) - phi_dot_list.append(yd[3]) - - ui = pn_kepler_equation(eta, y[0], y[1], y[2]) - ri = separation(ui, eta, y[0], y[1], mass1, mass2, S1z, S2z) - u_list.append(ui) - r_list.append(ri) - - if len(t_list) >= MAX_SAMPLES: - break - - if y[0] >= x_final: - break - - # Convert to arrays - t_arr = np.array(t_list) - x_arr = np.array(x_list) - e_arr = np.array(e_list) - l_arr = np.array(l_list) - phi_arr = np.array(phi_list) - phi_dot_arr = np.array(phi_dot_list) - r_arr = np.array(r_list) - - final_i = len(t_arr) if not bad_number else len(t_arr) # same either way here - if final_i < 4: - raise RuntimeError("Integration produced fewer than 4 points; cannot interpolate.") - - # ------------------------------------------------------------------ # - # 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_uniform(t_arr, phi_dot_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/build/lib/esigmapy/python_codes/generator_python.py b/build/lib/esigmapy/python_codes/generator_python.py deleted file mode 100644 index 761764d..0000000 --- a/build/lib/esigmapy/python_codes/generator_python.py +++ /dev/null @@ -1,1211 +0,0 @@ -# Copyright (C) 2020 Prayush Kumar, Akash Maurya, Kaushik Paul -# -"""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 * - -ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 -ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 - - -def eccentricity_at_extremum_frequency( - 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.data.data *= lal.MTSUN_SI - - if verbose: - print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") - - omega = pt.TimeSeries(phidot.data.data, delta_t=t.data.data[1] - t.data.data[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.data.data - time_at_sensitive_freq).argmin() - e0 = e.data.data[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.data.data, x.data.data**1.5, "--", label="x ** {3/2}") - ax1.plot(t.data.data, phidot.data.data, 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.data.data, e.data.data, 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( - 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.data.data *= 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.data.data - x_reference).argmin() - e0 = e.data.data[idx_e0] - - if show_figures: - import matplotlib.pyplot as plt - - fig, (ax) = plt.subplots(1, 1, figsize=(10, 5), sharex=True) - ax.plot(t.data.data, x.data.data**1.5, "--", label="x ** {3/2}") - ax.plot(t.data.data, phidot.data.data, lw=0.5, label="omega") - ax.axhline(x_reference**1.5, color="c", lw=1) - ax.axvline(t.data.data[idx_e0], color="r", lw=1) - ax.set_xlabel("Time (s)") - ax.set_ylabel("Orbital angular velocity") - - ax2 = plt.twinx(ax) - ax2.plot(t.data.data, e.data.data, 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( - 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.data.data[-1] - # mean_anomaly = l.data.data[-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: - ref_idx = np.searchsorted( - (retval[1].data.data ** 1.5) / ((mass1 + mass2) * lal.MTSUN_SI * np.pi), - f_lower, - ) - new_len = len(retval[0].data.data) - ref_idx - for ii in range(8): - lal.ResizeREAL8TimeSeries(retval[ii], ref_idx, new_len) - - t, x, e, l, phi, phidot, r, rdot = retval[:8] - t.data.data *= ( - 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.data, - x.data, - phi.data, - phidot.data, - r.data, - rdot.data, - mass1, - mass2, - spin1z, - spin2z, - distance, - ) - - if return_pycbc_timeseries: - modes = { - k: pt.TimeSeries( - modes[k].data.data, - delta_t=delta_t, - epoch=-delta_t * (len(modes[k].data.data) - 1), - ) - for k in modes - } - else: - modes = {k: np.asarray(modes[k].data.data) 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}.data.data, delta_t=delta_t, epoch=-delta_t * len({name}.data.data))" - ) - return orbital_var_dict, modes - - for name in return_orbital_params: - exec(f"orbital_var_dict['{name}'] = {name}.data.data") - return (t.data.data - t.data.data[-1]), orbital_var_dict, modes - - if return_pycbc_timeseries: - return modes - return (t.data.data - t.data.data[-1]), modes - - -def get_inspiral_esigma_waveform( - 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( - 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_window_start(freq_, delta_t_, delta_phi_, direction="forward"): - """Integrates frequency backward/forward from the start/end until - `delta_phi_` radians of orbital phase has elapsed. - - Args: - freq_ (numpy.array): Frequency time series, uniformly sampled - delta_t_ (float64): Step size in time - delta_phi_ (float64): Phase shift in radians to integrate across - direction (str, optional): Direction of integration in time. - Defaults to "forward". - - Returns: - int: Index in frequency array where `delta_phi` is reached - """ - from scipy import integrate - - if direction == "backward": - for idx in range(len(freq_) - 2, 0, -1): - # this could be optimized to not repeat integration - if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): - return idx - elif direction == "forward": - for idx in range(1, len(freq_)): - # this could be optimized to not repeat integration - if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( - delta_phi_ - ): - return idx - - -def _get_transition_frequency_window( - orbital_phase, - orbital_freq, - delta_t, - f_mr_transition, # orbital frequency - num_hyb_orbits, - keep_f_mr_transition_at_center, - blend_using_avg_orbital_frequency, - failsafe, - verbose=False, -): - """ - This function first finds where the `orbital_freq` crosses the transition - frequency `f_mr_transition`, and finds the point in time that is - `num_hyb_orbits` orbits before that time. This marks the start of the - hybridization window, and the orbital frequency at that time is returned - - Parameters: - ----------- - orbital_phase -- Orbital phase evolution - orbital_freq -- Orbital frequency evolution - delta_t -- Waveform's time grid-spacing (s) - f_mr_transition -- Inspiral to merger transition frequency - (Hz). This should be the same (mode/orbital) - frequency as passed for `orbital_freq`. - num_hyb_orbits -- number of orbital cycles to blend over. - 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). - blend_using_avg_orbital_frequency -- If True, the orbit averaged - frequency during the inspiral is used to - blend modes, instead of the modes' - frequency. - failsafe -- If True, we make reasonable choices for the - user, if the inputs to this method lead - into exceptions. - verbose -- Verbosity flag - - Returns: - -------- - f_window_mr_transition -- Hybridization frequency window (in Hz). - """ - transition_idx = ( - len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) - ) # index at which orbital_freq becomes just smaller than transition orbital - # frequency, towards the end of waveform - - if verbose > 4: - print( - f"""Transition frequency found at index {transition_idx} - in the orbital frequency array of length {len(orbital_freq)}""" - ) - - if blend_using_avg_orbital_frequency: - # We integrate `orbital_freq` to obtain `orbital_phase` - from scipy import integrate - - orbital_phase = integrate.cumulative_trapezoid( - orbital_freq, dx=delta_t, initial=0 - ) - if len(orbital_phase) != len(orbital_freq): - raise RuntimeError( - f"""Something went wrong while integrating orbital frequency. -They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" - ) - - if keep_f_mr_transition_at_center: - # Orbital cycle based hybridization that keeps f_mr_transition at - # the hybridization frequency window's midpoint - if blend_using_avg_orbital_frequency: - window_start_idx = _get_window_start( - orbital_freq[: transition_idx + 1], - delta_t, - num_hyb_orbits * np.pi, - direction="backward", - ) - window_end_idx = transition_idx + _get_window_start( - orbital_freq[transition_idx:], - delta_t, - num_hyb_orbits * np.pi, - direction="forward", - ) - # FAILSAFE mode behavior - if window_start_idx is None: - if failsafe: - window_start_idx = 0 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform before the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or increase the -inspiral-to-merger transition frequency. `window_start_idx` is None.""" - ) - if window_end_idx is None: - if failsafe: - window_end_idx = len(orbital_freq) - 1 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform after the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or decrease the -inspiral-to-merger transition frequency. `window_end_idx` is None.""" - ) - else: - # phase is always a monotonically increasing function, - # hence using the more efficient np.searchsorted method - window_start_idx = -1 + np.searchsorted( - orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi - ) - window_end_idx = np.searchsorted( - orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi - ) - f_window_mr_transition = 2 * np.min( - [ - orbital_freq[window_end_idx] - f_mr_transition, - f_mr_transition - orbital_freq[window_start_idx], - ] - ) - elif not keep_f_mr_transition_at_center: - # Orbital cycle based hybridization that keeps f_mr_transition at - # the hybridization frequency window's right end - if blend_using_avg_orbital_frequency: - window_start_idx = _get_window_start( - orbital_freq[: transition_idx + 1], - delta_t, - 2 * num_hyb_orbits * np.pi, - direction="backward", - ) - if window_start_idx is None: - if failsafe: - window_start_idx = 0 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform before the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or increase the -inspiral-to-merger transition frequency. `window_start_idx` is None.""" - ) - else: - # Orbital cycle based hybridization that keeps f_mr_transition - # at the hybridization frequency window's end - window_start_idx = ( - np.searchsorted( - orbital_phase, - orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, - ) - - 1 - ) - f_window_mr_transition = ( - orbital_freq[transition_idx] - orbital_freq[window_start_idx] - ) - - if verbose > 4: - print( - f"""The transition is to happen in a window from - frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, - between indices: [{window_start_idx}, {transition_idx}]""" - ) - else: - raise RuntimeError( - """We should never reach here. Did you set a non-bool value for the - flag `keep_f_mr_transition_at_center`?""" - ) - - if verbose > 4: - print(f"""f_window_mr_transition = {f_window_mr_transition}""") - - return f_window_mr_transition - - -def get_imr_esigma_modes( - 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( - 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. Its 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( - 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( - 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/build/lib/esigmapy/surrogate/__init__.py b/build/lib/esigmapy/surrogate/__init__.py deleted file mode 100644 index 90fe36e..0000000 --- a/build/lib/esigmapy/surrogate/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from __future__ import absolute_import - -from .surrogate import * -from .generator import * diff --git a/build/lib/esigmapy/surrogate/generator.py b/build/lib/esigmapy/surrogate/generator.py deleted file mode 100644 index e1c2f7c..0000000 --- a/build/lib/esigmapy/surrogate/generator.py +++ /dev/null @@ -1,820 +0,0 @@ -# Copyright (C) 2026 Akash Maurya, Prayush Kumar - -"""Functions for generating ESIGMASur 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 esigmapy.utils import f_ISCO_spin -from esigmapy.generator import _get_transition_frequency_window -from .surrogate import _get_surrogate - -ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 -ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 - - -def get_surrogate_object(): - """ - Returns the surrogate object. Useful for advanced users who want to - directly use the base surrogate class' functionalities. - """ - return _get_surrogate() - - -def get_inspiral_esigmasur_modes( - mass1, - mass2, - reference_eccentricity=0.0, - reference_mean_anomaly=0.0, - delta_t=None, - times=None, - t_start=None, - t_end=None, - distance=1.0, - include_conjugate_modes=False, - return_orbital_params=False, - return_pycbc_timeseries=True, - verbose=False, -): - """ - Returns inspiral ESIGMASur GW modes - - Parameters: - ----------- - mass1, mass2 -- Binary's component masses (in solar masses) - delta_t -- Waveform's time grid-spacing (in s) - reference_eccentricity -- Eccentricity at reference time of surrogate - reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) - distance -- Luminosity distance to the binary (in Mpc) - t_start, t_end -- Start and end times of the waveform - to be generated (in seconds). - Note that the surrogate defines t=0 at the end of - inspiral, so t_start and t_end should be negative - and t_start < t_end <= 0. - Defaults to the full duration of the surrogate. - include_conjugate_modes -- If True, negative "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'] - 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: - orbital_var_names = ["x", "e", "l"] - if return_orbital_params != True: - for name in return_orbital_params: - if name not in orbital_var_names: - raise Exception( - f"{name} is not a valid orbital variable name. Available orbital variable names are: {orbital_var_names}." - ) - - sur = _get_surrogate() - total_mass = mass1 + mass2 - q = mass1 / mass2 - if q < 1: - q = 1 / q - - modes_to_use = [(2, 2)] - - # Calculating the orbital variables - itime = time.perf_counter() - modes = {} - - retval = sur( - M=total_mass, - params=[ - q, - reference_eccentricity, - reference_mean_anomaly, - ], # q, e, l at t=t_ref - delta_t=delta_t, - t_start=t_start, - t_end=t_end, - times=times, - remove_start_phase=True, - return_orbital_variables=return_orbital_params, - ) - # Currently, the surrogate only supports (2, 2) mode, so we directly retrieve that from the returned dictionary. - el, em = modes_to_use[0] - if return_orbital_params: - t, orb_vars, modes[(el, em)] = retval - else: - t, modes[(el, em)] = retval - modes[(el, em)] /= distance - - if include_conjugate_modes: - for el, em in modes_to_use: - modes[(el, -em)] = (-1) ** el * np.conjugate(modes[(el, em)]) - - if return_pycbc_timeseries: - if times is None: - modes = { - k: pt.TimeSeries( - modes[k], - delta_t=delta_t, - epoch=-delta_t * (len(modes[k]) - 1), - ) - for k in modes - } - else: - raise ValueError( - """Cannot return PyCBC TimeSeries when the user provides custom time grid via `times` due to the possibilty of it being a non-uniform time-grid. -Please set `return_pycbc_timeseries=False` if you want to provide custom time grid.""" - ) - 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 = orbital_var_names - - if return_pycbc_timeseries: - # No need to check and raise error here if `times` is provided, - # because the error will be raised while trying to convert modes - # to PyCBC TimeSeries above. - for name in return_orbital_params: - exec( - f"orbital_var_dict['{name}'] = pt.TimeSeries(orb_vars['{name}'], delta_t=delta_t, epoch=-delta_t * (len(orb_vars['{name}'])-1))" - ) - return orbital_var_dict, modes - - for name in return_orbital_params: - exec(f"orbital_var_dict['{name}'] = orb_vars['{name}']") - # return (t - t[-1]), orbital_var_dict, modes - return t, orbital_var_dict, modes - - if return_pycbc_timeseries: - return modes - # return (t - t[-1]), modes - return t, modes - - -def get_inspiral_esigmasur_waveform( - mass1, - mass2, - reference_eccentricity=0.0, - reference_mean_anomaly=0.0, - delta_t=None, - t_start=None, - t_end=None, - times=None, - inclination=0.0, - coa_phase=0.0, - distance=1.0, - return_orbital_params=False, - return_pycbc_timeseries=True, - verbose=False, - **kwargs, -): - """ - Returns inspiral ESIGMASur GW polarizations - - Parameters: - ----------- - mass1, mass2 -- Binary's component masses (in solar masses) - reference_eccentricity -- Eccentricity at reference time of surrogate - reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) - delta_t -- Waveform's time grid-spacing (in s). - Can be omitted if providing custom time grid via - `times` argument. - t_start, t_end -- Start and end times of the waveform - to be generated (in seconds). - Note that the surrogate defines t=0 at the end of - inspiral, so t_start and t_end should be negative - and t_start < t_end <= 0. - Defaults to the full duration of the surrogate. - times -- Custom time grid (can be non-uniform) on which the - waveform should be generated. Should be a numpy - array of time values in seconds. - If provided, `delta_t`, `t_start` and `t_end` are ignored. - Also set `return_pycbc_timeseries=False` to use this option. - 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) - 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'] - return_pycbc_timeseries -- If True, returns data in the form of PyCBC timeseries. - True by default. Set to False if you want to provide custom time grid via `times` argument, or if you want the output in numpy arrays. - 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_esigmasur_modes( - mass1=mass1, - mass2=mass2, - reference_eccentricity=reference_eccentricity, - reference_mean_anomaly=reference_mean_anomaly, - t_start=t_start, - t_end=t_end, - delta_t=delta_t, - times=times, - distance=distance, - 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: - if times is None: - 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)) - else: - raise ValueError( - """Cannot return PyCBC TimeSeries when the user provides custom time grid via `times` due to the possibilty of it being a non-uniform time-grid. -Please set `return_pycbc_timeseries=False` if you want to provide custom time grid.""" - ) - - 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_esigmasur_mode( - mass1, - mass2, - delta_t, - reference_eccentricity=0.0, - reference_mean_anomaly=0.0, - t_start=None, - distance=1.0, - coa_phase=None, - include_conjugate_modes=False, - f_mr_transition=None, - f_window_mr_transition=None, - num_hyb_orbits=0.25, - 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 ESIGMASur for inspiral and - NRSur7dq4/SEOBNRv4PHM for merger-ringdown - - Parameters: - ----------- - mass1, mass2 -- Binary's component masses (in solar masses) - delta_t -- Waveform's time grid-spacing (in s) - reference_eccentricity -- Eccentricity at reference time of surrogate - reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) - t_start -- Start time of the waveform to be generated (in seconds). - Note that the surrogate defines t=0 at the end of - inspiral, so t_start should be negative - and t_start < 0. - Defaults to the full duration of the surrogate. - coa_phase -- Coalesence phase of the binary (in rad) - distance -- Luminosity distance to the binary (in Mpc) - 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 - using which alignment is done, i.e. (2,2) mode here. - Defaults to the minimum of the Kerr and - Schwarzschild ISCO frequency equivalent - for the (2,2)-mode. - f_window_mr_transition -- Hybridization frequency window (in Hz). - Should correspond to the mode - using which alignment is done, i.e. (2,2) mode here. - 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_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: - ['e', 'l', 'x']. - 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 - """ - - spin1z = 0.0 - spin2z = 0.0 - blend_using_avg_orbital_frequency = True - modes_to_use = [(2, 2)] - mode_to_align_by = (2, 2) - - if not hasattr(ls, merger_ringdown_approximant): - raise IOError( - """We cannot generate individual modes for {merger_ringdown_approximant}. -Try one of: [NRSur7dq4, SEOBNRv4PHM]""" - ) - if (reference_mean_anomaly is None) and (coa_phase is None): - raise IOError( - f"""Please specify one of the phase angles, either of `reference_mean_anomaly` or `coa_phase`.""" - ) - if blend_aligning_merger_to_inspiral and (reference_mean_anomaly is None): - raise IOError( - f"""If you want to attach ESIGMASur inspiral to merger, by phase shifting merger to inspiral, please specify the phase angle `reference_mean_anomaly`""" - ) - if (not blend_aligning_merger_to_inspiral) and (coa_phase is None): - raise IOError( - f"""If you want to attach ESIGMASur inspiral to merger, by phase shifting inspiral to merger, please specify the phase angle `coa_phase`""" - ) - if reference_mean_anomaly is None: - reference_mean_anomaly = 0 - if coa_phase is None: - coa_phase = 0 - - available_inspiral_orbital_params = ["e", "l", "x"] - 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 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: - 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_esigmasur_modes( - mass1=mass1, - mass2=mass2, - reference_eccentricity=reference_eccentricity, - reference_mean_anomaly=reference_mean_anomaly, - t_start=t_start, - t_end=None, # To generate the full inspiral for attachment to merger - delta_t=delta_t, - distance=distance, - 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 reference eccentricity -{reference_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 reference eccentricity -{reference_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: - NotImplementedError( - "Can't use any prescription other than the orbit averaged frequency one." - ) - - 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( - None, - 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. Its very likely -that you entered a very large reference eccentricity {reference_eccentricity}. The orbital -eccentricity at the end of inspiral was {orbital_eccentricity[-1]} - """ - ) - raise exc - modes_imr_numpy = retval[0] - - # Set t=0 at the end of inspiral surrogate - if mode_to_align_by not in modes_imr_numpy: - mode_to_align_by = list(modes_imr_numpy.keys())[0] - idx_peak = len(modes_inspiral_numpy[mode_to_align_by]) - 1 - 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 retval, orbital_vars_dict, modes_imr - elif return_orbital_params_user: - return orbital_vars_dict, modes_imr - elif return_hybridization_info: - return retval, modes_imr - return modes_imr - - -def get_imr_esigmasur_waveform( - mass1, - mass2, - delta_t, - reference_eccentricity=0.0, - reference_mean_anomaly=0.0, - t_start=None, - distance=1.0, - coa_phase=0.0, - inclination=0.0, - f_mr_transition=None, - f_window_mr_transition=None, - num_hyb_orbits=0.25, - 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 polarizations constructed using hybridized IMR ESIGMASur modes - - Parameters: - ----------- - mass1, mass2 -- Binary's component masses (in solar masses) - delta_t -- Waveform's time grid-spacing (in s) - reference_eccentricity -- Eccentricity at reference time of surrogate - reference_mean_anomaly -- Mean anomaly at reference time of surrogate (in rad) - t_start -- Start time of the waveform to be generated (in seconds). - Note that the surrogate defines t=0 at the end of - inspiral, so t_start should be negative - and t_start < 0. - Defaults to the full duration of the surrogate. - distance -- Luminosity distance to the binary (in Mpc) - coa_phase -- Coalesence phase of the binary (in rad) - inclination -- Inclination (in rad), defined as the angle - between the orbital angular momentum L and - the line-of-sight - f_mr_transition -- Inspiral to merger transition GW frequency - (Hz). Should correspond to the mode - using which alignment is done, i.e. (2,2) mode here. - Defaults to the minimum of the Kerr and - Schwarzschild ISCO frequency equivalent - for the (2,2)-mode. - f_window_mr_transition -- Hybridization frequency window (in Hz). - Should correspond to the mode - using which alignment is done, i.e. (2,2) mode here. - 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_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: - ['e', 'l', 'x']. - 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_esigmasur_mode( - mass1=mass1, - mass2=mass2, - reference_eccentricity=reference_eccentricity, - reference_mean_anomaly=reference_mean_anomaly, - delta_t=delta_t, - t_start=t_start, - distance=distance, - coa_phase=coa_phase, - 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_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/build/lib/esigmapy/surrogate/surrogate.py b/build/lib/esigmapy/surrogate/surrogate.py deleted file mode 100644 index 4f99a2c..0000000 --- a/build/lib/esigmapy/surrogate/surrogate.py +++ /dev/null @@ -1,556 +0,0 @@ -# Copyright (C) 2026 Akash Maurya - -"""ESIGMASur surrogate base evaluation code""" - -import os - -os.environ["OMP_NUM_THREADS"] = "1" -import numpy as np -from numba import njit -import h5py -import scipy.interpolate as si -import TPI -from lal import SpinWeightedSphericalHarmonic -from pycbc.conversions import eta_from_q -import re - -_amp_correction_factor = np.real( - SpinWeightedSphericalHarmonic(0, 0, -2, 2, 2) -) # The amplitude normalization factor saved in the surrogate files is off by this amount. So we correct for it during surrogate evaluation by using this factor. - - -@njit(fastmath=True) -def mode_from_amp_phase(amp, phase): - return amp * np.exp(-1j * phase) - - -def _unwrap_single_float(val): - if isinstance(val, (float, int, np.floating, np.integer)): - return float(val) - elif isinstance(val, np.ndarray) and val.size == 1: - return float(val.item()) - else: - raise ValueError( - f"Expected a float or a numpy array of size 1, got: {val} ({type(val)})" - ) - - -class Surrogate: - def __init__(self, data_dir): - # The directory where the surrogate data is stored - self.sur_dir = data_dir - # Surrogate data pieces. This will be set in the child classes, as the circular and eccentric surrogates are built of different data pieces. - self.data_piece_names = None - # Normalization factors for the surrogate data pieces, which are used to un-normalize the surrogate data pieces before reconstructing the waveform; read from the surrogate files via self.load_norm_factors(). - self.norm_factor = {} - # The EIM B-matrices for the surrogate data pieces; read from the surrogate files via self.load_eim_B_matrices(). - self.eim_B = {} - # The parametric fits for the surrogate data pieces at EI nodes; read from the surrogate files via self.load_param_space_fits(), defined in child classes. - self.fit = {} - - def get_metadata(self, key): - """ - Returns metadata of the surrogate - - Parameters: - ----------- - key -- Metadata key (string) or list of keys to return. - - Returns: - -------- - Metadata value(s) corresponding to the key(s). - If a single key is provided, returns the corresponding value, - otherwise returns a list of values corresponding to the list of keys. - """ - filename = os.path.join(self.sur_dir, "surrogate_metadata.hdf") - with h5py.File(filename, "r") as f: - if isinstance(key, str): - return f[key][()] - return [f[k][()] for k in key] - - def load_norm_factors(self): - filename_norm_factors = os.path.join(self.sur_dir, f"norm_factors.npz") - norm_factor_dataset = np.load(filename_norm_factors) - for data_piece_name in self.data_piece_names: - self.norm_factor[data_piece_name] = norm_factor_dataset[ - f"norm_factor_{data_piece_name}" - ] - norm_factor_dataset.close() - - def load_eim_B_matrices(self, filename=None, data_piece_names=None): - if filename is None: - filename = "eim_B.npz" - if data_piece_names is None: - data_piece_names = self.data_piece_names - if isinstance(data_piece_names, str): - data_piece_names = [data_piece_names] - filename_eim = os.path.join(self.sur_dir, filename) - eim_B_dataset = np.load(filename_eim) - for data_piece_name in data_piece_names: - self.eim_B[data_piece_name] = eim_B_dataset[f"eim_B_{data_piece_name}"] - eim_B_dataset.close() - - def _set_time_range(self, M, times, t_start, t_end): - mass_scaling_factor = M / self.sur_total_mass - - t_min_sur = self.t_grid_sur[0] * mass_scaling_factor - t_max_sur = self.t_grid_sur[-1] * mass_scaling_factor - - if times is None: - if t_start is None: - t_start = t_min_sur - if t_end is None: - t_end = t_max_sur - else: - t_start = times[0] - t_end = times[-1] - - if t_start < t_min_sur or t_end > t_max_sur: - raise ValueError( - f"""Requested time range [{t_start}s, {t_end}s] is out of the surrogate's time range of [{t_min_sur}s, {t_max_sur}s] for the given total mass of {M:.2f} M_sun. -Please choose a time interval within these bounds.""" - ) - - return t_start, t_end, mass_scaling_factor - - @staticmethod - def _find_conservative_starting_truncation_index(grid, val): - idx = np.searchsorted(grid, val, side="right") - 1 - idx -= 5 # Leaving some buffer to avoid edge effects in spline interpolation - # Checking if we even have data this deep in the starting - if idx < 0: - idx = 0 - return idx - - -class CircularSurrogate(Surrogate): - def __init__(self, data_dir): - super().__init__(data_dir=data_dir) - self.data_piece_names = [ - "amp", - "phase", - "x", - "l", - ] # The surrogate data pieces for the circular surrogate. The "x" and "l" data pieces are used only for reconstructing the orbital variables, and are not needed if one only wants the waveform. - self.load_sur_metadata() - self.load_norm_factors() - self.load_eim_B_matrices( - filename="eim_B.npz", data_piece_names=["amp", "phase"] - ) - self.load_eim_B_matrices( - filename="eim_B-orb_vars.npz", data_piece_names=["x", "l"] - ) - self.load_param_space_fits() - - def load_sur_metadata(self): - self.sur_total_mass, self.t_grid_sur = self.get_metadata(["M", "t_grid_sur"]) - - @staticmethod - def load_interpolant(filename): - # scipy BSplines - tck = np.load(filename) - vec_interpolant = si.BSpline(tck["t"], tck["c"], tck["k"], extrapolate=False) - return vec_interpolant - - def load_param_space_fits(self): - for data_piece_name in self.data_piece_names: - filename_interp = os.path.join(self.sur_dir, f"{data_piece_name}_fits.npz") - self.fit[data_piece_name] = self.load_interpolant(filename_interp) - - def __call__( - self, - M, - q, - reference_mean_anomaly=0.0, # This is only used for returning orbital variables - delta_t=None, - t_start=None, - t_end=None, - times=None, - remove_initial_phase=False, - return_amp_phase_only=False, - return_orbital_variables=False, - ): - - if delta_t is None and times is None: - raise ValueError("Either delta_t or times must be provided.") - - t_grid_sur = self.t_grid_sur - - t_start, t_end, mass_scaling_factor = self._set_time_range( - M=M, times=times, t_start=t_start, t_end=t_end - ) - - start_idx = self._find_conservative_starting_truncation_index( - grid=t_grid_sur, val=t_start / mass_scaling_factor - ) - t_grid_sur = t_grid_sur[start_idx:] * mass_scaling_factor - - if times is None: - num_samples = int((t_end - t_start) / delta_t) + 1 - new_t_grid = t_start + np.arange(num_samples) * delta_t - else: - new_t_grid = times - - q = _unwrap_single_float( - q - ) # This is to ensure that q is a single value and not an array - eta = eta_from_q(q) - - amp_node_vals = self.fit["amp"](eta) - phase_node_vals = self.fit["phase"](eta) - - amp_native = self.norm_factor["amp"] * np.dot( - amp_node_vals, self.eim_B["amp"][:, start_idx:] - ) - phase_native = self.norm_factor["phase"] * np.dot( - phase_node_vals, self.eim_B["phase"][:, start_idx:] - ) - - amp = np.interp(new_t_grid, t_grid_sur, amp_native) - phase = np.interp(new_t_grid, t_grid_sur, phase_native) - - amp *= ( - mass_scaling_factor / _amp_correction_factor - ) # Correcting for the amplitude normalization factor that was off in the surrogate files. - - if remove_initial_phase: - phase -= phase[0] - if return_amp_phase_only: - return amp, phase - - if return_orbital_variables: - e = np.zeros(len(new_t_grid)) - - l_node_vals = self.fit["l"](eta) - l_native = self.norm_factor["l"] * np.dot( - l_node_vals, self.eim_B["l"][:, start_idx:] - ) - l = ( - np.interp(new_t_grid, t_grid_sur, l_native) + reference_mean_anomaly - ) # The circular surrogate mean anomaly data was prepared such that the mean anomaly at the reference time is 0, so we add the reference_mean_anomaly here to get the correct mean anomaly values at the reference time. This is physically correct because circular waveforms just differing in mean anomalies are simply constant phase-shifted versions of each other - # Caution: This also means that one should make sure during surrogate construction that the reference time of the circular surrogate is the same as that of the eccentric surrogate. - l -= ( - 2 * np.pi * np.floor(l[0] / (2 * np.pi)) - ) # Bringing starting value of mean anomaly in [0, 2pi) - - x_node_vals = self.fit["x"](eta) - x_native = self.norm_factor["x"] * np.dot( - x_node_vals, self.eim_B["x"][:, start_idx:] - ) - x = np.interp(new_t_grid, t_grid_sur, x_native) - - orb_vars = {"e": e, "l": l, "x": x} - return new_t_grid, orb_vars, mode_from_amp_phase(amp, phase) - - return new_t_grid, mode_from_amp_phase(amp, phase) - - -class EccentricSurrogate(Surrogate): - def __init__(self, ecc_data_dir, circ_data_dir, verbose=False): - super().__init__(data_dir=ecc_data_dir) - self.circ_sur_dir = circ_data_dir - self.circ_sur = CircularSurrogate(data_dir=self.circ_sur_dir) - - self.data_piece_names = [ - "res_amp", - "res_phase", - "res_circ_phase", - "shifted_mean_anomaly", - "e", - "x", - ] - self.ei_indices = {} - - self.load_sur_metadata() - self.load_norm_factors() - self.load_eim_B_matrices() - self.load_ei_indices() - self.load_param_space_fits(verbose=verbose) - - self.q_min = 1.0 - self.q_max = 6.0 - self.e_ref_min = 5.0e-7 # The TPI fits below this value fail to evaluate - self.e_ref_max = 0.431 - self.l_ref_min = 0.0 - self.l_ref_max = 2 * np.pi - - def check_param_range(self, q, e_ref, l_ref, override=False): - if not override: - if not (self.q_min <= q <= self.q_max): - raise ValueError( - f"Mass ratio q={q} is out of range [{self.q_min}, {self.q_max}]. Please choose a value within this range." - ) - if not (0.0 <= e_ref <= self.e_ref_max): - raise ValueError( - f"Reference eccentricity e_ref={e_ref} is out of range [{0.}, {self.e_ref_max}]. Please choose a value within this range." - ) - if not (self.l_ref_min <= l_ref <= self.l_ref_max): - raise ValueError( - f"Reference mean anomaly l_ref={l_ref} is out of range [{self.l_ref_min}, {self.l_ref_max}]. Please choose a value within this range." - ) - - def load_sur_metadata(self): - self.sur_total_mass, self.t_ref, self.t_grid_sur, self.l_grid_sur = ( - self.get_metadata(["M", "t_ref", "t_grid_sur", "l_grid_sur"]) - ) - - def load_ei_indices(self): - filename_ei_indices = os.path.join(self.sur_dir, f"ei_indices.npz") - ei_indices_dataset = np.load(filename_ei_indices) - for data_piece_name in ["res_amp", "res_phase"]: - self.ei_indices[data_piece_name] = ei_indices_dataset[ - f"ei_indices_{data_piece_name}" - ] - ei_indices_dataset.close() - - @staticmethod - def _get_sorted_fit_filenames(filenames): - # Use a regex to extract the number before .h5, regardless of the prefix - pattern = re.compile(r"-(\d+)_spline\.h5$") - indexed_files = {} - for fname in filenames: - match = pattern.search( - fname - ) # search instead of match allows for variable prefix - if match: - idx = int(match.group(1)) - indexed_files[idx] = fname - - # Create array of appropriate size - max_index = max(indexed_files.keys()) - sorted_filenames = [None] * (max_index + 1) - # Fill the array - for idx, fname in indexed_files.items(): - sorted_filenames[idx] = fname - return sorted_filenames - - @staticmethod - def load_fit(filepath): - with h5py.File(filepath, "r") as f: - nodes = f["nodes"][()] - coeffs = f["coefficients"][()] - fit = TPI.TP_Interpolant_ND(list(nodes), coeffs=coeffs) - return fit - - def load_param_space_fits(self, verbose=False): - for data_piece_name in self.data_piece_names: - load_dir = os.path.join(self.sur_dir, f"fits/{data_piece_name}_fits") - # List of all the files which end in .h5 - filenames = [f for f in os.listdir(load_dir) if f.endswith(".h5")] - sorted_filenames = self._get_sorted_fit_filenames(filenames=filenames) - self.fit[data_piece_name] = [] - - for filename in sorted_filenames: - filepath = os.path.join(load_dir, filename) - if verbose: - print(f"Loading spline interpolant of GPR from {filename}...") - fit = self.load_fit(filepath) - self.fit[data_piece_name].append(fit) - - filepath = os.path.join( - self.sur_dir, f"fits/mean_anomaly_offset-ref_space-3D-fit_spline.h5" - ) - fit = self.load_fit(filepath) - self.fit["mean_anomaly_offset_fit"] = [fit] - - def __call__( - self, - M, - params, - delta_t=None, - t_start=None, - t_end=None, - times=None, - remove_start_phase=True, - return_orbital_variables=False, - ): - - if delta_t is None and times is None: - raise ValueError("Either delta_t or times must be provided.") - - t_grid_sur = self.t_grid_sur # The native time grid of the surrogate - l_grid_sur = ( - self.l_grid_sur - ) # The native shifted mean anomaly grid of the surrogate - - t_start, t_end, mass_scaling_factor = self._set_time_range( - M=M, times=times, t_start=t_start, t_end=t_end - ) - - start_idx_t = self._find_conservative_starting_truncation_index( - grid=t_grid_sur, val=t_start / mass_scaling_factor - ) - t_grid_sur = t_grid_sur[start_idx_t:] * mass_scaling_factor - - if times is None: - num_samples = int((t_end - t_start) / delta_t) + 1 - new_t_grid = t_start + np.arange(num_samples) * delta_t - else: - new_t_grid = times - - q, e_ref, l_ref = params - self.check_param_range(q=q, e_ref=e_ref, l_ref=l_ref) - - if e_ref > self.e_ref_min: - amp0_, phase0_ = self.circ_sur( - M=M, - q=q, - delta_t=delta_t, - t_start=t_start, - t_end=t_end, - times=new_t_grid, - remove_initial_phase=True, - return_amp_phase_only=True, - return_orbital_variables=False, - ) - else: - if e_ref != 0.0: - print( - f"Warning: e_ref={e_ref} < {self.e_ref_min}. Setting e_ref to 0 and using circular surrogate instead." - ) - return self.circ_sur( - M=M, - q=q, - reference_mean_anomaly=l_ref, - delta_t=delta_t, - t_start=t_start, - t_end=t_end, - times=new_t_grid, - remove_initial_phase=True, - return_orbital_variables=return_orbital_variables, - ) - eta = eta_from_q(q) - - e_node_vals = np.asarray( - [self.fit["e"][i]([eta, e_ref, l_ref]) for i in range(len(self.fit["e"]))] - ) - e_eim_res_amp = self.norm_factor["e"] * np.dot( - e_node_vals, self.eim_B["e"][:, self.ei_indices["res_amp"]] - ) - e_eim_res_phase = self.norm_factor["e"] * np.dot( - e_node_vals, self.eim_B["e"][:, self.ei_indices["res_phase"]] - ) - - mean_anomaly_offset_of_shifted_mean_anomaly = self.fit[ - "mean_anomaly_offset_fit" - ][0]( - [eta, e_ref, l_ref] - ) # This is the mean anomaly offset of the shifted mean anomaly - # Caution: It's important here that l_grid_sur is the un-truncated, full sur.l_grid_sur - l_eim_res_amp = ( - l_grid_sur[self.ei_indices["res_amp"]] - + mean_anomaly_offset_of_shifted_mean_anomaly - ) % (2 * np.pi) - l_eim_res_phase = ( - l_grid_sur[self.ei_indices["res_phase"]] - + mean_anomaly_offset_of_shifted_mean_anomaly - ) % (2 * np.pi) - - res_amp_node_vals = np.asarray( - [ - self.fit["res_amp"][i]([eta, e_eim_res_amp[i], l_eim_res_amp[i]]) - for i in range(len(self.fit["res_amp"])) - ] - ) - res_phase_node_vals = np.asarray( - [ - self.fit["res_phase"][i]([eta, e_eim_res_phase[i], l_eim_res_phase[i]]) - for i in range(len(self.fit["res_phase"])) - ] - ) - res_circ_phase_node_vals = np.asarray( - [ - self.fit["res_circ_phase"][i]([eta, e_ref, l_ref]) - for i in range(len(self.fit["res_circ_phase"])) - ] - ) - shifted_mean_anomaly_node_vals = np.asarray( - [ - self.fit["shifted_mean_anomaly"][i]([eta, e_ref, l_ref]) - for i in range(len(self.fit["shifted_mean_anomaly"])) - ] - ) - - lt_relation = self.norm_factor["shifted_mean_anomaly"] * np.dot( - shifted_mean_anomaly_node_vals, - self.eim_B["shifted_mean_anomaly"][:, start_idx_t:], - ) - - l_start = lt_relation[0] - start_idx_l = self._find_conservative_starting_truncation_index( - grid=l_grid_sur, val=l_start - ) - l_grid_sur = l_grid_sur[start_idx_l:] - - delta_amp_native = self.norm_factor["res_amp"] * np.dot( - res_amp_node_vals, self.eim_B["res_amp"][:, start_idx_l:] - ) - delta_phase_native = self.norm_factor["res_phase"] * np.dot( - res_phase_node_vals, self.eim_B["res_phase"][:, start_idx_l:] - ) - res_circ_phase_native = self.norm_factor["res_circ_phase"] * np.dot( - res_circ_phase_node_vals, self.eim_B["res_circ_phase"][:, start_idx_l:] - ) - - l_s = np.interp(new_t_grid, t_grid_sur, lt_relation) - delta_A = np.interp(l_s, l_grid_sur, delta_amp_native) - delta_phi = np.interp(l_s, l_grid_sur, delta_phase_native) - res_circ_phi = np.interp(l_s, l_grid_sur, res_circ_phase_native) - - delta_A *= mass_scaling_factor / _amp_correction_factor - amp = amp0_ + delta_A - phase = phase0_ + res_circ_phi + delta_phi - if remove_start_phase: - phase -= phase[0] - - if return_orbital_variables: - e_native = self.norm_factor["e"] * np.dot( - e_node_vals, self.eim_B["e"][:, start_idx_l:] - ) - e = np.interp(l_s, l_grid_sur, e_native) - - l = l_s + mean_anomaly_offset_of_shifted_mean_anomaly - l -= ( - 2 * np.pi * np.floor(l[0] / (2 * np.pi)) - ) # Bringing starting value of mean anomaly in [0, 2pi) - - x_node_vals = np.asarray( - [ - self.fit["x"][i]([eta, e_ref, l_ref]) - for i in range(len(self.fit["x"])) - ] - ) - x_native = self.norm_factor["x"] * np.dot( - x_node_vals, self.eim_B["x"][:, start_idx_l:] - ) - x = np.interp(l_s, l_grid_sur, x_native) - - orb_vars = {"e": e, "l": l, "x": x} - return new_t_grid, orb_vars, mode_from_amp_phase(amp, phase) - - return new_t_grid, mode_from_amp_phase(amp, phase) - - -_surrogate_instance = None - - -def _get_surrogate(): - global _surrogate_instance - if _surrogate_instance is None: - sur_data_dir = os.environ.get("ESIGMASUR_DATA_PATH", None) - if sur_data_dir is None: - raise RuntimeError( - "Surrogate data not found. Please set the ESIGMASUR_DATA_PATH " - "environment variable to the path of the surrogate data directory." - ) - ecc_sur_data_dir = os.path.join(sur_data_dir, "ecc_sur_data") - circ_sur_data_dir = os.path.join(sur_data_dir, "circ_sur_data") - if not os.path.isdir(ecc_sur_data_dir) or not os.path.isdir(circ_sur_data_dir): - raise RuntimeError( - f"Surrogate data not found. Please ensure that the environment variable ESIGMASUR_DATA_PATH points to the surrogate data directory." - ) - _surrogate_instance = EccentricSurrogate( - ecc_data_dir=ecc_sur_data_dir, circ_data_dir=circ_sur_data_dir - ) - print("Surrogate data loaded successfully.") - return _surrogate_instance diff --git a/build/lib/esigmapy/utils.py b/build/lib/esigmapy/utils.py deleted file mode 100644 index 4a80b49..0000000 --- a/build/lib/esigmapy/utils.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (C) 2025 Kaushik Paul, Akash Maurya -# -from __future__ import absolute_import, print_function - -import lal -import numpy as np - - -def f22_from_x(x, M): - """ - Parameters: - ----------- - x: Dimensionless PN parameter - M: Total mass of binary (in solar masses) - - Returns: - -------- - f22: "Orbit-averaged (2,2)-mode GW frequency" (in Hz) - """ - return x**1.5 / (M * lal.MTSUN_SI * lal.PI) - - -def x_from_f22(f22, M): - """ - Parameters: - ----------- - f22: "Orbit-averaged (2,2)-mode GW frequency" (in Hz) - M: Total mass of binary (in solar masses) - - Returns: - -------- - x: Dimensionless PN parameter - """ - return (M * lal.MTSUN_SI * lal.PI * f22) ** (2.0 / 3) - - -def f_ISCO_spin(mass1, mass2, spin1z, spin2z): - """ - Kerr ISCO frequency fitting formula for aligned-spins binary black holes - - Parameters: - ---------- - mass1, mass2 -- Binary's component masses (in solar masses) - spin1z, spin2z -- z-components of component dimensionless spins (lies in [0,1)) - - Returns: Kerr ISCO frequency (in Hz) - ------- - """ - Msun = lal.MTSUN_SI - k00 = -3.821158961 - k01 = -1.2019 - k02 = -1.20764 - k10 = 3.79245 - k11 = 1.18385 - k12 = 4.90494 - Zeta = 0.41616 - - m1 = mass1 * Msun - m2 = mass2 * Msun - M = m1 + m2 - eta = (m1 * m2) / (M**2) - z = 0 - - atot = (spin1z + spin2z * (m2 / m1) ** 2) / ((1 + (m2 / m1)) ** 2) - aeff = atot + Zeta * eta * (spin1z + spin2z) - - Z1 = 1 + ((1 - aeff**2) ** (1 / 3)) * ( - ((1 + aeff) ** (1 / 3)) + ((1 - aeff) ** (1 / 3)) - ) - Z2 = np.sqrt(3 * aeff**2 + Z1**2) - # riscocap = 3 + Z2 - ((aeff) / abs(aeff)) * np.sqrt((3 - Z1) * (3 + Z1 + 2 * Z2)) - riscocap = 3 + Z2 - np.sign(aeff) * np.sqrt((3 - Z1) * (3 + Z1 + 2 * Z2)) - # Equivalent to the above commented expression, and works also for aeff=0. - # For aeff=0, np.sign(aeff)=0, which is alright because its coefficient - # np.sqrt((3 - Z1) * (3 + Z1 + 2 * Z2)) is anyway 0 when aeff=0 because Z1 = 3 then. - Eiscocap = np.sqrt((1 - (2 / (3 * riscocap)))) - Liscocap = (2 / (3 * np.sqrt(3))) * (1 + 2 * np.sqrt(3 * riscocap - 2)) - - chichif = ( - atot - + eta * (Liscocap - 2 * atot * (Eiscocap - 1)) - + k00 * eta**2 - + k01 * eta**2 * aeff - + k02 * eta**2 * aeff**2 - + k10 * eta**3 - + k11 * eta**3 * aeff - + k12 * eta**3 * aeff**2 - ) - chichip = chichif - - Z1p = 1 + ((1 - chichip**2) ** (1 / 3)) * ( - ((1 + chichip) ** (1 / 3)) + ((1 - chichip) ** (1 / 3)) - ) - Z2p = np.sqrt(3 * chichip**2 + Z1p**2) - riscocapp = ( - 3 + Z2p - ((chichip) / abs(chichip)) * np.sqrt((3 - Z1p) * (3 + Z1p + 2 * Z2p)) - ) - - omegacap = 1 / (riscocapp ** (3 / 2) + chichip) - Scap = (1 / (1 - 2 * eta)) * (spin1z * m1**2 + spin2z * m2**2) / (M**2) - SS = (1 + Scap * (-0.00303023 - 2.00661 * eta + 7.70506 * eta**2)) / ( - 1 + Scap * (-0.67144 - 1.475698 * eta + 7.30468 * eta**2) - ) - - # Erad = (M* (0.0559745 * eta + 0.580951 * eta**2 - 0.960673 * eta**3 + 3.35241 * eta**4)* SS) - Erad_by_M = ( - 0.0559745 * eta + 0.580951 * eta**2 - 0.960673 * eta**3 + 3.35241 * eta**4 - ) * SS - - # Mfin = M * (1 - Erad / M) - Mfin = M * (1 - Erad_by_M) - fre = (1 / (1 + z)) * omegacap / (np.pi * Mfin) - - return 1 / 2 * fre - - -def get_peak_freqs(freq): - """ - Inputs - ------ - freq: Array of similar iterable of frequency values - - Outputs - ------- - peak_times: Times at which local maxima of frequency are attained - peak_freqs: Frequency at these times - """ - peaks, peak_times = [], [] - fvals = freq.data - - for idx, finst in enumerate(fvals): - if idx == 0 or idx == len(fvals) - 1: - continue - - if ((fvals[idx - 1]) < (finst)) and ((fvals[idx + 1]) < (finst)): - peaks.append(finst) - peak_times.append(freq.sample_times[idx]) - - return np.array(peak_times), np.array(peaks) - - -def get_polarizations_from_multipoles( - waveform_multipoles, inclination, coa_phase, verbose=False -): - """ - Returns GW polarizations from complex GW multipoles - - Parameters: - ----------- - waveform_multipoles -- Dictionary of complex Waveform Multipoles - indexed by their `(el, em)` values - 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) - delta_t -- Waveform's time grid-spacing (in s) - verbose -- Verbosity level. Available values are: 0, 1, 2 - - Returns: - -------- - hp, hc -- Plus and cross GW polarizations - """ - available_modes = list(waveform_multipoles.keys()) - - # Initialize with zeros, preserving data type and precision - try: - hp = waveform_multipoles[available_modes[0]].real * 0 - hc = waveform_multipoles[available_modes[0]].real * 0 - except: - hp = waveform_multipoles[available_modes[0]].real() * 0 - hc = waveform_multipoles[available_modes[0]].real() * 0 - - for el, em in waveform_multipoles: - ylm = lal.SpinWeightedSphericalHarmonic(inclination, coa_phase, -2, el, em) - glm = waveform_multipoles[(el, em)] * ylm - if verbose > 4: - print(f"Adding mode {el}, {em} with ylm = {ylm}", flush=True) - print( - f"... adding {waveform_multipoles[(el, em)]}, {glm}", - flush=True, - ) - print(f"... after adding, hp={hp}, hc={hc}", flush=True) - try: - hp = hp + glm.real - hc = hc - glm.imag - except: - hp = hp + glm.real() - hc = hc - glm.imag() - - return hp, hc diff --git a/esigmapy.egg-info/PKG-INFO b/esigmapy.egg-info/PKG-INFO deleted file mode 100644 index 7a3e778..0000000 --- a/esigmapy.egg-info/PKG-INFO +++ /dev/null @@ -1,174 +0,0 @@ -Metadata-Version: 2.4 -Name: esigmapy -Version: 2025.3.7 -Summary: A collection of tools for generating ESIGMA waveform multipoles and polarizations. -Home-page: https://github.com/gwnrtools/esigmapy -Author: Prayush Kumar -Author-email: prayush.kumar@gmail.com -License: GPL -License-File: LICENSE -Requires-Dist: lscsoft_glue>=2.0.0 -Requires-Dist: matplotlib>=2.1 -Requires-Dist: numpy -Requires-Dist: pycbc -Requires-Dist: scipy>=1.2.3 -Requires-Dist: tpi-splines -Requires-Dist: numba -Dynamic: author -Dynamic: author-email -Dynamic: description -Dynamic: home-page -Dynamic: license -Dynamic: license-file -Dynamic: requires-dist -Dynamic: summary - -# ESIGMAPy: a Python package to generate ESIGMA waveforms - -`ESIGMAPy` provides access to two eccentric waveform models: - -| Model | Description | -|---|---| -| `ESIGMAHM` | Eccentric, aligned-spin IMR waveform with higher modes (arXiv:[2409.13866](https://arxiv.org/abs/2409.13866)) | -| `ESIGMASur` | Surrogate model of the (2,2) GW-mode of `ESIGMAHM` (arXiv:[2510.00116](https://arxiv.org/abs/2510.00116))| - ---- - -## :rocket: Quick Start - -Install via: - -```bash -pip install git+https://github.com/gwnrtools/esigmapy.git -``` -Then to use: - * `ESIGMAHM`: requires custom `LALSuite` installation and `NRSur7dq4` surrogate data download ([installation instructions below](https://github.com/gwnrtools/esigmapy/tree/master?tab=readme-ov-file#blue_square-esigmahm)). - * `ESIGMASur`: requires downloading `ESIGMASur` surrogate data ([installation instructions below](https://github.com/gwnrtools/esigmapy/tree/master?tab=readme-ov-file#green_square-esigmasur)) - -Usage instructions at: - * `ESIGMAHM` [tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMA_tutorial.ipynb) - * `ESIGMASur` [tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMASur_tutorial.ipynb) -*** -## :blue_square: ESIGMAHM - -`ESIGMAHM` is an eccentric, aligned-spin, inspiral-merger-ringdown (IMR) waveform model with higher-order modes. It is composed of two pieces: - -* **Inspiral piece (`InspiralESIGMAHM`):** It comes from a combination of post-Newtonian theory, self-force, and black hole perturbation theory. -* **Plunge-merger-ringdown piece**: Assuming moderate starting eccentricities that decay by the late inspiral, we use the quasi-circular (QC) NR surrogate `NRSur7dq4` for the plunge-merger-ringdown piece for `ESIGMAHM`. - - (**Note:** We also allow other QC `LALSuite` waveform models to be used as the plunge-merger-ringdown piece; see the optional argument `merger_ringdown_approximant` in the generation functions `get_imr_esigma_waveform` and `get_imr_esigma_modes` [here](https://github.com/gwnrtools/esigmapy/blob/master/esigmapy/generator.py). However, the default and the most tested choice is `NRSur7dq4`.) - -The full IMR waveform `ESIGMAHM` is produced by smoothly attaching the inspiral piece `InspiralESIGMAHM` to the plunge-merger-ringdown piece `NRSur7dq4`. This attachment is facilitated by `ESIGMAPy`. - -Using `ESIGMAHM` therefore requires installing 1. `InspiralESIGMAHM`, 2. `NRSur7dq4`, and 3. `ESIGMAPy`. - -### Installing `InspiralESIGMAHM` - -* **Getting the source code:** The `LALSuite` fork containing the implementation of `InspiralESIGMAHM` is currently private, but interested users are welcome to write to the developers for access at esigmahm@icts.res.in. - - Clone the [`LALSuite` fork](https://git.ligo.org/kaushik.paul/lalsuite/-/tree/enigma_spins_v2023?ref_type=heads) and checkout the commit with tag `ESIGMAHMv1`: - - ```bash - git clone https://git.ligo.org/kaushik.paul/lalsuite.git - cd lalsuite - git checkout ESIGMAHMv1 - ``` -* **Installation:** - - It is advised to create a conda environment using [the `igwn` yaml file](https://computing.docs.ligo.org/conda/environments/igwn-py311/) and install the above mentioned fork of `LALSuite` inside it to minimize the dependency issues. Please remove `_x86_64-microarch-level=3=3_haswell` from the yaml file in case you get errors related to microarchitecture mismatch. - - Activate your `conda` environment. Make sure that the `swig` version in this environment is below `4.2.1` (you can check this by running `conda list swig`). If not, install its version `4.2.0` by running `conda install -c conda-forge swig=4.2.0`. - - Create a directory where you want to install ESIGMA. Let the absolute path of this directory be `/path/to/esigmahm`. - - Go back inside the above cloned `LALSuite` fork, and run the following commands: - - ```bash - ./00boot - ./configure --prefix="/path/to/esigmahm" --enable-swig-python --disable-laldetchar --disable-lalpulsar --disable-lalapps --enable-mpi=yes --enable-hdf5 CFLAGS="-Wno-error" CXXFLAGS="-Wno-error" CPPFLAGS="-Wno-error" - make - make install - ``` -* **Configuring `conda` to source this `LALSuite` fork automatically on activating the `conda` environment** - - With your `conda` environment activated, run the following commands: - ```bash - cd $CONDA_PREFIX/etc/conda/activate.d - ln -s /path/to/esigmahm/etc/lalsuite-user-env.sh - ``` - -### Installing `NRSur7dq4` -* Download the `NRSur7dq4` [data file](https://git.ligo.org/lscsoft/lalsuite-extra/-/blob/master/data/lalsimulation/NRSur7dq4.h5) in some directory (for `LALSuite` versions >= 7.25, download [this data file](https://git.ligo.org/waveforms/software/lalsuite-waveform-data/-/blob/main/waveform_data/NRSur7dq4_v1.0.h5?ref_type=heads) instead). Let's say the absolute path to this directory is `/path/to/NRSur7dq4`. -* Append the path of this directory to the shell environment variable `LAL_DATA_PATH` by running: `export LAL_DATA_PATH="$LAL_DATA_PATH:/path/to/NRSur7dq4"` -* To avoid performing the above step in every new terminal session, either add the above command to your `.bashrc` file, or follow the instructions [here](http://gitlab.icts.res.in/akash.maurya/Installation-instructions/wikis/conda-tricks), replacing `PYTHONPATH` with `LAL_DATA_PATH`, to set this environment variable automatically on activating your `conda` environment. - -### Installing `ESIGMAPy` -* Activate your `conda` environment and install `ESIGMAPy` by running: `pip install esigmapy`. - -*** -### Trying out `ESIGMAHM` -The instructions to generate `ESIGMAHM` waveforms and the various functionalities that it offers are detailed in [this tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMA_tutorial.ipynb). - -### Trying out `ESIGMAHM` via `gwsignal` -`ESIGMAHM` can now also be accessed via [`gwsignal`](https://waveforms.docs.ligo.org/reviews/lalsuite/lalsimulation/gwsignal/index.html). See [this notebook](https://github.com/gwnrtools/esigmapy/blob/cfe881a6052f1e9a7c3e066d9fd32462a3af2c89/notebooks/esigma_gwsignal.ipynb) for usage instructions. - -*** - -## :green_square: ESIGMASur - -`ESIGMASur` is a fast time-domain surrogate model of the (2,2)-mode of `ESIGMAHM`. - -### Installation -* Install `ESIGMAPy` by running: - ```bash - pip install git+https://github.com/gwnrtools/esigmapy.git - ``` - It **does not** require the custom LALSuite installation above. - - **Note:** `ESIGMASur` currently supports Python <= 3.12, owing to a compatibility restriction in [`tpi-splines`](https://github.com/mpuerrer/TPI), a dependency of `ESIGMASur`. - -* Download the surrogate data files of `ESIGMASur`, which can be found [on the repo](https://github.com/gwnrtools/esigmapy/tree/master/esigmapy/surrogate/data). Next, set the shell environment variable `ESIGMASUR_DATA_PATH` to the directory where you keep these surrogate data files by running: `export ESIGMASUR_DATA_PATH="/path/to/ESIGMASur"`. - -* Using the inspiral-only surrogate `InspiralESIGMASur` would not require any further dependencies. However, its hybridized IMR version `IMRESIGMASur` will require downloading the NR surrogate data file ([installation instructions above](https://github.com/gwnrtools/esigmapy/tree/master#installing-nrsur7dq4)). - -### Trying out `ESIGMASur` -The usage instructions and the various functionalities of `ESIGMASur` are detailed in [this tutorial notebook](https://github.com/gwnrtools/esigmapy/blob/master/notebooks/ESIGMASur_tutorial.ipynb). - -*** -## 📖 Citation -* If you use `ESIGMAHM` in your work, please cite: - - Paul et al., _"ESIGMAHM: An Eccentric, Spinning inspiral-merger-ringdown waveform model with Higher Modes for the detection and characterization of binary black holes"_, [PhysRevD.111.084074](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.111.084074), arXiv:[2409.13866](https://arxiv.org/abs/2409.13866) (2024) - - ```bibtex - @article{Paul:2024ujx, - author = "Paul, Kaushik and Maurya, Akash and Henry, Quentin and Sharma, Kartikey and Satheesh, Pranav and Divyajyoti and Kumar, Prayush and Mishra, Chandra Kant", - title = "{Eccentric, spinning, inspiral-merger-ringdown waveform model with higher modes for the detection and characterization of binary black holes}", - eprint = "2409.13866", - archivePrefix = "arXiv", - primaryClass = "gr-qc", - doi = "10.1103/PhysRevD.111.084074", - journal = "Phys. Rev. D", - volume = "111", - number = "8", - pages = "084074", - year = "2025" - } - ``` - `ESIGMAHM` is built on the `ENIGMA` framework, which was developed in arXiv:[1609.05933](https://arxiv.org/abs/1609.05933), arXiv:[1711.06276](https://arxiv.org/abs/1711.06276), arXiv:[2008.03313](https://arxiv.org/abs/2008.03313). In addition to citing `ESIGMAHM`, please consider citing them as well. - -* If you use `ESIGMASur` in your work, please cite: - - Maurya et al., _"Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates"_, arXiv:[2510.00116](https://arxiv.org/abs/2510.00116) (2025) - - ```bibtex - @article{Maurya:2025shc, - author = "Maurya, Akash and Kumar, Prayush and Field, Scott E. and Mishra, Chandra Kant and Nee, Peter James and Paul, Kaushik and Pfeiffer, Harald P. and Ravichandran, Adhrit and Varma, Vijay", - title = "{Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates}", - eprint = "2510.00116", - archivePrefix = "arXiv", - primaryClass = "gr-qc", - journal = {}, - month = "9", - year = "2025", - } - ``` - -*** -## :mailbox_with_mail: Contact Us -If you have any questions, issues, or suggestions regarding the model, feel free to reach out to us at esigmahm@icts.res.in! diff --git a/esigmapy.egg-info/SOURCES.txt b/esigmapy.egg-info/SOURCES.txt deleted file mode 100644 index 80b4a79..0000000 --- a/esigmapy.egg-info/SOURCES.txt +++ /dev/null @@ -1,23 +0,0 @@ -LICENSE -README.md -setup.py -esigmapy/__init__.py -esigmapy/blend.py -esigmapy/generator.py -esigmapy/legacy.py -esigmapy/utils.py -esigmapy.egg-info/PKG-INFO -esigmapy.egg-info/SOURCES.txt -esigmapy.egg-info/dependency_links.txt -esigmapy.egg-info/entry_points.txt -esigmapy.egg-info/requires.txt -esigmapy.egg-info/top_level.txt -esigmapy/python_codes/__init__.py -esigmapy/python_codes/esigma_go_terms.py -esigmapy/python_codes/esigma_go_terms_draft.py -esigmapy/python_codes/esigma_pn_inspiral.py -esigmapy/python_codes/esigma_pn_main.py -esigmapy/python_codes/generator_python.py -esigmapy/surrogate/__init__.py -esigmapy/surrogate/generator.py -esigmapy/surrogate/surrogate.py \ No newline at end of file diff --git a/esigmapy.egg-info/dependency_links.txt b/esigmapy.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/esigmapy.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/esigmapy.egg-info/entry_points.txt b/esigmapy.egg-info/entry_points.txt deleted file mode 100644 index e1c29d6..0000000 --- a/esigmapy.egg-info/entry_points.txt +++ /dev/null @@ -1,2 +0,0 @@ -[pycbc.waveform.td] -esigma = esigmapy:pycbc_esigma diff --git a/esigmapy.egg-info/requires.txt b/esigmapy.egg-info/requires.txt deleted file mode 100644 index 612f901..0000000 --- a/esigmapy.egg-info/requires.txt +++ /dev/null @@ -1,7 +0,0 @@ -lscsoft_glue>=2.0.0 -matplotlib>=2.1 -numpy -pycbc -scipy>=1.2.3 -tpi-splines -numba diff --git a/esigmapy.egg-info/top_level.txt b/esigmapy.egg-info/top_level.txt deleted file mode 100644 index 4475386..0000000 --- a/esigmapy.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -esigmapy diff --git a/esigmapy/__pycache__/__init__.cpython-311.pyc b/esigmapy/__pycache__/__init__.cpython-311.pyc deleted file mode 100644 index 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They are returned as `PyCBC` `TimeSeries` objects." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "763108d4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m1 = 20.0 # masses (in solar masses)\n", - "m2 = 30.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = -0.3\n", - "eccentricity = 0.15 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", - "\n", - "hp, hc = 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", - ")\n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.title(\n", - " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"$h$\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "8d52f275", - "metadata": {}, - "source": [ - "One can also specify the eccentricity and mean anomaly at some reference frequency $f_{\\rm{ref}}$ different from the starting frequency $f_{\\rm{low}}$ of the waveform." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2d9e21a6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_ref = 25.0\n", - "f_low = 20.0\n", - "\n", - "# This time, the following eccentricity and mean anomaly values are\n", - "# defined at f_ref, and not at f_low\n", - "eccentricity = 0.15\n", - "mean_anomaly = 30.0 * np.pi / 180.0\n", - "\n", - "hp, hc, orb_vars = 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_ref=f_ref,\n", - " f_lower=f_low,\n", - " delta_t=delta_t,\n", - " modes_to_use=modes_to_use,\n", - " # Returns orbital variables' evolution; explained below\n", - " return_orbital_params=[\"x\", \"e\"],\n", - ")\n", - "\n", - "######### Plotting #################\n", - "from esigmapy.utils import f22_from_x\n", - "\n", - "t_imr = hp.sample_times.data\n", - "t = orb_vars[\"e\"].sample_times.data\n", - "# Orbital variables are only available for\n", - "# inspiral potion, so the IMR waveform's time grid\n", - "# is longer and shifted as compared to inspiral's\n", - "t_shift = t[0] - t_imr[0]\n", - "# Making the inspiral time grid start\n", - "# at the same time as the IMR grid\n", - "t -= t_shift\n", - "e = orb_vars[\"e\"].data\n", - "\n", - "# Getting the orbit-averaged GW frequency from\n", - "# ESIGMA's PN parameter x. This is the frequency\n", - "# that is set when supplying f_lower/f_ref to ESIGMA.\n", - "f_22 = f22_from_x(orb_vars[\"x\"].data, M=m1 + m2)\n", - "if f_ref is None or f_ref <= f_low:\n", - " ref_idx = 0\n", - "else:\n", - " ref_idx = np.argmax(f_22 >= f_ref)\n", - "t_ref = t[ref_idx]\n", - "e_ref = e[ref_idx]\n", - "f_22_ref = f_22[ref_idx]\n", - "\n", - "fig, axs = plt.subplots(2, sharex=True, figsize=(10, 6))\n", - "axs[0].set_title(\n", - " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", - " $e_{{\\rm{{ref}}}}={eccentricity}, l_{{\\rm{{ref}}}}={mean_anomaly:.2f}, f_{{\\rm{{ref}}}}={f_ref}\\rm{{Hz}}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\n", - " $d_L={distance}\\rm{{Mpc}}, \\iota={inclination:.2f}$\"\"\"\n", - ")\n", - "axs[0].plot(t_imr, hp.data, label=r\"$h_+$\")\n", - "axs[0].plot(t_imr, hc.data, label=r\"$h_\\times$\")\n", - "if f_ref >= f_low:\n", - " axs[0].axvline(t_ref, color=\"k\", ls=\"--\", lw=0.7, label=r\"$t_{\\rm{ref}}$\")\n", - " axs[0].set_ylabel(r\"$h$\")\n", - "axs[0].legend()\n", - "\n", - "# Plotting the reference point at which eccentricity was defined\n", - "# in the eccentricity evolution plot\n", - "axsL = axs[1]\n", - "axsR = axsL.twinx()\n", - "axsL.plot(t, e, label=r\"$e(t)$\")\n", - "axsR.plot(t, f_22, label=r\"$2 \\bar{f}_{\\rm{orb}}(t)$\", color=\"green\")\n", - "if f_ref >= f_low:\n", - " axsL.scatter(t_ref, e_ref, color=\"k\", label=r\"Reference point\")\n", - " axsL.axvline(t_ref, color=\"k\", ls=\"--\", lw=0.7)\n", - " axsL.axhline(e_ref, color=\"k\", ls=\"--\", lw=0.7)\n", - "\n", - " axsR.scatter(t_ref, f_22_ref, color=\"k\")\n", - " axsR.axhline(f_22_ref, color=\"k\", ls=\"--\", lw=0.7)\n", - "\n", - "axsL.set_ylabel(r\"$e$\")\n", - "axsR.set_ylabel(r\"$f$ (Hz)\")\n", - "axs[1].legend()\n", - "\n", - "axs[1].set_xlabel(r\"$t (s)$\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "7eba1795", - "metadata": {}, - "source": [ - "### Waveform modes\n", - "One can also generate the spin-weighted spherical harmonic modes via the `get_imr_esigma_modes` function." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "81532049", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m1 = 10.0 # masses (in solar masses)\n", - "m2 = 40.0\n", - "spin1z = 0.7 # dimensionless spins\n", - "spin2z = 0.6\n", - "eccentricity = 0.15 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\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**12 # time grid-spacing (in s)\n", - "\n", - "modes_to_use = [(2, 2), (2, 1), (3, 3), (3, 2), (4, 4), (4, 3)]\n", - "\n", - "modes = 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", - " f_lower=f_low,\n", - " delta_t=delta_t,\n", - " modes_to_use=modes_to_use,\n", - " include_conjugate_modes=False,\n", - ")\n", - "\n", - "fig, axs = plt.subplots(len(modes_to_use), sharex=True, figsize=(10, 10))\n", - "axs[0].set_title(\n", - " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", - " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}, d_L={distance}\\rm{{Mpc}}$\"\"\"\n", - ")\n", - "for i, mode_name in enumerate(modes_to_use):\n", - " ell, m = mode_name\n", - " axs[i].plot(\n", - " modes[mode_name].sample_times.data,\n", - " modes[mode_name].real().data,\n", - " label=rf\"$\\Re(h_{{{ell} {m}}})$\",\n", - " )\n", - " axs[i].plot(\n", - " modes[mode_name].sample_times.data,\n", - " modes[mode_name].imag().data,\n", - " label=rf\"$\\Im(h_{{{ell} {m}}})$\",\n", - " )\n", - " axs[i].legend(loc=2)\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "eb60ec34", - "metadata": {}, - "source": [ - "Other options related to hybridization settings, merger-ringdown model choice, etc. are available as well. One can check them in the respective docstrings of the functions like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "aa78d9e2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function get_imr_esigma_waveform in module esigmapy.generator:\n", - "\n", - "get_imr_esigma_waveform(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)\n", - " Returns IMR GW polarizations constructed using IMR ESIGMA modes\n", - " \n", - " Parameters:\n", - " -----------\n", - " mass1, mass2 -- Binary's component masses (in solar masses)\n", - " f_lower -- Starting frequency of the waveform (in Hz)\n", - " f_ref -- Reference frequency at which to define the\n", - " waveform parameters. We require that\n", - " `f_ref <= f_lower`.\n", - " `f_ref = f_lower` by default.\n", - " delta_t -- Waveform's time grid-spacing (in s)\n", - " spin1z, spin2z -- z-components of component dimensionless\n", - " spins (lies in [0,1))\n", - " eccentricity -- Initial eccentricity\n", - " mean_anomaly -- Mean anomaly of the periastron (in rad)\n", - " inclination -- Inclination (in rad), defined as the angle\n", - " between the orbital angular momentum L and\n", - " the line-of-sight\n", - " coa_phase -- Coalesence phase of the binary (in rad)\n", - " distance -- Luminosity distance to the binary (in Mpc)\n", - " modes_to_use -- GW modes to use. List of tuples (l, |m|)\n", - " mode_to_align_by -- GW mode to use to align inspiral and merger\n", - " in phase and time\n", - " f_mr_transition -- Inspiral to merger transition GW frequency\n", - " (Hz).\n", - " Defaults to the minimum of the Kerr and\n", - " Schwarzschild ISCO frequency\n", - " f_window_mr_transition -- Hybridization frequency window (in Hz).\n", - " Disabled by the default value (None). In\n", - " such a case, the hybridization proceeds\n", - " over a window of `num_hyb_orbits` orbital\n", - " cycles (1 orbital cycle ~ 2 GW cycles)\n", - " that ends at the frequency value given by\n", - " `f_mr_transition`.\n", - " Also see `keep_f_mr_transition_at_center`\n", - " to choose the position of `f_mr_transition`\n", - " within this window.\n", - " num_hyb_orbits -- number of orbital cycles to blend over.\n", - " Only used if f_window_mr_transition is not\n", - " specified.\n", - " blend_using_avg_orbital_frequency -- If True, the orbit averaged\n", - " frequency during the inspiral is used to\n", - " blend modes, instead of the modes'\n", - " frequency.\n", - " keep_f_mr_transition_at_center -- If True, `f_mr_transition` is kept at\n", - " the center of the hybridization window.\n", - " Otherwise, it's kept at the end of the\n", - " window (default).\n", - " merger_ringdown_approximant -- Choose merger-ringdown model. Tested\n", - " choices: [NRSur7dq4, SEOBNRv4PHM]\n", - " return_hybridization_info -- If True, returns hybridization related data\n", - " return_orbital_params -- If True, returns the orbital evolution of\n", - " all the orbital elements (in\n", - " geometrized units). Can also be a list of\n", - " orbital variable names to return\n", - " only those specific variables. Available\n", - " orbital variables names are:\n", - " ['x', 'e', 'l', 'phi', 'phidot', 'r', 'rdot'].\n", - " Note that these are available only for the\n", - " inspiral portion of the waveform!\n", - " failsafe -- If True, we make reasonable choices for the\n", - " user, if the inputs to this method lead\n", - " into exceptions.\n", - " verbose -- Verbosity level. Available values are: 0, 1, 2\n", - " \n", - " Returns:\n", - " --------\n", - " hp, hc -- Plus and cross IMR GW polarizations PyCBC TimeSeries\n", - " orbital_vars_dict -- Dictionary of evolution of orbital elements.\n", - " Returned only if return_orbital_params is specified\n", - " retval -- Hybridization related data.\n", - " Returned only if return_hybridization_info is True\n", - "\n" - ] - } - ], - "source": [ - "help(esigmapy.get_imr_esigma_waveform)" - ] - }, - { - "cell_type": "markdown", - "id": "52eb4d06", - "metadata": {}, - "source": [ - "## InspiralESIGMA" - ] - }, - { - "cell_type": "markdown", - "id": "2c052713", - "metadata": {}, - "source": [ - "One can also generate the inspiral-only wavefroms and modes via the `get_inspiral_esigma_waveform` and `get_inspiral_esigma_modes` functions respectively. Unlike the full IMR waveform which uses a quasi-circular merger-ringdown model and thus forbids using large starting eccentricities, the inspiral waveforms can be generated for arbitrarily eccentric (bounded) orbits (i.e. with $0 \\leq e < 1$)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "16d39835", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m1 = 10.0 # masses (in solar masses)\n", - "m2 = 50.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = 0.6\n", - "eccentricity = 0.55 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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 = 8 # starting frequency (in Hz)\n", - "delta_t = 1 / 2**12 # time grid-spacing (in s)\n", - "\n", - "hp, hc = esigmapy.get_inspiral_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", - ")\n", - "plt.figure(figsize=(10, 4))\n", - "plt.title(\n", - " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"$h$\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4a5b0cb7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "modes_to_use = [(2, 2), (2, 1), (3, 3), (3, 2), (4, 4), (4, 3)]\n", - "\n", - "modes = 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", - " f_lower=f_low,\n", - " delta_t=delta_t,\n", - " modes_to_use=modes_to_use,\n", - " include_conjugate_modes=False,\n", - ")\n", - "\n", - "fig, axs = plt.subplots(len(modes_to_use), sharex=True, figsize=(10, 10))\n", - "axs[0].set_title(\n", - " rf\"\"\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", - " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}, d_L={distance}\\rm{{Mpc}}$\"\"\"\n", - ")\n", - "for i, mode_name in enumerate(modes_to_use):\n", - " ell, m = mode_name\n", - " axs[i].plot(\n", - " modes[mode_name].sample_times.data,\n", - " modes[mode_name].real().data,\n", - " label=rf\"$\\Re(h_{{{ell} {m}}})$\",\n", - " )\n", - " axs[i].plot(\n", - " modes[mode_name].sample_times.data,\n", - " modes[mode_name].imag().data,\n", - " label=rf\"$\\Im(h_{{{ell} {m}}})$\",\n", - " )\n", - " axs[i].legend(loc=2)\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "fbd28174", - "metadata": {}, - "source": [ - "## Evolution of orbital elements\n", - "The evolution of binary's orbital elements can also be accessed, but only for the *inspiral part of the dynamics*. These can be accessed via the argument `return_orbital_params` in all of the above discussed waveform/mode functions. The available orbital elements are\n", - "\n", - "- $x$: The post-Newtonian (PN) parameter. It's related to the orbit-averaged (azimuthal) orbital frequency\n", - "- $e$: Orbital eccentricity\n", - "- $l$: Mean anomaly\n", - "- $r$: Relative separation\n", - "- $\\dot{r}$: Radial velocity\n", - "- $\\phi$: Orbital phase\n", - "- $\\dot{\\phi}$: Angular velocity\n", - "\n", - "All of these are returned in geometric units ($G=c=1$)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5974efc1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, '$t (s)$')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m1 = 10.0 # masses (in solar masses)\n", - "m2 = 50.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = 0.6\n", - "eccentricity = 0.15 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", - "orb_params_list = [\"x\", \"e\", \"l\", \"r\", \"rdot\", \"phi\", \"phidot\"]\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 # starting frequency (in Hz)\n", - "delta_t = 1 / 2**12 # time grid-spacing (in s)\n", - "\n", - "orb_vars, hp, hc = esigmapy.get_inspiral_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", - " return_orbital_params=orb_params_list,\n", - ")\n", - "\n", - "fig, axs = plt.subplots(len(orb_params_list), sharex=True, figsize=(10, 10))\n", - "axs[0].set_title(\n", - " rf\"$m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}, e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\"\n", - ")\n", - "\n", - "orb_var_latex_labels = {\n", - " \"x\": r\"$x$\",\n", - " \"e\": r\"$e$\",\n", - " \"l\": r\"$l$\",\n", - " \"r\": r\"r\",\n", - " \"rdot\": r\"$\\dot{r}$\",\n", - " \"phi\": \"$\\phi$\",\n", - " \"phidot\": r\"$\\dot{\\phi}$\",\n", - "}\n", - "\n", - "for i, orb_params_name in enumerate(orb_params_list):\n", - " axs[i].plot(\n", - " orb_vars[orb_params_name].sample_times.data,\n", - " orb_vars[orb_params_name].data,\n", - " label=rf\"{orb_var_latex_labels[orb_params_name]}\",\n", - " )\n", - " axs[i].legend(loc=2)\n", - "plt.xlabel(r\"$t (s)$\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esigma-igwn39", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb deleted file mode 100644 index 363fcab..0000000 --- a/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb deleted file mode 100644 index 5c91f21..0000000 --- a/notebooks/.ipynb_checkpoints/esigma_python-checkpoint.ipynb +++ /dev/null @@ -1,359 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", - "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", - "text": [ - "No version information file '.version' found\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import esigmapy" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b962168c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "x=os.environ.get(\"ModePNOrder\")\n", - "print('Poof!') if x is None else int(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a666270a-c644-473b-8559-57df6e16217c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Orbital evolution took: 7.503528344001097 seconds\n", - "3\n", - "3\n", - "Modes generation took: 0.2238857789998292 seconds\n" - ] - } - ], - "source": [ - "from esigmapy.python_codes import generator_python as gp\n", - "\n", - "m1 = 20.0 # masses (in solar masses)\n", - "m2 = 30.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = -0.3\n", - "eccentricity = 0.15 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", - "\n", - "# hp, hc = gp.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", - "# )\n", - "\n", - "modes_py = gp.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" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "16244db1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Orbital evolution took: 0.05242229400028009 seconds\n", - "Modes generation took: 0.14869545600231504 seconds\n" - ] - } - ], - "source": [ - "# m1 = 20.0 # masses (in solar masses)\n", - "# m2 = 30.0\n", - "# spin1z = 0.5 # dimensionless spins\n", - "# spin2z = -0.3\n", - "# eccentricity = 0.15 # starting eccentricity\n", - "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", - "\n", - "# hp, hc = gp.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", - "# )\n", - "\n", - "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" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9fc2cdd6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hp22_py_real = modes_py[(2, 2)].real()\n", - "hp22_c_real = modes_c[(2, 2)].real()\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", - "# hp.plot(label=r\"$h_+$\")\n", - "# hc.plot(label=r\"$h_\\times$\")\n", - "\n", - "plt.plot(hp22_py_real.sample_times,hp22_py_real,label='Python')\n", - "plt.plot(hp22_c_real.sample_times+0.0185,hp22_c_real,label='C',ls='--')\n", - "\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"Re [$h_{22}$]\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c208204c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hp22_py_real = modes_py[(2, 2)].real()\n", - "hp22_c_real = modes_c[(2, 2)].real()\n", - "plt.figure(figsize=(10, 4))\n", - "plt.title(\n", - " rf\"\"\"INSPIRAL-ESIGMA\n", - " ==============================\n", - " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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_py_real.sample_times,hp22_py_real,label='Python')\n", - "plt.plot(hp22_c_real.sample_times+0.019,hp22_c_real,label='C',ls='--')\n", - "\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"Re [$h_{22}$]\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys([(2, 2), (2, -2)])\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(modes_py.keys())\n", - "hp22 = modes_py[(2, 2)].real()\n", - "hc22 = modes_py[(2, 2)].imag()\n", - "plt.figure(figsize=(10, 4))\n", - "plt.title(\n", - " rf\"\"\"INSPIRAL-ESIGMA\n", - " ==============================\n", - " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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.sample_times,hp22,label=r\"Re [$h_{22}$]\")\n", - "plt.plot(hc22.sample_times,hc22,label=r\"Im [$h_{22}$]\")\n", - "\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"$h$\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "173f050f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From be8fd609da87f9eccb89a7509cfb5582758f6169 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 30 Apr 2026 12:41:52 +0530 Subject: [PATCH 12/36] Updated notebook --- Untitled.ipynb | 33 --------- notebooks/esigma_python.ipynb | 129 +++++++--------------------------- 2 files changed, 26 insertions(+), 136 deletions(-) delete mode 100644 Untitled.ipynb diff --git a/Untitled.ipynb b/Untitled.ipynb deleted file mode 100644 index 89e3f04..0000000 --- a/Untitled.ipynb +++ /dev/null @@ -1,33 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "c90ab439-6d4c-406b-8602-1bcba3c748b0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esigmapy-dev", - "language": "python", - "name": "esigmapy-dev" - }, - "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 -} diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb index 6cf03cc..156fbc3 100644 --- a/notebooks/esigma_python.ipynb +++ b/notebooks/esigma_python.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", "metadata": { "collapsed": true, @@ -10,45 +10,7 @@ "outputs_hidden": true } }, - "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", - "text": [ - "No version information file '.version' found\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -57,19 +19,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "b962168c", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Poof!\n" + ] } ], "source": [ @@ -80,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a666270a-c644-473b-8559-57df6e16217c", "metadata": {}, "outputs": [ @@ -88,10 +47,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Orbital evolution took: 7.503528344001097 seconds\n", - "3\n", - "3\n", - "Modes generation took: 0.2238857789998292 seconds\n" + "Orbital evolution took: 7.925930153000081 seconds\n", + "8\n", + "8\n", + "Modes generation took: 1.3593262409995077 seconds\n" ] } ], @@ -144,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "16244db1", "metadata": {}, "outputs": [ @@ -152,8 +111,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Orbital evolution took: 0.05242229400028009 seconds\n", - "Modes generation took: 0.14869545600231504 seconds\n" + "Orbital evolution took: 0.011978193000686588 seconds\n", + "Modes generation took: 0.03905334000046423 seconds\n" ] } ], @@ -204,13 +163,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "9fc2cdd6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -234,59 +193,22 @@ "# hp.plot(label=r\"$h_+$\")\n", "# hc.plot(label=r\"$h_\\times$\")\n", "\n", - "plt.plot(hp22_py_real.sample_times,hp22_py_real,label='Python')\n", - "plt.plot(hp22_c_real.sample_times+0.0185,hp22_c_real,label='C',ls='--')\n", - "\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"Re [$h_{22}$]\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c208204c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hp22_py_real = modes_py[(2, 2)].real()\n", - "hp22_c_real = modes_c[(2, 2)].real()\n", - "plt.figure(figsize=(10, 4))\n", - "plt.title(\n", - " rf\"\"\"INSPIRAL-ESIGMA\n", - " ==============================\n", - " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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", + "hp22_py_real.start_time=0\n", + "hp22_c_real.start_time=0\n", "\n", "plt.plot(hp22_py_real.sample_times,hp22_py_real,label='Python')\n", - "plt.plot(hp22_c_real.sample_times+0.019,hp22_c_real,label='C',ls='--')\n", + "plt.plot(hp22_c_real.sample_times,hp22_c_real,label='C',ls='--')\n", "\n", "plt.xlabel(r\"$t (s)$\")\n", "plt.ylabel(r\"Re [$h_{22}$]\")\n", "plt.legend()\n", + "plt.grid()\n", "plt.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", "metadata": {}, "outputs": [ @@ -299,7 +221,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -329,6 +251,7 @@ "plt.xlabel(r\"$t (s)$\")\n", "plt.ylabel(r\"$h$\")\n", "plt.legend()\n", + "plt.grid()\n", "plt.tight_layout()" ] }, @@ -343,7 +266,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "esigma-dev", "language": "python", "name": "python3" }, From 2c7c6f8563ccd5ee799fa0015d7358af906b700a Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Sat, 2 May 2026 23:56:10 +0530 Subject: [PATCH 13/36] commented out redundant functions, fixed array definitions --- .../__pycache__/__init__.cpython-311.pyc | Bin 180 -> 0 bytes .../esigma_go_terms_draft.cpython-311.pyc | Bin 63964 -> 0 bytes .../esigma_pn_inspiral.cpython-311.pyc | Bin 125694 -> 0 bytes .../esigma_pn_main.cpython-311.pyc | Bin 13950 -> 0 bytes .../generator_python.cpython-311.pyc | Bin 47909 -> 0 bytes esigmapy/python_codes/generator_python.py | 411 +++++++++--------- 6 files changed, 206 insertions(+), 205 deletions(-) delete mode 100644 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zjE#=wtg>Pd>rKOmD|fS_2<1GJ(3yo^C+y)!J??*|doEcyFMeYd8b?<4BP)lUqO@qG zICxc2y`7yNor3bW_`lPWza?;kzz+y8jXF9(Rs4tm(`h5+AJ*7eua`Ax19Ls@9az&&uyD<%`PUN*q97Ot#{B2?|8 z=SFbU4By)jB4z{S^!50&PK3Ks?3=CQv!O)J)9AYm>_su4^Eg(7-%-Y3s&8GE3)3Q!k1Q{!|>Vi>TGReHdvKy z@1}1{XO{2DHndQ=JOL`#j#o=5GaKMD9?r$er4a|^Lg_#vjXrqAm*fAA*#r^j;~;Bg F{}0(2D#`!= diff --git a/esigmapy/python_codes/generator_python.py b/esigmapy/python_codes/generator_python.py index 49f7259..3d3c428 100644 --- a/esigmapy/python_codes/generator_python.py +++ b/esigmapy/python_codes/generator_python.py @@ -12,6 +12,7 @@ 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 @@ -469,208 +470,208 @@ def get_inspiral_esigma_waveform_py( return t, hp, hc -def _get_window_start(freq_, delta_t_, delta_phi_, direction="forward"): - """Integrates frequency backward/forward from the start/end until - `delta_phi_` radians of orbital phase has elapsed. - - Args: - freq_ (numpy.array): Frequency time series, uniformly sampled - delta_t_ (float64): Step size in time - delta_phi_ (float64): Phase shift in radians to integrate across - direction (str, optional): Direction of integration in time. - Defaults to "forward". - - Returns: - int: Index in frequency array where `delta_phi` is reached - """ - from scipy import integrate - - if direction == "backward": - for idx in range(len(freq_) - 2, 0, -1): - # this could be optimized to not repeat integration - if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): - return idx - elif direction == "forward": - for idx in range(1, len(freq_)): - # this could be optimized to not repeat integration - if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( - delta_phi_ - ): - return idx - - -def _get_transition_frequency_window( - orbital_phase, - orbital_freq, - delta_t, - f_mr_transition, # orbital frequency - num_hyb_orbits, - keep_f_mr_transition_at_center, - blend_using_avg_orbital_frequency, - failsafe, - verbose=False, -): - """ - This function first finds where the `orbital_freq` crosses the transition - frequency `f_mr_transition`, and finds the point in time that is - `num_hyb_orbits` orbits before that time. This marks the start of the - hybridization window, and the orbital frequency at that time is returned - - Parameters: - ----------- - orbital_phase -- Orbital phase evolution - orbital_freq -- Orbital frequency evolution - delta_t -- Waveform's time grid-spacing (s) - f_mr_transition -- Inspiral to merger transition frequency - (Hz). This should be the same (mode/orbital) - frequency as passed for `orbital_freq`. - num_hyb_orbits -- number of orbital cycles to blend over. - 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). - blend_using_avg_orbital_frequency -- If True, the orbit averaged - frequency during the inspiral is used to - blend modes, instead of the modes' - frequency. - failsafe -- If True, we make reasonable choices for the - user, if the inputs to this method lead - into exceptions. - verbose -- Verbosity flag - - Returns: - -------- - f_window_mr_transition -- Hybridization frequency window (in Hz). - """ - transition_idx = ( - len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) - ) # index at which orbital_freq becomes just smaller than transition orbital - # frequency, towards the end of waveform - - if verbose > 4: - print( - f"""Transition frequency found at index {transition_idx} - in the orbital frequency array of length {len(orbital_freq)}""" - ) - - if blend_using_avg_orbital_frequency: - # We integrate `orbital_freq` to obtain `orbital_phase` - from scipy import integrate - - orbital_phase = integrate.cumulative_trapezoid( - orbital_freq, dx=delta_t, initial=0 - ) - if len(orbital_phase) != len(orbital_freq): - raise RuntimeError( - f"""Something went wrong while integrating orbital frequency. -They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" - ) - - if keep_f_mr_transition_at_center: - # Orbital cycle based hybridization that keeps f_mr_transition at - # the hybridization frequency window's midpoint - if blend_using_avg_orbital_frequency: - window_start_idx = _get_window_start( - orbital_freq[: transition_idx + 1], - delta_t, - num_hyb_orbits * np.pi, - direction="backward", - ) - window_end_idx = transition_idx + _get_window_start( - orbital_freq[transition_idx:], - delta_t, - num_hyb_orbits * np.pi, - direction="forward", - ) - # FAILSAFE mode behavior - if window_start_idx is None: - if failsafe: - window_start_idx = 0 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform before the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or increase the -inspiral-to-merger transition frequency. `window_start_idx` is None.""" - ) - if window_end_idx is None: - if failsafe: - window_end_idx = len(orbital_freq) - 1 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform after the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or decrease the -inspiral-to-merger transition frequency. `window_end_idx` is None.""" - ) - else: - # phase is always a monotonically increasing function, - # hence using the more efficient np.searchsorted method - window_start_idx = -1 + np.searchsorted( - orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi - ) - window_end_idx = np.searchsorted( - orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi - ) - f_window_mr_transition = 2 * np.min( - [ - orbital_freq[window_end_idx] - f_mr_transition, - f_mr_transition - orbital_freq[window_start_idx], - ] - ) - elif not keep_f_mr_transition_at_center: - # Orbital cycle based hybridization that keeps f_mr_transition at - # the hybridization frequency window's right end - if blend_using_avg_orbital_frequency: - window_start_idx = _get_window_start( - orbital_freq[: transition_idx + 1], - delta_t, - 2 * num_hyb_orbits * np.pi, - direction="backward", - ) - if window_start_idx is None: - if failsafe: - window_start_idx = 0 - else: - raise RuntimeError( - f"""Requested number of orbits to blend over not available -in the waveform before the transition frequency. Either decrease the number of -orbits to blend over (currently {num_hyb_orbits}) or increase the -inspiral-to-merger transition frequency. `window_start_idx` is None.""" - ) - else: - # Orbital cycle based hybridization that keeps f_mr_transition - # at the hybridization frequency window's end - window_start_idx = ( - np.searchsorted( - orbital_phase, - orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, - ) - - 1 - ) - f_window_mr_transition = ( - orbital_freq[transition_idx] - orbital_freq[window_start_idx] - ) - - if verbose > 4: - print( - f"""The transition is to happen in a window from - frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, - between indices: [{window_start_idx}, {transition_idx}]""" - ) - else: - raise RuntimeError( - """We should never reach here. Did you set a non-bool value for the - flag `keep_f_mr_transition_at_center`?""" - ) - - if verbose > 4: - print(f"""f_window_mr_transition = {f_window_mr_transition}""") - - return f_window_mr_transition - - -def get_imr_esigma_modes( +# def _get_window_start(freq_, delta_t_, delta_phi_, direction="forward"): +# """Integrates frequency backward/forward from the start/end until +# `delta_phi_` radians of orbital phase has elapsed. + +# Args: +# freq_ (numpy.array): Frequency time series, uniformly sampled +# delta_t_ (float64): Step size in time +# delta_phi_ (float64): Phase shift in radians to integrate across +# direction (str, optional): Direction of integration in time. +# Defaults to "forward". + +# Returns: +# int: Index in frequency array where `delta_phi` is reached +# """ +# from scipy import integrate + +# if direction == "backward": +# for idx in range(len(freq_) - 2, 0, -1): +# # this could be optimized to not repeat integration +# if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): +# return idx +# elif direction == "forward": +# for idx in range(1, len(freq_)): +# # this could be optimized to not repeat integration +# if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( +# delta_phi_ +# ): +# return idx + + +# def _get_transition_frequency_window( +# orbital_phase, +# orbital_freq, +# delta_t, +# f_mr_transition, # orbital frequency +# num_hyb_orbits, +# keep_f_mr_transition_at_center, +# blend_using_avg_orbital_frequency, +# failsafe, +# verbose=False, +# ): +# """ +# This function first finds where the `orbital_freq` crosses the transition +# frequency `f_mr_transition`, and finds the point in time that is +# `num_hyb_orbits` orbits before that time. This marks the start of the +# hybridization window, and the orbital frequency at that time is returned + +# Parameters: +# ----------- +# orbital_phase -- Orbital phase evolution +# orbital_freq -- Orbital frequency evolution +# delta_t -- Waveform's time grid-spacing (s) +# f_mr_transition -- Inspiral to merger transition frequency +# (Hz). This should be the same (mode/orbital) +# frequency as passed for `orbital_freq`. +# num_hyb_orbits -- number of orbital cycles to blend over. +# 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). +# blend_using_avg_orbital_frequency -- If True, the orbit averaged +# frequency during the inspiral is used to +# blend modes, instead of the modes' +# frequency. +# failsafe -- If True, we make reasonable choices for the +# user, if the inputs to this method lead +# into exceptions. +# verbose -- Verbosity flag + +# Returns: +# -------- +# f_window_mr_transition -- Hybridization frequency window (in Hz). +# """ +# transition_idx = ( +# len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) +# ) # index at which orbital_freq becomes just smaller than transition orbital +# # frequency, towards the end of waveform + +# if verbose > 4: +# print( +# f"""Transition frequency found at index {transition_idx} +# in the orbital frequency array of length {len(orbital_freq)}""" +# ) + +# if blend_using_avg_orbital_frequency: +# # We integrate `orbital_freq` to obtain `orbital_phase` +# from scipy import integrate + +# orbital_phase = integrate.cumulative_trapezoid( +# orbital_freq, dx=delta_t, initial=0 +# ) +# if len(orbital_phase) != len(orbital_freq): +# raise RuntimeError( +# f"""Something went wrong while integrating orbital frequency. +# They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" +# ) + +# if keep_f_mr_transition_at_center: +# # Orbital cycle based hybridization that keeps f_mr_transition at +# # the hybridization frequency window's midpoint +# if blend_using_avg_orbital_frequency: +# window_start_idx = _get_window_start( +# orbital_freq[: transition_idx + 1], +# delta_t, +# num_hyb_orbits * np.pi, +# direction="backward", +# ) +# window_end_idx = transition_idx + _get_window_start( +# orbital_freq[transition_idx:], +# delta_t, +# num_hyb_orbits * np.pi, +# direction="forward", +# ) +# # FAILSAFE mode behavior +# if window_start_idx is None: +# if failsafe: +# window_start_idx = 0 +# else: +# raise RuntimeError( +# f"""Requested number of orbits to blend over not available +# in the waveform before the transition frequency. Either decrease the number of +# orbits to blend over (currently {num_hyb_orbits}) or increase the +# inspiral-to-merger transition frequency. `window_start_idx` is None.""" +# ) +# if window_end_idx is None: +# if failsafe: +# window_end_idx = len(orbital_freq) - 1 +# else: +# raise RuntimeError( +# f"""Requested number of orbits to blend over not available +# in the waveform after the transition frequency. Either decrease the number of +# orbits to blend over (currently {num_hyb_orbits}) or decrease the +# inspiral-to-merger transition frequency. `window_end_idx` is None.""" +# ) +# else: +# # phase is always a monotonically increasing function, +# # hence using the more efficient np.searchsorted method +# window_start_idx = -1 + np.searchsorted( +# orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi +# ) +# window_end_idx = np.searchsorted( +# orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi +# ) +# f_window_mr_transition = 2 * np.min( +# [ +# orbital_freq[window_end_idx] - f_mr_transition, +# f_mr_transition - orbital_freq[window_start_idx], +# ] +# ) +# elif not keep_f_mr_transition_at_center: +# # Orbital cycle based hybridization that keeps f_mr_transition at +# # the hybridization frequency window's right end +# if blend_using_avg_orbital_frequency: +# window_start_idx = _get_window_start( +# orbital_freq[: transition_idx + 1], +# delta_t, +# 2 * num_hyb_orbits * np.pi, +# direction="backward", +# ) +# if window_start_idx is None: +# if failsafe: +# window_start_idx = 0 +# else: +# raise RuntimeError( +# f"""Requested number of orbits to blend over not available +# in the waveform before the transition frequency. Either decrease the number of +# orbits to blend over (currently {num_hyb_orbits}) or increase the +# inspiral-to-merger transition frequency. `window_start_idx` is None.""" +# ) +# else: +# # Orbital cycle based hybridization that keeps f_mr_transition +# # at the hybridization frequency window's end +# window_start_idx = ( +# np.searchsorted( +# orbital_phase, +# orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, +# ) +# - 1 +# ) +# f_window_mr_transition = ( +# orbital_freq[transition_idx] - orbital_freq[window_start_idx] +# ) + +# if verbose > 4: +# print( +# f"""The transition is to happen in a window from +# frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, +# between indices: [{window_start_idx}, {transition_idx}]""" +# ) +# else: +# raise RuntimeError( +# """We should never reach here. Did you set a non-bool value for the +# flag `keep_f_mr_transition_at_center`?""" +# ) + +# if verbose > 4: +# print(f"""f_window_mr_transition = {f_window_mr_transition}""") + +# return f_window_mr_transition + + +def get_imr_esigma_modes_py( mass1, mass2, f_lower, @@ -1027,7 +1028,7 @@ def get_imr_esigma_modes( 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 + modes_mr_numpy[key] = hlm_mr.mode.data.data hlm_mr = hlm_mr.next try: @@ -1047,7 +1048,7 @@ def get_imr_esigma_modes( ) except Exception as exc: print( - f"""Inspiral + MergerRingdown attachment failed. Its very likely + 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]} """ @@ -1086,7 +1087,7 @@ def get_imr_esigma_modes( return modes_imr -def get_imr_esigma_waveform( +def get_imr_esigma_waveform_py( mass1, mass2, f_lower, From e9faebb436ed39cfff60c32c93e156a753972517 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Sat, 2 May 2026 23:56:38 +0530 Subject: [PATCH 14/36] updated gitignore to not sync unnecessary files and folders --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 966cc98..38851b9 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,6 @@ venv/ # ipynb folders .ipynb_checkpoints/ + +# vs code +.vscode From 6a2be91a7a860c11c4dac47bd69b01a33f1f0874 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Sat, 2 May 2026 23:57:08 +0530 Subject: [PATCH 15/36] fixed PN order definition --- esigmapy/python_codes/esigma_pn_inspiral.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index 444cc8d..923efac 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -2638,12 +2638,11 @@ def de_dt(radiation_pn_order: int, if radiation_pn_order >= 6: inst += e_dot_3pn(e, eta, x) * x3 - inst += e_dot_3_5pn(e, eta) * x3 * np.sqrt(x) 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 += 0.0 # placeholder for higher order terms, not implemented + inst += e_dot_3_5pn(e, eta) * x3 * np.sqrt(x) if radiation_pn_order >= 8: inst += 0.0 # placeholder for higher order terms, not implemented From 57366ed1cb569ad98d5aa3734a918ad9d8ae1c1d Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Sat, 2 May 2026 23:57:36 +0530 Subject: [PATCH 16/36] Print the PN orders --- esigmapy/python_codes/esigma_pn_main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index 9675e6e..69d9c14 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -236,7 +236,7 @@ def inspiral_esigma_mode_from_dynamics( scaling inside compute_mode_from_dynamics (matches the C end result). """ mode_pn_order = int(os.environ.get("ModePNOrder", ModePNOrderDefault)) - print(mode_pn_order) + print('Mode PN order =',mode_pn_order) return compute_mode_from_dynamics( l, m, @@ -363,7 +363,7 @@ def inspiral_esigma_dynamics( # PN / radiation order (mirror the C env-var logic; default hardcoded) import os rad_pn_order = int(os.environ.get("RadiationPNOrder", RadiationPNOrderDefault)) - + print('Radiation PN order =',rad_pn_order) params = Params( eta=eta, radiation_pn_order=rad_pn_order, From 84b8938f769496ead422ca5c9eab6c63c1254abb Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Sat, 2 May 2026 23:58:01 +0530 Subject: [PATCH 17/36] Cleanup notebook --- notebooks/esigma_python.ipynb | 305 +++++++++++++++++++++++++--------- 1 file changed, 227 insertions(+), 78 deletions(-) diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb index 156fbc3..8bc7ea6 100644 --- a/notebooks/esigma_python.ipynb +++ b/notebooks/esigma_python.ipynb @@ -4,12 +4,7 @@ "cell_type": "code", "execution_count": 2, "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -27,19 +22,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Poof!\n" + "Poof!\n", + ":/home/samanwaya/nrsur7dq4\n" ] } ], "source": [ "import os\n", "x=os.environ.get(\"ModePNOrder\")\n", - "print('Poof!') if x is None else int(x)" + "print('Poof!' if x is None else int(x))\n", + "\n", + "y=os.environ.get(\"LAL_DATA_PATH\")\n", + "print('Poof!' if y is None else y)" + ] + }, + { + "cell_type": "markdown", + "id": "ff51d368", + "metadata": {}, + "source": [ + "### Inspiral ESIGMA modes" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "a666270a-c644-473b-8559-57df6e16217c", "metadata": {}, "outputs": [ @@ -47,10 +54,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Orbital evolution took: 7.925930153000081 seconds\n", - "8\n", - "8\n", - "Modes generation took: 1.3593262409995077 seconds\n" + "================ Python ================\n", + "Radiation PN order = 8\n", + "Orbital evolution took: 3.262218417999975 seconds\n", + "Mode PN order = 8\n", + "Mode PN order = 8\n", + "Modes generation took: 0.3844245680011227 seconds\n", + "================= C =================\n", + "Orbital evolution took: 0.05307760899813729 seconds\n", + "Modes generation took: 0.011499872998683713 seconds\n" ] } ], @@ -69,23 +81,26 @@ "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**12 # time grid-spacing (in s)\n", + "delta_t = 1 / 2**10 # time grid-spacing (in s)\n", "\n", - "# hp, hc = gp.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", - "# )\n", + "print('================ Python ================')\n", + "inspiral_modes_py = gp.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", - "modes_py = gp.get_inspiral_esigma_modes_py(\n", + "print('================= C =================')\n", + "inspiral_modes_c = esigmapy.get_inspiral_esigma_modes(\n", " mass1=m1,\n", " mass2=m2,\n", " spin1z=spin1z,\n", @@ -103,49 +118,183 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "16244db1", + "execution_count": 21, + "id": "9fc2cdd6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real_inspiral = inspiral_modes_py[(2, 2)].real()\n", + "hp22_c_real_inspiral = inspiral_modes_c[(2, 2)].real()\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", + "# hp.plot(label=r\"$h_+$\")\n", + "# hc.plot(label=r\"$h_\\times$\")\n", + "\n", + "hp22_py_real_inspiral.start_time=0\n", + "hp22_c_real_inspiral.start_time=0\n", + "\n", + "plt.plot(hp22_py_real_inspiral.sample_times,hp22_py_real_inspiral,label='Python')\n", + "plt.plot(hp22_c_real_inspiral.sample_times,hp22_c_real_inspiral,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "# plt.xlim(0.2,0.3)\n", + "# plt.ylim(-1e-21,1e-21)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys([(2, 2), (2, -2)])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(inspiral_modes_py.keys())\n", + "hp22_inspiral = inspiral_modes_py[(2, 2)].real()\n", + "hc22_inspiral = inspiral_modes_py[(2, 2)].imag()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"Inspiral-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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_inspiral.sample_times,hp22_inspiral,label=r\"Re [$h_{22}$]\")\n", + "plt.plot(hc22_inspiral.sample_times,hc22_inspiral,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": "4d79b9f9", + "metadata": {}, + "source": [ + "### IMR ESIGMA modes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "311c00be", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Orbital evolution took: 0.011978193000686588 seconds\n", - "Modes generation took: 0.03905334000046423 seconds\n" + "================ Python ================\n", + "Radiation PN order = 8\n", + "Orbital evolution took: 8.60927068700039 seconds\n", + "Mode PN order = 8\n", + "Mode PN order = 8\n", + "Modes generation took: 1.4204796830017585 seconds\n", + "Generating MR waveform from 49.62645847549604Hz...\n", + "Hybridizing the following modes: [(2, 2), (2, -2)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", + "INSPIRAL mode (2, 2) goes from 15.51002742012073Hz to 133.6547676158098Hz\n", + "MERGER mode (2, 2) goes from 49.67733142367634Hz to 351.6339524069211Hz\n", + "INSPIRAL mode (2, -2) goes from -133.6547676158098Hz to -15.51002742012073Hz\n", + "MERGER mode (2, -2) goes from -352.64823272619185Hz to -49.67650156836734Hz\n", + "blended.\n", + "================= C =================\n", + "Orbital evolution took: 0.012976661000720924 seconds\n", + "Modes generation took: 0.045316491999983555 seconds\n", + "Generating MR waveform from 49.79651161822471Hz...\n", + "Hybridizing the following modes: [(2, 2), (2, -2)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", + "INSPIRAL mode (2, 2) goes from 15.523337342773294Hz to 126.35235015121037Hz\n", + "MERGER mode (2, 2) goes from 49.817281724494144Hz to 352.4487639118021Hz\n", + "INSPIRAL mode (2, -2) goes from -126.35235015121037Hz to -15.523337342773294Hz\n", + "MERGER mode (2, -2) goes from -353.44200854926675Hz to -49.81639786367156Hz\n", + "blended.\n" ] } ], "source": [ - "# m1 = 20.0 # masses (in solar masses)\n", - "# m2 = 30.0\n", - "# spin1z = 0.5 # dimensionless spins\n", - "# spin2z = -0.3\n", - "# eccentricity = 0.15 # starting eccentricity\n", - "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", + "from esigmapy.python_codes import generator_python as gp\n", "\n", - "# distance = 400.0 # source luminosity distance (in Mpc)\n", - "# inclination = 30 * np.pi / 180.0 # orbital inclination with line-of-sight\n", + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.3\n", + "eccentricity = 0.15 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", + "f_low = 20.0 # starting frequency (in Hz)\n", + "delta_t = 1 / 2**12 # time grid-spacing (in s)\n", "\n", - "# hp, hc = gp.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", - "# )\n", + "print('================ Python ================')\n", + "imr_modes_py = gp.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", + " verbose=True\n", + ")\n", "\n", - "modes_c = esigmapy.get_inspiral_esigma_modes(\n", + "print('================= C =================')\n", + "imr_modes_c = esigmapy.get_imr_esigma_modes(\n", " mass1=m1,\n", " mass2=m2,\n", " spin1z=spin1z,\n", @@ -163,13 +312,13 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "9fc2cdd6", + "execution_count": null, + "id": "05fba2cf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -179,11 +328,11 @@ } ], "source": [ - "hp22_py_real = modes_py[(2, 2)].real()\n", - "hp22_c_real = modes_c[(2, 2)].real()\n", + "hp22_py_real_imr = imr_modes_py[(2, 2)].real()\n", + "hp22_c_real_imr = imr_modes_c[(2, 2)].real()\n", "plt.figure(figsize=(10, 4))\n", "plt.title(\n", - " rf\"\"\"INSPIRAL-ESIGMA\n", + " rf\"\"\"IMR-ESIGMA\n", " ==============================\n", " $m_1={m1} M_\\odot, m_2={m2} M_\\odot$, \n", " $\\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$, \n", @@ -193,11 +342,11 @@ "# hp.plot(label=r\"$h_+$\")\n", "# hc.plot(label=r\"$h_\\times$\")\n", "\n", - "hp22_py_real.start_time=0\n", - "hp22_c_real.start_time=0\n", + "hp22_py_real_imr.start_time=0\n", + "hp22_c_real_imr.start_time=0\n", "\n", - "plt.plot(hp22_py_real.sample_times,hp22_py_real,label='Python')\n", - "plt.plot(hp22_c_real.sample_times,hp22_c_real,label='C',ls='--')\n", + "plt.plot(hp22_py_real_imr.sample_times,hp22_py_real_imr,label='Python')\n", + "plt.plot(hp22_c_real_imr.sample_times,hp22_c_real_imr,label='C',ls='--')\n", "\n", "plt.xlabel(r\"$t (s)$\")\n", "plt.ylabel(r\"Re [$h_{22}$]\")\n", @@ -208,8 +357,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", + "execution_count": null, + "id": "b445d311", "metadata": {}, "outputs": [ { @@ -221,7 +370,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -231,12 +380,12 @@ } ], "source": [ - "print(modes_py.keys())\n", - "hp22 = modes_py[(2, 2)].real()\n", - "hc22 = modes_py[(2, 2)].imag()\n", + "print(imr_modes_py.keys())\n", + "hp22_imr = imr_modes_py[(2, 2)].real()\n", + "hc22_imr = imr_modes_py[(2, 2)].imag()\n", "plt.figure(figsize=(10, 4))\n", "plt.title(\n", - " rf\"\"\"INSPIRAL-ESIGMA\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", @@ -245,8 +394,8 @@ "# hp.plot(label=r\"$h_+$\")\n", "# hc.plot(label=r\"$h_\\times$\")\n", "\n", - "plt.plot(hp22.sample_times,hp22,label=r\"Re [$h_{22}$]\")\n", - "plt.plot(hc22.sample_times,hc22,label=r\"Im [$h_{22}$]\")\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", @@ -258,7 +407,7 @@ { "cell_type": "code", "execution_count": null, - "id": "173f050f", + "id": "bf734c70", "metadata": {}, "outputs": [], "source": [] @@ -268,7 +417,7 @@ "kernelspec": { "display_name": "esigma-dev", "language": "python", - "name": "python3" + "name": "esigma-dev" }, "language_info": { "codemirror_mode": { From 615ab40a36bc473488cafa274fbe42f2eea59174 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Mon, 4 May 2026 17:08:52 +0530 Subject: [PATCH 18/36] Fixed tolerance --- esigmapy/python_codes/esigma_pn_main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index 69d9c14..3a32362 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -405,7 +405,7 @@ def rhs(t, y): solver = ode(rhs).set_integrator( "dopri5", # RK4(5) Dormand-Prince ≈ gsl rkf45 rtol=ode_eps, - atol=0.0, + atol=1e-12, nsteps=10_000, # max_hnil=0, first_step=2 * LAL_PI / phi_dot0 / 100, From f4796b8736bb4fb0ea903a10662d687147e06197 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Mon, 4 May 2026 17:09:44 +0530 Subject: [PATCH 19/36] Fixed float comparison with eccentricity values and optimized performance for circular binaries --- esigmapy/python_codes/esigma_pn_inspiral.py | 236 ++++++++++---------- 1 file changed, 124 insertions(+), 112 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index 923efac..60279e7 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -237,18 +237,21 @@ def zed_n(e: float) -> float: 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) - e0 = 2.0 * eta * 96.0 / 15.0 - return (num / den) if e else e0 + 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 @@ -259,36 +262,37 @@ def x_dot_1pn(e: float, eta: float) -> float: t6 = e6 * (-11717.0 + 8288.0 * eta) den = 420.0 * ef**4 * np.sqrt(ef) num = -eta * (t0 + t2 + t4 + t6) - e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) - return (num / den) if e else e0 + 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 - M = m1 + m2 - if e: - 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 * ef**5 * M**4) - else: - 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) + + 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 * ef**5 * M**4) + def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: """Eq. (A28)""" pre = eta * x**6 * np.sqrt(x) - if e: + 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 @@ -298,12 +302,7 @@ def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: def x_dot_2pn(e: float, eta: float, x: float) -> float: """Eq. (A29)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e4 * e4 - - ef = 1.0 - e2 + eta2 = eta * eta # Small-e limit @@ -311,8 +310,15 @@ def x_dot_2pn(e: float, eta: float, x: float) -> float: 27322.0 * eta / 315.0 + 1888.0 * eta2 / 45.0) - if e: return e0_lim + 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 @@ -340,17 +346,24 @@ def x_dot_2pn_SS(e: float, eta: float, m1: float, m2: float, """2PN spin-spin term (Quentin Henry et al., arXiv:2308.13606)""" kappa1 = 1.0 kappa2 = 1.0 - 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**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) - if e: + 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) + @@ -360,23 +373,19 @@ def x_dot_2pn_SS(e: float, eta: float, m1: float, m2: float, 8*e2*(57+2692*kappa2) + 2*e4*(207+7472*kappa2)) * m2*m2 * S2z*S2z ) ) / (60.0 * ef_55 * M4) - else: - return pre * ( - ((1 + 80*kappa1)*m1*m1*S1z*S1z + - 158*m1*m2*S1z*S2z + - (1 + 80*kappa2)*m2*m2*S2z*S2z) - ) / (16.0 * M2) + ## --------- 2.5PN terms ------------ def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: """See Huerta et al.""" - ef = 1.0 - e*e - ef2 = ef * ef - e2 = e * e + pre = eta * x**7 * np.sqrt(x) - if e: + 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 @@ -390,18 +399,19 @@ def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: 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)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e6 * e2 - ef = 1.0 - e2 - ef_sqrt = np.sqrt(ef) + M2 = (m1 + m2)**2 M4 = M2 * M2 - M6 = M4 * M2 pre = 64.0 * eta / 5.0 - if e: + 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 * ( @@ -455,7 +465,7 @@ def x_dot_hereditary_3(e: float, eta: float, x: float) -> float: pi2 = np.pi * np.pi pre = eta * x**8 - if e: + 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)) @@ -471,13 +481,7 @@ def x_dot_hereditary_3(e: float, eta: float, x: float) -> float: def x_dot_3pn(e: float, eta: float, x: float) -> float: """3PN term (Huerta et al.)""" - e2 = e * e - e4 = e2 * e2 - e6 = e4 * e2 - e8 = e4 * e4 - e10 = e8 * e2 - - ef = 1.0 - e2 + eta2 = eta * eta pi2 = np.pi * np.pi @@ -493,6 +497,14 @@ def x_dot_3pn(e: float, eta: float, x: float) -> float: 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 @@ -577,18 +589,19 @@ def x_dot_3pn(e: float, eta: float, x: float) -> float: 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)""" - 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 + M2 = (m1 + m2)**2 M4 = M2 * M2 pre = 64.0 * eta / 5.0 - if e: + 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 * ( @@ -615,20 +628,9 @@ def x_dot_3pn_SS(e: float, eta: float, pre_factor = 64.0 * eta / 5.0 - 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 - M = m1 + m2 M2 = M * M M4 = M2 * M2 - M6 = M4 * M2 # --- Small-e fallback (better with tolerance) --- if abs(e) < 1e-12: @@ -646,7 +648,17 @@ def x_dot_3pn_SS(e: float, eta: float, (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) @@ -801,7 +813,7 @@ def x_dot_3_5pnSO(e: float, eta: float, m1: float, m2: float, / M**6 ) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + 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, @@ -817,7 +829,7 @@ def x_dot_3_5pn_SS(e: float, eta: float, m1: float, m2: float, (1 + 160*kappa2)*m2*m2*S2z*S2z) ) / (8.0 * M2) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + 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, @@ -837,7 +849,7 @@ def x_dot_3_5pn_cubicSpin(e: float, eta: float, m1: float, m2: float, / M**4 ) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + return val_e if abs(e) > 1e-12 else val_e0 def x_dot_3_5_pn(e: float, eta: float) -> float: @@ -846,7 +858,7 @@ def x_dot_3_5_pn(e: float, eta: float) -> float: (-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 e else val_e0 + return val_e if abs(e) > 1e-12 else val_e0 ## --------- 4PN and 4.5PN terms ------------ @@ -867,7 +879,7 @@ def x_dot_4pn(e: float, eta: float, x: float) -> float: (124741 - 582500*eta)*np.log(x)/8820.0 ) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + 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: @@ -882,7 +894,7 @@ def x_dot_4pnSO(e: float, eta: float, m1: float, m2: float, m1**3*m2*(75131*S1z + 119880*S2z)) / M**4 ) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + return val_e if abs(e) > 1e-12 else val_e0 def x_dot_4pnSS(e: float, eta: float, m1: float, m2: float, @@ -902,7 +914,7 @@ def x_dot_4pnSS(e: float, eta: float, m1: float, m2: float, / (72576.0 * M**6) ) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + 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)""" @@ -916,7 +928,7 @@ def x_dot_4_5_pn(e: float, eta: float, x: float) -> float: 3424*np.pi*np.log(x)/105.0 ) val_e = val_e0 #placeholder for e-dependent expression - return val_e if e else val_e0 + return val_e if abs(e) > 1e-12 else val_e0 # Self-Force (SF) higher-order terms in addition to x_dot @@ -1001,7 +1013,7 @@ def e_dot_0pn(e: float, eta: float) -> float: """Eq. (A31)""" e2 = e * e ef = 1.0 - e2 - if not e: + if abs(e) < 1e-12: return 0.0 num = -e * eta * (121.0*e2 + 304.0) den = 15.0 * ef**2 * np.sqrt(ef) @@ -1011,7 +1023,7 @@ def e_dot_0pn(e: float, eta: float) -> float: def e_dot_1pn(e: float, eta: float) -> float: """Eq. (A32)""" - if not e: + if abs(e) < 1e-12: return 0.0 e2 = e * e e4 = e2 * e2 @@ -1027,7 +1039,7 @@ def e_dot_1pn(e: float, eta: float) -> float: 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 not e: + if abs(e) < 1e-12: return 0.0 e2 = e * e e4 = e2 * e2 @@ -1041,7 +1053,7 @@ def e_dot_1_5pn_SO(e: float, m1: float, m2: float, ) def e_rad_hereditary_1_5(e: float, eta: float, x: float) -> float: - if not e: + if abs(e) < 1e-12: return 0.0 pre = 32.0 * eta * e * x**7 / 5.0 return pre * (-985.0 * np.pi * phi_e_rad(e) / 48.0) @@ -1049,7 +1061,9 @@ def e_rad_hereditary_1_5(e: float, eta: float, x: float) -> float: ## ---------- 2PN ------------- def e_dot_2pn(e: float, eta: float) -> float: - e_2_pn = 0.0 + + if abs(e) < 1e-12: + return 0.0 eta_pow_2 = eta * eta e_pow_2 = e * e @@ -1087,25 +1101,23 @@ def e_dot_2pn(e: float, eta: float) -> float: e_4_rt = e_pow_4 * (565.0 / 6.0 - 113.0 * eta / 3.0) - if e != 0.0: - 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) - ) - else: - e_2_pn = 0.0 - + + 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 not e: + if abs(e) < 1e-12: return 0.0 kappa1 = 1.0; kappa2 = 1.0 e2 = e * e @@ -1126,7 +1138,7 @@ def e_dot_2pn_SS(e: float, m1: float, m2: float, ## ---------- 2.5PN ------------- def e_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: - if e == 0.0: + if abs(e) < 1e-12: return 0.0 e_2 = e * e @@ -1209,7 +1221,7 @@ def e_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float) -> fl return result def e_rad_hereditary_2_5(e: float, eta: float, x: float) -> float: - if not e: + if abs(e) < 1e-12: return 0.0 pre = 32.0 * eta * e * x**4 * x**4 * 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 @@ -1218,7 +1230,7 @@ def e_rad_hereditary_2_5(e: float, eta: float, x: float) -> float: ## --------- 3PN ------------- def e_rad_hereditary_3(e: float, eta: float, x: float) -> float: - if not e: + if abs(e) < 1e-12: return 0.0 pre = 32.0 * eta * e * x**7 / 5.0 x32 = x * np.sqrt(x) @@ -1227,7 +1239,7 @@ def e_rad_hereditary_3(e: float, eta: float, x: float) -> float: return pre * (a3 + a4) def e_dot_3pn(e: float, eta: float, x: float) -> float: - if e == 0.0: + if abs(e) < 1e-12: return 0.0 eta_pow_2 = eta * eta @@ -1331,7 +1343,7 @@ def e_dot_3pn(e: float, eta: float, x: float) -> float: 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 not e: + if abs(e) < 1e-12: return 0.0 e2 = e * e e4 = e2 * e2 @@ -1353,7 +1365,7 @@ def e_dot_3pn_SO(e: float, m1: float, m2: float, import math def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: - if e == 0.0: + if abs(e) < 1e-12: return 0.0 kappa1 = 1.0 @@ -1629,7 +1641,7 @@ def phi_dot_1pn(e: float, eta: float, u: float) -> float: 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 not e: + 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) @@ -1665,7 +1677,7 @@ def phi_dot_2pn(e: float, eta: float, u: float) -> float: 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 not e: + if abs(e) < 1e-12: return 0.0 kappa1 = 1.0; kappa2 = 1.0 return ( @@ -2822,7 +2834,7 @@ def eccentric_x_model_odes(t, y, params): dydt = [0.0, 0.0, 0.0, 0.0] - if e != 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) From 8ff938a91a0ceb4d2b1f085e0adef0b303bd16d5 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Mon, 4 May 2026 17:29:25 +0530 Subject: [PATCH 20/36] fix link issues --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f3e26b8..1a1d675 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,8 @@ | Model | Description | |---|---| -| `ESIGMAHM` | Eccentric, aligned-spin IMR waveform with higher modes (arXiv:[2409.13866](https://arxiv.org/abs/2409.13866)) | -| `ESIGMASur` | Surrogate model of the (2,2) GW-mode of `ESIGMAHM` (arXiv:[2510.00116](https://arxiv.org/abs/2510.00116))| +| `ESIGMAHM` | Eccentric, aligned-spin IMR waveform with higher modes (arXiv:[2409.13866](https://arxiv.org/abs/2409.13866 )) | +| `ESIGMASur` | Surrogate model of the (2,2) GW-mode of `ESIGMAHM` (arXiv:[2510.00116](https://arxiv.org/abs/2510.00116 ))| --- From a91a4bc0dcd94e6db4f18dabf31bea5e9c93b4a8 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:07:35 +0530 Subject: [PATCH 21/36] Add polygamma for complex numbers, add self-force (SF) terms, cleanup the utility functions --- esigmapy/python_codes/esigma_pn_inspiral.py | 123 ++++++++++++++++++++ 1 file changed, 123 insertions(+) diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index 60279e7..0ba8ae9 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -34,6 +34,53 @@ LOG5 = 1.60943791243410037460075933323 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): + from scipy.special import digamma # Look up the polygamma definition in LALSimESIGMA.c, line number 490 + import mpmath as mp + """ + 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: + return digamma(z) + + # 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: @@ -458,6 +505,24 @@ def x_dot_2_5pn_SO(e: float, eta: float, m1: float, m2: float, 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: @@ -860,6 +925,21 @@ def x_dot_3_5_pn(e: float, eta: float) -> float: 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: @@ -896,6 +976,46 @@ def x_dot_4pnSO(e: float, eta: float, m1: float, m2: float, 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 + + np.sqrt(1 - S1z2) + + 13 * S1z2 * np.sqrt(1 - S1z2) + + 6 * S1z4 * np.sqrt(1 - S1z2) + + 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: @@ -2572,6 +2692,7 @@ def dx_dt(radiation_pn_order: int, if radiation_pn_order >= 5: inst += x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z) * x2 * np.sqrt(x) + inst += x_dot_2_5pn_SF(e, eta, S1z) * x2 * np.sqrt(x) if radiation_pn_order >= 6: inst += x_dot_3pn(e, eta, x) * x3 @@ -2583,6 +2704,7 @@ def dx_dt(radiation_pn_order: int, inst += x_dot_3_5_pn(e, eta) * x3 * np.sqrt(x) inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) + inst += x_dot_3_5pn_SF(e, eta, S1z) * x3 * np.sqrt(x) if radiation_pn_order >= 8: inst += ( @@ -2590,6 +2712,7 @@ def dx_dt(radiation_pn_order: int, + 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) * np.sqrt(x) From a3da812c1d3eaf40956e7a2b7dbf793a8f329442 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:08:48 +0530 Subject: [PATCH 22/36] Bug fix --- esigmapy/python_codes/esigma_pn_inspiral.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index 0ba8ae9..1455e95 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -1175,7 +1175,7 @@ def e_dot_1_5pn_SO(e: float, m1: float, m2: float, 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**7 / 5.0 + 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 ------------- @@ -1343,7 +1343,7 @@ def e_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float) -> fl 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**4 * np.sqrt(x) / 5.0 + 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 From 426f152923f58f6ac00ac47ac8f4e50363cad2a6 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:09:25 +0530 Subject: [PATCH 23/36] minor changes in the code structure to better match C version --- esigmapy/python_codes/esigma_pn_inspiral.py | 34 ++++++--------------- 1 file changed, 9 insertions(+), 25 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index 1455e95..57b4099 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -26,6 +26,7 @@ import numpy as np + # ------ Constants -------------------------------------------------- EULER_GAMMA = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant LOG2 = 0.693147180559945309417232121458 @@ -325,6 +326,8 @@ def x_dot_1_5_pn(e: float, eta: float, m1: float, m2: float, e4 = e2 * e2 e6 = e4 * e2 ef = 1.0 - e2 + ef5 = ef*ef*ef*ef*ef + M4 = M*M*M*M return -( m1 * m2 * ( @@ -332,13 +335,13 @@ def x_dot_1_5_pn(e: float, eta: float, m1: float, m2: float, (5424 + 27608*e2 + 16694*e4 + 585*e6) * m2*m2 * S2z + 3 * (1200 + 6976*e2 + 4886*e4 + 207*e6) * m1*m2 * (S1z + S2z) ) - ) / (45.0 * ef**5 * M**4) + ) / (45.0 * ef5 * M4) def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: """Eq. (A28)""" - pre = eta * x**6 * np.sqrt(x) + 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: @@ -542,7 +545,6 @@ def x_dot_hereditary_3(e: float, eta: float, x: float) -> float: ) / 18375.0 return pre * x_3_term_e0 - def x_dot_3pn(e: float, eta: float, x: float) -> float: """3PN term (Huerta et al.)""" @@ -650,7 +652,6 @@ def x_dot_3pn(e: float, eta: float, x: float) -> float: 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)""" @@ -880,7 +881,6 @@ def x_dot_3_5pnSO(e: float, eta: float, m1: float, m2: float, 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""" @@ -896,7 +896,6 @@ def x_dot_3_5pn_SS(e: float, eta: float, m1: float, m2: float, 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""" @@ -916,7 +915,6 @@ def x_dot_3_5pn_cubicSpin(e: float, eta: float, m1: float, m2: float, 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 * @@ -1116,7 +1114,6 @@ def dxdt_4pn(x: float, eta: float) -> float: 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. @@ -1164,13 +1161,13 @@ def e_dot_1_5pn_SO(e: float, m1: float, m2: float, e2 = e * e e4 = e2 * e2 ef = 1.0 - e2 - M = m1 + m2 - pre = e / (ef**4) * (m1*m2) / (90.0 * M**4) + 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) - ) + 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: @@ -2651,19 +2648,6 @@ def separation( rel_sep_3pn_SS(e, u, m1, m2, S1z, S2z) * x * x) - -# ============ 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) - - # ================== ODE right-hand-side dispatchers ========================== def dx_dt(radiation_pn_order: int, From ef6e277ec7f472608eeee1e40fc720f1340a9a38 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:12:06 +0530 Subject: [PATCH 24/36] Choose the integration method solve_ivp with method='LSODA', add flag to identify nan/inf --- esigmapy/python_codes/esigma_pn_main.py | 118 ++++++++++-------------- 1 file changed, 50 insertions(+), 68 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index 3a32362..eb2f977 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -1,5 +1,5 @@ import numpy as np -from scipy.integrate import ode +from scipy.integrate import solve_ivp from scipy.interpolate import CubicSpline import math from .esigma_pn_inspiral import * @@ -389,85 +389,67 @@ def inspiral_esigma_dynamics( # ------------------------------------------------------------------ # # Initial conditions y = [x, e, l (mean anomaly), phi] # ------------------------------------------------------------------ # - u0 = pn_kepler_equation(eta, x_init, e_init, mean_anom_init) - r0 = separation(u0, eta, x_init, e_init, mass1, mass2, S1z, S2z) - y0_dot = eccentric_x_model_odes(0.0, [x_init, e_init, mean_anom_init, 0.0], params) - phi_dot0 = y0_dot[3] - + y0 = np.array([x_init, e_init, mean_anom_init, 0.0]) - # ------------------------------------------------------------------ # - # ODE integration (RK45 via scipy) - # ------------------------------------------------------------------ # + # 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 + #----------------------------------- + import time as tt + start_time = tt.time() + 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, + ) + print(f'time taken = {tt.time()-start_time} secs', ) + t_arr = sol.t + y_arr = sol.y.T # shape (N, 4) - solver = ode(rhs).set_integrator( - "dopri5", # RK4(5) Dormand-Prince ≈ gsl rkf45 - rtol=ode_eps, - atol=1e-12, - nsteps=10_000, - # max_hnil=0, - first_step=2 * LAL_PI / phi_dot0 / 100, - ) - solver.set_initial_value(y0, 0.0) - - MAX_SAMPLES = 2048 * 16384 - - t_list = [0.0] - x_list = [x_init] - e_list = [e_init] - l_list = [mean_anom_init] - phi_list = [0.0] - phi_dot_list = [phi_dot0] - r_list = [r0] - u_list = [u0] - + # NaN / Inf guard (equivalent to your check) bad_number = False + if not np.all(np.isfinite(y_arr)): + bad_number = True - while solver.successful(): - solver.integrate(solver.t + dt) + # Unpack variables + x_arr = y_arr[:, 0] + e_arr = y_arr[:, 1] + l_arr = y_arr[:, 2] + phi_arr = y_arr[:, 3] - y = solver.y + u_arr = np.empty_like(x_arr) + r_arr = np.empty_like(x_arr) - # NaN / Inf guard - if not np.all(np.isfinite(y)): - bad_number = True - break + 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 - t_list.append(solver.t) - x_list.append(y[0]) - e_list.append(y[1]) - l_list.append(y[2]) - phi_list.append(y[3]) - - yd = eccentric_x_model_odes(solver.t, y, params) - phi_dot_list.append(yd[3]) - - ui = pn_kepler_equation(eta, y[0], y[1], y[2]) - ri = separation(ui, eta, y[0], y[1], mass1, mass2, S1z, S2z) - u_list.append(ui) - r_list.append(ri) - - if len(t_list) >= MAX_SAMPLES: - break - - if y[0] >= x_final: - break - - # Convert to arrays - t_arr = np.array(t_list) - x_arr = np.array(x_list) - e_arr = np.array(e_list) - l_arr = np.array(l_list) - phi_arr = np.array(phi_list) - phi_dot_arr = np.array(phi_dot_list) - r_arr = np.array(r_list) - - final_i = len(t_arr) if not bad_number else len(t_arr) # same either way here + 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 # ------------------------------------------------------------------ # From 1ff252d31ffc26db5fb181cd2bb0717b65ec35b6 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:15:25 +0530 Subject: [PATCH 25/36] Cleanup --- esigmapy/python_codes/esigma_pn_main.py | 76 +------------------------ 1 file changed, 3 insertions(+), 73 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index eb2f977..367ff48 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -7,8 +7,8 @@ import lal # Constants (LAL equivalents) -LAL_PI = math.pi -LAL_MTSUN_SI = 4.925491025543576e-6 # Solar mass in seconds +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 @@ -33,74 +33,6 @@ class Params: m2: float S1z: float S2z: float -# # ------------------------------------------------------------------ # -# # Helper: populate powers of orbital variables -# # (mirrors PopulateKeplerParams + kepler_vars struct) -# # ------------------------------------------------------------------ # -# @dataclass -# class KeplerVars: -# eta: float = 0.0 -# eta2: float = 0.0 -# eta3: float = 0.0 -# eta4: float = 0.0 -# eta5: float = 0.0 -# eta6: float = 0.0 - -# Mtot: float = 0.0 -# Mtot2: float = 0.0 -# Mtot3: float = 0.0 -# Mtot4: float = 0.0 -# Mtot5: float = 0.0 -# Mtot6: float = 0.0 - -# S1z: float = 0.0 -# S1z2: float = 0.0 -# S1z3: float = 0.0 -# S1z4: float = 0.0 -# S1z5: float = 0.0 -# S1z6: float = 0.0 -# S1z7: float = 0.0 -# S1z8: float = 0.0 -# S1z9: float = 0.0 -# S1z10: float = 0.0 -# S1z11: float = 0.0 -# S1z12: float = 0.0 -# S1z13: float = 0.0 -# S1z14: float = 0.0 -# S1z15: float = 0.0 -# S1z16: float = 0.0 - -# S2z: float = 0.0 -# S2z2: float = 0.0 -# S2z3: float = 0.0 -# S2z4: float = 0.0 -# S2z5: float = 0.0 -# S2z6: float = 0.0 - - -# def _build_kepler_vars(eta: float, total_mass: float, -# S1z: float, S2z: float) -> KeplerVars: -# """Pre-compute and cache all integer powers used by the mode functions.""" -# kv = KeplerVars() -# kv.eta = eta -# kv.eta2 = eta**2; kv.eta3 = eta**3; kv.eta4 = eta**4 -# kv.eta5 = eta**5; kv.eta6 = eta**6 - -# kv.Mtot = total_mass -# kv.Mtot2 = total_mass**2; kv.Mtot3 = total_mass**3 -# kv.Mtot4 = total_mass**4; kv.Mtot5 = total_mass**5 -# kv.Mtot6 = total_mass**6 - -# kv.S1z = S1z -# for p in range(2, 17): -# setattr(kv, f"S1z{p}", S1z**p) - -# kv.S2z = S2z -# for p in range(2, 7): -# setattr(kv, f"S2z{p}", S2z**p) - -# return kv - # ------------------------------------------------------------------ # # 1. compute_mode_from_dynamics @@ -236,7 +168,6 @@ def inspiral_esigma_mode_from_dynamics( scaling inside compute_mode_from_dynamics (matches the C end result). """ mode_pn_order = int(os.environ.get("ModePNOrder", ModePNOrderDefault)) - print('Mode PN order =',mode_pn_order) return compute_mode_from_dynamics( l, m, @@ -363,7 +294,6 @@ def inspiral_esigma_dynamics( # PN / radiation order (mirror the C env-var logic; default hardcoded) import os rad_pn_order = int(os.environ.get("RadiationPNOrder", RadiationPNOrderDefault)) - print('Radiation PN order =',rad_pn_order) params = Params( eta=eta, radiation_pn_order=rad_pn_order, @@ -471,7 +401,7 @@ def interp_deriv_uniform(t_raw, y_raw): uniform_x = interp_uniform(t_arr, x_arr) uniform_phi = interp_uniform(t_arr, phi_arr) - uniform_phi_dot = interp_uniform(t_arr, phi_dot_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) From 761c8f41ff84ae6198e2a7f209c047964e43ce96 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:19:23 +0530 Subject: [PATCH 26/36] Update notebook --- notebooks/esigma_python.ipynb | 885 +++++++++++++++++++++++++++++++--- 1 file changed, 820 insertions(+), 65 deletions(-) diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb index 8bc7ea6..ba4a651 100644 --- a/notebooks/esigma_python.ipynb +++ b/notebooks/esigma_python.ipynb @@ -2,9 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -14,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "b962168c", "metadata": {}, "outputs": [ @@ -22,20 +24,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "Poof!\n", + "4\n", + "3\n", ":/home/samanwaya/nrsur7dq4\n" ] } ], "source": [ "import os\n", + "\n", + "os.environ[\"RadiationPNOrder\"] = '3'\n", + "os.environ[\"ModePNOrder\"] = '4'\n", "x=os.environ.get(\"ModePNOrder\")\n", + "y=os.environ.get(\"RadiationPNOrder\")\n", "print('Poof!' if x is None else int(x))\n", + "print('Poof!' if y is None else 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": 28, + "id": "4ec5f8c9", + "metadata": {}, + "outputs": [], + "source": [ + "from esigmapy.python_codes import generator_python as gp\n", + "\n", + "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", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)" + ] + }, { "cell_type": "markdown", "id": "ff51d368", @@ -46,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "id": "a666270a-c644-473b-8559-57df6e16217c", "metadata": {}, "outputs": [ @@ -55,33 +95,32 @@ "output_type": "stream", "text": [ "================ Python ================\n", - "Radiation PN order = 8\n", - "Orbital evolution took: 3.262218417999975 seconds\n", - "Mode PN order = 8\n", - "Mode PN order = 8\n", - "Modes generation took: 0.3844245680011227 seconds\n", + "time taken = 0.8608009815216064 secs\n", + "3113 False\n", + "Orbital evolution took: 1.09792704499705 seconds\n", + "Modes generation took: 0.4364413240036811 seconds\n", "================= C =================\n", - "Orbital evolution took: 0.05307760899813729 seconds\n", - "Modes generation took: 0.011499872998683713 seconds\n" + "Orbital evolution took: 0.01612507500249194 seconds\n", + "Modes generation took: 0.04842561600526096 seconds\n" ] } ], "source": [ - "from esigmapy.python_codes import generator_python as gp\n", + "# from esigmapy.python_codes import generator_python as gp\n", "\n", - "m1 = 20.0 # masses (in solar masses)\n", - "m2 = 30.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = -0.3\n", - "eccentricity = 0.15 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", + "# m1 = 20.0 # masses (in solar masses)\n", + "# m2 = 30.0\n", + "# spin1z = 0.5 # dimensionless spins\n", + "# spin2z = -0.3\n", + "# eccentricity = 0.15 # starting eccentricity\n", + "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", "\n", - "distance = 400.0 # source luminosity distance (in Mpc)\n", - "inclination = 30 * np.pi / 180.0 # orbital inclination with line-of-sight\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", + "# f_low = 20.0 # starting frequency (in Hz)\n", + "# delta_t = 1 / 2**10 # time grid-spacing (in s)\n", "\n", "print('================ Python ================')\n", "inspiral_modes_py = gp.get_inspiral_esigma_modes_py(\n", @@ -118,13 +157,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 36, "id": "9fc2cdd6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -158,14 +197,95 @@ "plt.ylabel(r\"Re [$h_{22}$]\")\n", "plt.legend()\n", "plt.grid()\n", - "# plt.xlim(0.2,0.3)\n", + "# plt.xlim(0.7,0.74)\n", "# plt.ylim(-1e-21,1e-21)\n", - "plt.tight_layout()" + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# plt.figure(figsize=(10, 4))\n", + "# plt.plot(hp22_py_real_inspiral.sample_times,hp22_py_real_inspiral,label='Python')\n", + "# plt.plot(hp22_c_real_inspiral.sample_times,hp22_c_real_inspiral,label='C',ls='--')\n", + "\n", + "# plt.xlabel(r\"$t (s)$\")\n", + "# plt.ylabel(r\"Re [$h_{22}$]\")\n", + "# plt.legend()\n", + "# plt.grid()\n", + "# # plt.xlim(left=1.2, right=1.245)\n", + "# # plt.ylim(-1e-21,1e-21)\n", + "# plt.tight_layout()\n", + "# plt.show()\n", + "\n", + "# plt.figure(figsize=(10, 4))\n", + "# plt.semilogy(hp22_py_real_inspiral.sample_times,np.abs(hp22_py_real_inspiral-hp22_c_real_inspiral)/np.abs(hp22_c_real_inspiral),label='Python')\n", + "\n", + "# plt.xlabel(r\"$t (s)$\")\n", + "# plt.ylabel(r\"Re [$h_{22}$]\")\n", + "# plt.legend()\n", + "# plt.grid()\n", + "# # plt.xlim(8.05,9.25)\n", + "# # plt.ylim(-1e-21,1e-21)\n", + "# plt.tight_layout()\n", + "# plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, + "id": "67b21752-4eac-4fc7-805e-032fd58be48b", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "lengths do not match (4652 vs 4653)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 2\u001b[39m hp22_c_real_inspiral = inspiral_modes_c[(\u001b[32m2\u001b[39m, \u001b[32m2\u001b[39m)].real()\n\u001b[32m 3\u001b[39m hp22_py_real_inspiral.start_time=\u001b[32m0\u001b[39m\n\u001b[32m 4\u001b[39m hp22_c_real_inspiral.start_time=\u001b[32m0\u001b[39m\n\u001b[32m 5\u001b[39m plt.figure(figsize=(\u001b[32m10\u001b[39m, \u001b[32m4\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m plt.semilogy(hp22_py_real_inspiral.sample_times,np.abs(hp22_py_real_inspiral-hp22_c_real_inspiral),label=\u001b[33m'Python'\u001b[39m)\n\u001b[32m 7\u001b[39m \n\u001b[32m 8\u001b[39m plt.xlabel(\u001b[33mr\"$t (s)$\"\u001b[39m)\n\u001b[32m 9\u001b[39m plt.ylabel(\u001b[33mr\"Re [$h_{22}$]\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:257\u001b[39m, in \u001b[36mArray._returntype..returntype\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mreturntype\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m ary = \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;66;03m# pylint:disable=not-callable\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ary \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:68\u001b[39m, in \u001b[36m_convert..convert\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 65\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mconvert\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m 67\u001b[39m _convert_to_scheme(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:279\u001b[39m, in \u001b[36mArray._checkother..checkother\u001b[39m\u001b[34m(self, *args)\u001b[39m\n\u001b[32m 277\u001b[39m nargs +=(other,)\n\u001b[32m 278\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(other) \u001b[38;5;129;01mis\u001b[39;00m Array:\n\u001b[32m--> \u001b[39m\u001b[32m279\u001b[39m \u001b[30;43mcheck_same_len_precision\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mother\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 280\u001b[39m _convert_to_scheme(other)\n\u001b[32m 281\u001b[39m nargs += (other._data,)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:135\u001b[39m, in \u001b[36mcheck_same_len_precision\u001b[39m\u001b[34m(a, b)\u001b[39m\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(a) != \u001b[38;5;28mlen\u001b[39m(b):\n\u001b[32m 133\u001b[39m msg = \u001b[33m'\u001b[39m\u001b[33mlengths do not match (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m'\u001b[39m.format(\n\u001b[32m 134\u001b[39m \u001b[38;5;28mlen\u001b[39m(a), \u001b[38;5;28mlen\u001b[39m(b))\n\u001b[32m--> \u001b[39m\u001b[32m135\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m a.precision != b.precision:\n\u001b[32m 137\u001b[39m msg = \u001b[33m'\u001b[39m\u001b[33mprecisions do not match (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m'\u001b[39m.format(\n\u001b[32m 138\u001b[39m a.precision, b.precision)\n", + "\u001b[31mValueError\u001b[39m: lengths do not match (4652 vs 4653)" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real_inspiral = inspiral_modes_py[(2, 2)].real()\n", + "hp22_c_real_inspiral = inspiral_modes_c[(2, 2)].real()\n", + "hp22_py_real_inspiral.start_time=0\n", + "hp22_c_real_inspiral.start_time=0\n", + "plt.figure(figsize=(10, 4))\n", + "plt.semilogy(hp22_py_real_inspiral.sample_times,np.abs(hp22_py_real_inspiral-hp22_c_real_inspiral),label='Python')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "# plt.xlim(1.05,1.25)\n", + "# plt.ylim(-1e-21,1e-21)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", "metadata": {}, "outputs": [ @@ -178,7 +298,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -222,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "311c00be", "metadata": {}, "outputs": [ @@ -231,31 +351,38 @@ "output_type": "stream", "text": [ "================ Python ================\n", - "Radiation PN order = 8\n", - "Orbital evolution took: 8.60927068700039 seconds\n", - "Mode PN order = 8\n", - "Mode PN order = 8\n", - "Modes generation took: 1.4204796830017585 seconds\n", - "Generating MR waveform from 49.62645847549604Hz...\n", + "time taken = 0.8143424987792969 secs\n", + "3113 False\n", + "Orbital evolution took: 1.0490614450027351 seconds\n", + "Modes generation took: 0.4306351409977651 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.038731628947603076. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 27.66742430543558Hz...\n", "Hybridizing the following modes: [(2, 2), (2, -2)]\n", "By aligning (2, 2) mode\n", "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", - "INSPIRAL mode (2, 2) goes from 15.51002742012073Hz to 133.6547676158098Hz\n", - "MERGER mode (2, 2) goes from 49.67733142367634Hz to 351.6339524069211Hz\n", - "INSPIRAL mode (2, -2) goes from -133.6547676158098Hz to -15.51002742012073Hz\n", - "MERGER mode (2, -2) goes from -352.64823272619185Hz to -49.67650156836734Hz\n", + "INSPIRAL mode (2, 2) goes from -234.69690036721462Hz to 161.82422506567798Hz\n", + "MERGER mode (2, 2) goes from -799.1291300472742Hz to 1128.8976373558457Hz\n", + "INSPIRAL mode (2, -2) goes from -161.82422506567798Hz to 234.69690036721462Hz\n", + "MERGER mode (2, -2) goes from -1049.0987227068747Hz to 781.0501686609422Hz\n", "blended.\n", "================= C =================\n", - "Orbital evolution took: 0.012976661000720924 seconds\n", - "Modes generation took: 0.045316491999983555 seconds\n", - "Generating MR waveform from 49.79651161822471Hz...\n", + "Orbital evolution took: 0.015542758999799844 seconds\n", + "Modes generation took: 0.04799114799970994 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.03873155501634525. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 27.667424305474597Hz...\n", "Hybridizing the following modes: [(2, 2), (2, -2)]\n", "By aligning (2, 2) mode\n", "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", - "INSPIRAL mode (2, 2) goes from 15.523337342773294Hz to 126.35235015121037Hz\n", - "MERGER mode (2, 2) goes from 49.817281724494144Hz to 352.4487639118021Hz\n", - "INSPIRAL mode (2, -2) goes from -126.35235015121037Hz to -15.523337342773294Hz\n", - "MERGER mode (2, -2) goes from -353.44200854926675Hz to -49.81639786367156Hz\n", + "INSPIRAL mode (2, 2) goes from 9.234580422265868Hz to 163.44646629577284Hz\n", + "MERGER mode (2, 2) goes from -799.1291322431845Hz to 1128.8976566699175Hz\n", + "INSPIRAL mode (2, -2) goes from -163.44646629577284Hz to -9.234580422265868Hz\n", + "MERGER mode (2, -2) goes from -1049.0987416676346Hz to 781.0501706322736Hz\n", "blended.\n" ] } @@ -263,19 +390,19 @@ "source": [ "from esigmapy.python_codes import generator_python as gp\n", "\n", - "m1 = 20.0 # masses (in solar masses)\n", - "m2 = 30.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = -0.3\n", - "eccentricity = 0.15 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", + "# m1 = 20.0 # masses (in solar masses)\n", + "# m2 = 30.0\n", + "# spin1z = 0.5 # dimensionless spins\n", + "# spin2z = -0.3\n", + "# eccentricity = 0.15 # starting eccentricity\n", + "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", "\n", - "distance = 400.0 # source luminosity distance (in Mpc)\n", - "inclination = 30 * np.pi / 180.0 # orbital inclination with line-of-sight\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**12 # time grid-spacing (in s)\n", + "# f_low = 20.0 # starting frequency (in Hz)\n", + "# delta_t = 1 / 2**12 # time grid-spacing (in s)\n", "\n", "print('================ Python ================')\n", "imr_modes_py = gp.get_imr_esigma_modes_py(\n", @@ -312,13 +439,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "05fba2cf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -357,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "b445d311", "metadata": {}, "outputs": [ @@ -370,7 +497,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -404,10 +531,638 @@ "plt.tight_layout()" ] }, + { + "cell_type": "markdown", + "id": "bf734c70", + "metadata": {}, + "source": [ + "## Checking evolution of dynamical quantities" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a375d82e-ff58-4b81-bb9c-e7c6feba6a28", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/samanwaya/lalsuite-esigma-install/lib/python3.11/site-packages/lalsimulation/_lalsimulation_swig.py:8: 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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No version information file '.version' found\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import lalsimulation as lalsim\n", + "from esigmapy.python_codes import esigma_pn_main as espn\n", + "import lal" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4e7c8da2-ed95-495a-a859-753676dbb471", + "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": "61abe72a-af7b-4343-8b9a-24ad6112456c", + "metadata": {}, + "source": [ + "### LALSuite (C) and python" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b4e8a2c7-07ee-4e5a-91bc-4ed30c142804", + "metadata": {}, + "outputs": [], + "source": [ + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.5\n", + "eccentricity = 0.1 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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 / 4096. # time grid-spacing (in s)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ca70e4cd-94b4-4ef9-bf3c-6054f4e724c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken = 2.5000295639038086 secs\n", + "4783 False\n", + "dict_keys(['time_evol', 'x_evol', 'eccentricity_evol', 'mean_ano_evol', 'phi_evol', 'phi_dot_evol', 'r_evol', 'r_dot_evol'])\n" + ] + } + ], + "source": [ + "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": 9, + "id": "73c89a53-73d7-4b80-a8df-52a9be1e6443", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(time_py,e_py,label='python')\n", + "plt.plot(time_c,e_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$e (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()\n", + "\n", + "plt.plot(time_py,x_py,label='python')\n", + "plt.plot(time_c,x_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$x (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()\n", + "\n", + "plt.plot(time_py,meanan_py,label='python')\n", + "plt.plot(time_c,meanan_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$l (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()\n", + "\n", + "plt.plot(time_py,phi_py,label='python')\n", + "plt.plot(time_c,phi_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$\\\\phi (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()\n", + "\n", + "plt.plot(time_py,phidot_py,label='python')\n", + "plt.plot(time_c,phidot_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$\\\\dot{\\\\phi} (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()\n", + "\n", + "plt.plot(time_py,r_py,label='python')\n", + "plt.plot(time_c,r_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$r (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()\n", + "\n", + "plt.plot(time_py,rdot_py,label='python')\n", + "plt.plot(time_c,rdot_c,ls='--',label='C')\n", + "plt.xlabel('$t (M)$',fontsize=13)\n", + "plt.ylabel('$\\\\dot{r} (t)$',fontsize=13)\n", + "plt.legend(), plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "004f84c0-2685-4117-9f84-199a29574321", + "metadata": {}, + "source": [ + "## Check with SEOBNRv5EHM" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c0dcc211-7ab8-489d-a0a6-9e2945e2267a", + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No version information file '.version' found\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import esigmapy\n", + "from pyseobnr.generate_waveform import GenerateWaveform, generate_modes_opt\n", + "import lal\n", + "from esigmapy.python_codes import generator_python as gp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "34165747-ac2d-4245-9f6c-c7b1a78d4f0a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0007736943088216417\n" + ] + } + ], + "source": [ + "m1 = 20.0 # masses (in solar masses)\n", + "m2 = 30.0\n", + "spin1z = 0.5 # dimensionless spins\n", + "spin2z = -0.3\n", + "eccentricity = 0.05 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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 = 50.0 # starting frequency (in Hz)\n", + "delta_t = 1 / 2048. # time grid-spacing (in s)\n", + "\n", + "# Input parameters for SEOB\n", + "q = m2/m1\n", + "chi_1, chi_2 = spin1z, spin2z\n", + "omega_start = np.pi*f_low*lal.MTSUN_SI #0.0137 # This is the orbital frequency in geometric units with M=1\n", + "# eccentricity = 0.4\n", + "rel_anomaly = mean_anomaly #2.3\n", + "print(omega_start)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "71035b84-3c81-4de0-890e-6fd94772e528", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================ Python ================\n", + "Orbital evolution took: 0.6134227930015186 seconds\n", + "Modes generation took: 0.05245385800662916 seconds\n", + "Generating MR waveform from 50.11548092913955Hz...\n", + "Hybridizing the following modes: [(2, 2), (2, -2)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", + "INSPIRAL mode (2, 2) goes from 47.79145657063043Hz to 122.77938006168975Hz\n", + "MERGER mode (2, 2) goes from 50.149759320137925Hz to 349.405367681844Hz\n", + "INSPIRAL mode (2, -2) goes from -122.77938006168975Hz to -47.79145657063043Hz\n", + "MERGER mode (2, -2) goes from -350.04376353886676Hz to -50.14885885724892Hz\n", + "blended.\n", + "================= C =================\n", + "Orbital evolution took: 0.003511044997139834 seconds\n", + "Modes generation took: 0.0018358320085098967 seconds\n", + "Generating MR waveform from 49.83655249209011Hz...\n", + "Hybridizing the following modes: [(2, 2), (2, -2)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", + "INSPIRAL mode (2, 2) goes from 47.92635547090774Hz to 119.87538703628631Hz\n", + "MERGER mode (2, 2) goes from 49.87962380222865Hz to 349.00018011199046Hz\n", + "INSPIRAL mode (2, -2) goes from -119.87538703628631Hz to -47.92635547090788Hz\n", + "MERGER mode (2, -2) goes from -349.6894142266106Hz to -49.878717424127885Hz\n", + "blended.\n" + ] + } + ], + "source": [ + "print('================ Python ================')\n", + "imr_modes_py = gp.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", + " 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", + " verbose=True\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "bf734c70", + "id": "d46a691b-65d2-4d07-9128-bb5a4a563b54", + "metadata": {}, + "outputs": [], + "source": [ + "settings = dict(\n", + " # \"EccIC\" determines the prescription for the starting frequency.\n", + " # EccIC=0 means that omega_start corresponds to the *instantaneous* angular frequency, and\n", + " # EccIC=1 means that omega_start corresponds to the *orbit-averaged* angular frequency [default option]\n", + " EccIC=1,\n", + " # Specify which modes are to be returned,\n", + " return_modes=[(2, 2)],\n", + ")\n", + "# See the pyseobnr/generate_waveform.py file for a description of more settings\n", + "\n", + "\n", + "t_seob, modes_seob = generate_modes_opt(\n", + " q,\n", + " chi_1,\n", + " chi_2,\n", + " omega_start,\n", + " eccentricity=eccentricity,\n", + " rel_anomaly=rel_anomaly,\n", + " approximant=\"SEOBNRv5EHM\",\n", + " settings=settings,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "59da8c7e-c67e-4af3-bf3d-1885da3a4564", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp22_py_real_imr = imr_modes_py[(2, 2)].real()\n", + "hp22_c_real_imr = imr_modes_c[(2, 2)].real()\n", + "hp22_seob_imr = modes_seob['2,2'].real\n", + "\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", + "# hp22_py_real_imr.start_time=0\n", + "# hp22_c_real_imr.start_time=0\n", + "\n", + "M = (m1+m2)*lal.MTSUN_SI\n", + "R = distance* 1e6 * lal.PC_SI / lal.C_SI\n", + "\n", + "# plt.plot(hp22_py_real_imr.sample_times/M,hp22_py_real_imr*R/M,label='Python')\n", + "# plt.plot(hp22_c_real_imr.sample_times/M,hp22_c_real_imr*R/M,label='C',ls='--')\n", + "# plt.plot(t_seob,modes_seob['2,2'],label='SEOBNRv5EHM',ls='-.')\n", + "\n", + "plt.plot(hp22_py_real_imr.sample_times/M,hp22_py_real_imr*R/M,label='Python')\n", + "plt.plot(hp22_c_real_imr.sample_times/M+12,hp22_c_real_imr*R/M,label='C',ls='--')\n", + "plt.plot(t_seob+8,hp22_seob_imr,label='SEOBNRv5EHM',ls='-.')\n", + "\n", + "plt.xlabel(r\"$t (M)$\")\n", + "plt.ylabel(r\"Re [$h_{22}$]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "# plt.xlim(left=-4000,right=-3000)\n", + "# plt.xlim(left=-100,right=100)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e4bd117b-cda0-4853-b808-aae85cd4fb3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(hp22_py_real_imr.sample_times/M,np.abs(imr_modes_py[(2, 2)]*R/M),label='Python')\n", + "plt.plot(hp22_c_real_imr.sample_times/M,np.abs(imr_modes_c[(2, 2)]*R/M),label='C',ls='--')\n", + "plt.plot(t_seob,np.abs(modes_seob['2,2']),label='SEOBNRv5EHM',ls='-.')\n", + "\n", + "# plt.plot(hp22_py_real_imr.sample_times/M,hp22_py_real_imr*R/M,label='Python')\n", + "# plt.plot(hp22_c_real_imr.sample_times/M+12,hp22_c_real_imr*R/M,label='C',ls='--')\n", + "# plt.plot(t_seob+8,modes_seob['2,2'],label='SEOBNRv5EHM',ls='-.')\n", + "\n", + "plt.xlabel(r\"$t (M)$\")\n", + "plt.ylabel(r\"$|h_{2}|$\")\n", + "plt.legend()\n", + "plt.grid()\n", + "# plt.xlim(left=-4000,right=-3000)\n", + "plt.xlim(left=-500,right=100)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7166baea-aa99-4171-bdc1-fcd59894828f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The match with C is: 0.2150\n", + "The match with python is: 0.2149\n" + ] + } + ], + "source": [ + "from pycbc.waveform import get_td_waveform\n", + "from pycbc.filter import match\n", + "from pycbc.psd import aLIGOZeroDetHighPower\n", + "from pycbc.types import TimeSeries\n", + "\n", + "TS_py = TimeSeries(hp22_py_real_imr,delta_t=delta_t)\n", + "TS_c = TimeSeries(hp22_c_real_imr,delta_t=delta_t)\n", + "TS_seob = TimeSeries(hp22_seob_imr,delta_t=delta_t)\n", + "\n", + "f_low = 20\n", + "sample_rate = 2048\n", + "\n", + "tlen = max(len(TS_seob), len(TS_c), len(TS_py))\n", + "TS_c.resize(tlen)\n", + "TS_py.resize(tlen)\n", + "TS_seob.resize(tlen)\n", + "\n", + "# Generate the aLIGO ZDHP PSD\n", + "delta_f = 1.0 / TS_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", + "m1, _ = match(TS_c, TS_seob, psd=psd, low_frequency_cutoff=f_low)\n", + "m2, _ = match(TS_py, TS_seob, psd=psd, low_frequency_cutoff=f_low)\n", + "print('The match with C is: {:.4f}'.format(m1))\n", + "print('The match with python is: {:.4f}'.format(m2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e6461bd-c4bf-4d26-89c6-410a13e3a400", "metadata": {}, "outputs": [], "source": [] @@ -417,7 +1172,7 @@ "kernelspec": { "display_name": "esigma-dev", "language": "python", - "name": "esigma-dev" + "name": "python3" }, "language_info": { "codemirror_mode": { From 6875b5d0eccdf15b10bf0173fa7f769de8fc5954 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Tue, 12 May 2026 23:35:01 +0530 Subject: [PATCH 27/36] Revert "fix link issues" This reverts commit 8ff938a91a0ceb4d2b1f085e0adef0b303bd16d5. --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1a1d675..f3e26b8 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,8 @@ | Model | Description | |---|---| -| `ESIGMAHM` | Eccentric, aligned-spin IMR waveform with higher modes (arXiv:[2409.13866](https://arxiv.org/abs/2409.13866 )) | -| `ESIGMASur` | Surrogate model of the (2,2) GW-mode of `ESIGMAHM` (arXiv:[2510.00116](https://arxiv.org/abs/2510.00116 ))| +| `ESIGMAHM` | Eccentric, aligned-spin IMR waveform with higher modes (arXiv:[2409.13866](https://arxiv.org/abs/2409.13866)) | +| `ESIGMASur` | Surrogate model of the (2,2) GW-mode of `ESIGMAHM` (arXiv:[2510.00116](https://arxiv.org/abs/2510.00116))| --- From d46c335fbf739beb5d467f7b75be384a0a6e3460 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 02:48:08 +0530 Subject: [PATCH 28/36] prestore variables for faster evaluation --- esigmapy/python_codes/esigma_pn_inspiral.py | 94 +++++++++++---------- 1 file changed, 51 insertions(+), 43 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index 57b4099..aff67ec 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -26,13 +26,12 @@ import numpy as np - # ------ Constants -------------------------------------------------- -EULER_GAMMA = 0.5772156649015329 # LAL_GAMMA / Euler–Mascheroni constant -LOG2 = 0.693147180559945309417232121458 -LOG3 = 1.09861228866810969139524523692 -LOG4 = 1.38629436111989061883446424292 -LOG5 = 1.60943791243410037460075933323 +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 ========================================= @@ -47,8 +46,8 @@ def SmallMassRatio(eta: float) -> float: 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): - from scipy.special import digamma # Look up the polygamma definition in LALSimESIGMA.c, line number 490 - import mpmath as mp + # Look up the polygamma definition in LALSimESIGMA.c, line number 490 + """ General polygamma wrapper: n = 0 → digamma (ψ) @@ -74,8 +73,10 @@ def polygamma(n: int, z: complex, mp_dps=30): # 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) @@ -210,37 +211,41 @@ def kappa_e_tilde(e: float) -> float: def phi_e_rad(e: float) -> float: ef = 1.0 - e * e + sqef = np.sqrt(ef) e2 = e * e - pre = 192.0 * np.sqrt(ef) / (985.0 * e2) - return pre * (np.sqrt(ef) * phi_e(e) - phi_e_tilde(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 * np.sqrt(ef)) - pf2 = 16382.0 * np.sqrt(ef) / (55691.0 * e2) - b1 = np.sqrt(ef) * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) - b2 = np.sqrt(ef) * psi_e(e) - psi_e_tilde(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 * np.sqrt(ef)) - pf2 = 12243.0 * np.sqrt(ef) / (76268.0 * e2) - b1 = -ef * np.sqrt(ef) * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) - b2 = np.sqrt(ef) * zed_e(e) - zed_e_tilde(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 = np.sqrt(ef) / e2 - b1 = np.sqrt(ef) * kappa_e(e) - kappa_e_tilde(e) + pf = sqef / e2 + b1 = sqef * kappa_e(e) - kappa_e_tilde(e) return pf * b1 / den @@ -993,9 +998,9 @@ def x_dot_4pn_SF(e: float, eta: float, S1z: float) -> float: inner_term = ( -1 + 15 * S1z2 + 42 * S1z4 + - np.sqrt(1 - S1z2) + - 13 * S1z2 * np.sqrt(1 - S1z2) + - 6 * S1z4 * np.sqrt(1 - S1z2) + + denom + + 13 * S1z2 * denom + + 6 * S1z4 * denom + 2j * PolyGammaFunc01 * (S1z + 3 * S1z3) - 2j * PolyGammaFunc02 * (S1z + 3 * S1z3) ) @@ -1478,9 +1483,6 @@ def e_dot_3pn_SO(e: float, m1: float, m2: float, ) ) / (51840.0 * ef_55 * M4) - -import math - def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: if abs(e) < 1e-12: return 0.0 @@ -2638,12 +2640,12 @@ def separation( ) -> 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) * np.sqrt(x) + + 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 * np.sqrt(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) @@ -2656,8 +2658,11 @@ def dx_dt(radiation_pn_order: int, x2 = x * x x3 = x2 * x - xsqx = x * np.sqrt(x) - x5 = x**5 + x5 = x3*x2 + sqx = np.sqrt(x) + xsqx = x * sqx + x2sqx = x2 * sqx + x3sqx = x3 * sqx # ------------------------- # Instantaneous PN part @@ -2675,8 +2680,8 @@ def dx_dt(radiation_pn_order: int, 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) * x2 * np.sqrt(x) - inst += x_dot_2_5pn_SF(e, eta, S1z) * x2 * np.sqrt(x) + 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 @@ -2684,11 +2689,11 @@ def dx_dt(radiation_pn_order: int, 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) * x3 * np.sqrt(x) - inst += x_dot_3_5_pn(e, eta) * x3 * np.sqrt(x) - inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) - inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3 * np.sqrt(x) - inst += x_dot_3_5pn_SF(e, eta, S1z) * x3 * np.sqrt(x) + 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 += ( @@ -2699,7 +2704,7 @@ def dx_dt(radiation_pn_order: int, 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) * np.sqrt(x) + 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 @@ -2735,7 +2740,10 @@ def de_dt(radiation_pn_order: int, x2 = x * x x3 = x2 * x x4 = x2 * x2 - xsqx = x * np.sqrt(x) + sqx = np.sqrt(x) + xsqx = x * sqx + x2sqx = x2 * sqx + x3sqx = x3 * sqx # ------------------------- # Instantaneous PN part @@ -2753,7 +2761,7 @@ def de_dt(radiation_pn_order: int, 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) * x2 * np.sqrt(x) + 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 @@ -2761,7 +2769,7 @@ def de_dt(radiation_pn_order: int, inst += e_dot_3pn_SS(e, m1, m2, S1z, S2z) * x3 if radiation_pn_order >= 7: - inst += e_dot_3_5pn(e, eta) * x3 * np.sqrt(x) + inst += e_dot_3_5pn(e, eta) * x3sqx if radiation_pn_order >= 8: inst += 0.0 # placeholder for higher order terms, not implemented From c68221de434c044a89675758510413c2a21495fa Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 02:51:25 +0530 Subject: [PATCH 29/36] Cleanup --- esigmapy/python_codes/esigma_pn_main.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index 367ff48..4b4d7d7 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -338,8 +338,6 @@ def isco_event(t, y): isco_event.direction = 1 t_max = MAX_SAMPLES * dt #----------------------------------- - import time as tt - start_time = tt.time() sol = solve_ivp( rhs, (0.0, t_max), # large upper bound; event will stop earlier @@ -350,7 +348,6 @@ def isco_event(t, y): max_step=dt, events=isco_event, ) - print(f'time taken = {tt.time()-start_time} secs', ) t_arr = sol.t y_arr = sol.y.T # shape (N, 4) From a458732e1f5192dbb0783282527244d6361ed65c Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 02:56:54 +0530 Subject: [PATCH 30/36] Restructure code --- esigmapy/python_codes/esigma_go_terms.py | 4411 +++++++++++----------- 1 file changed, 2150 insertions(+), 2261 deletions(-) diff --git a/esigmapy/python_codes/esigma_go_terms.py b/esigmapy/python_codes/esigma_go_terms.py index e0057f2..0ddec31 100644 --- a/esigmapy/python_codes/esigma_go_terms.py +++ b/esigmapy/python_codes/esigma_go_terms.py @@ -1,128 +1,99 @@ -""" -esigma_go_terms.py -==================== -Python translation of ESIGMA_GOTerms.c -""" - import numpy as np from dataclasses import dataclass -# Mapping M_PI2 to pi^2 (standard in PN GW theory for this coefficient) +# Storing constants M_PI = np.pi M_PI2 = M_PI ** 2 +LOG2 = np.log(2) +LOG3 = np.log(3) -def Complex(real: float, imag: float) -> complex: - """Helper function to mimic the C-style Complex constructor.""" - return complex(real, imag) +#---------------------------------------------------- def rect(r: float, phi: float) -> complex: """Convert polar coordinates to rectangular form.""" return r * np.exp(1j * phi) +#---------------------------------------------------- @dataclass -class KeplerVars: - # Basic orbital elements - pn_order: int - eta: float - Mtot: float - x: float - e: float - l: float - r: float - rDOT: float - PhiDOT: float - S1z: float - S2z: float - - # Powers of x - x2: float = 0.0; x2p5: float = 0.0; x3: float = 0.0; x3p5: float = 0.0 - x4: float = 0.0; x4p5: float = 0.0; x5: float = 0.0; x6: float = 0.0 - x7: float = 0.0; x8: float = 0.0; x9: float = 0.0 - - # Powers of e - e2: float = 0.0; e3: float = 0.0; e4: float = 0.0; e5: float = 0.0 - e6: float = 0.0; e7: float = 0.0; e8: float = 0.0; e9: float = 0.0 - - # Powers of r - r2: float = 0.0; r3: float = 0.0; r4: float = 0.0; r5: float = 0.0 - r6: float = 0.0; r7: float = 0.0; r8: float = 0.0; r9: float = 0.0 - - # Powers of rDOT - rDOT2: float = 0.0; rDOT3: float = 0.0; rDOT4: float = 0.0; rDOT5: float = 0.0 - rDOT6: float = 0.0; rDOT7: float = 0.0; rDOT8: float = 0.0; rDOT9: float = 0.0 - - # Powers of PhiDOT - PhiDOT2: float = 0.0; PhiDOT3: float = 0.0; PhiDOT4: float = 0.0; PhiDOT5: float = 0.0 - PhiDOT6: float = 0.0; PhiDOT7: float = 0.0; PhiDOT8: float = 0.0; PhiDOT9: float = 0.0 - - # Powers of S1z - S1z2: float = 0.0; S1z3: float = 0.0; S1z4: float = 0.0; S1z5: float = 0.0 - S1z6: float = 0.0; S1z7: float = 0.0; S1z8: float = 0.0; S1z9: float = 0.0 - S1z10: float = 0.0; S1z11: float = 0.0; S1z12: float = 0.0; S1z13: float = 0.0 - S1z14: float = 0.0; S1z15: float = 0.0; S1z16: float = 0.0 - - # Powers of S2z - S2z2: float = 0.0; S2z3: float = 0.0; S2z4: float = 0.0 - S2z5: float = 0.0; S2z6: float = 0.0 - - # Powers of eta - eta2: float = 0.0; eta3: float = 0.0; eta4: float = 0.0 - eta5: float = 0.0; eta6: float = 0.0 - - # Powers of Mtot - Mtot2: float = 0.0; Mtot3: float = 0.0; Mtot4: float = 0.0 - Mtot5: float = 0.0; Mtot6: float = 0.0 - - def compute_powers(self): - """Compute all power fields from the base values.""" - self.x2 = self.x**2; self.x2p5 = self.x**2.5; self.x3 = self.x**3 - self.x3p5 = self.x**3.5; self.x4 = self.x**4; self.x4p5 = self.x**4.5 - self.x5 = self.x**5; self.x6 = self.x**6; self.x7 = self.x**7 - self.x8 = self.x**8; self.x9 = self.x**9 - - self.e2 = self.e**2; self.e3 = self.e**3; self.e4 = self.e**4; self.e5 = self.e**5 - self.e6 = self.e**6; self.e7 = self.e**7; self.e8 = self.e**8; self.e9 = self.e**9 - - self.r2 = self.r**2; self.r3 = self.r**3; self.r4 = self.r**4; self.r5 = self.r**5 - self.r6 = self.r**6; self.r7 = self.r**7; self.r8 = self.r**8; self.r9 = self.r**9 - - self.rDOT2 = self.rDOT**2; self.rDOT3 = self.rDOT**3; self.rDOT4 = self.rDOT**4 - self.rDOT5 = self.rDOT**5; self.rDOT6 = self.rDOT**6; self.rDOT7 = self.rDOT**7 - self.rDOT8 = self.rDOT**8; self.rDOT9 = self.rDOT**9 - - self.PhiDOT2 = self.PhiDOT**2; self.PhiDOT3 = self.PhiDOT**3; self.PhiDOT4 = self.PhiDOT**4 - self.PhiDOT5 = self.PhiDOT**5; self.PhiDOT6 = self.PhiDOT**6; self.PhiDOT7 = self.PhiDOT**7 - self.PhiDOT8 = self.PhiDOT**8; self.PhiDOT9 = self.PhiDOT**9 - - self.S1z2 = self.S1z**2; self.S1z3 = self.S1z**3; self.S1z4 = self.S1z**4 - self.S1z5 = self.S1z**5; self.S1z6 = self.S1z**6; self.S1z7 = self.S1z**7 - self.S1z8 = self.S1z**8; self.S1z9 = self.S1z**9; self.S1z10 = self.S1z**10 - self.S1z11 = self.S1z**11; self.S1z12 = self.S1z**12; self.S1z13 = self.S1z**13 - self.S1z14 = self.S1z**14; self.S1z15 = self.S1z**15; self.S1z16 = self.S1z**16 - - self.S2z2 = self.S2z**2; self.S2z3 = self.S2z**3; self.S2z4 = self.S2z**4 - self.S2z5 = self.S2z**5; self.S2z6 = self.S2z**6 - - self.eta2 = self.eta**2; self.eta3 = self.eta**3; self.eta4 = self.eta**4 - self.eta5 = self.eta**5; self.eta6 = self.eta**6 - - self.Mtot2 = self.Mtot**2; self.Mtot3 = self.Mtot**3; self.Mtot4 = self.Mtot**4 - self.Mtot5 = self.Mtot**5; self.Mtot6 = self.Mtot**6 - -# All the hGO_l_m functions contain the 3PN non-spinning general orbit (GO) terms -# from Mishra et al. arXiv:1501.07096 and 3.5PN quasi-circular spinning -# corrections as given in Henry et al, arXiv:2209.00374v2 - -# The (2,2) mode has newly computed 4PN non-spinning quasi-circular piece from -# arXiv:2304.11185 +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 -def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, +@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: KeplerVars) -> complex: - - # Note: In Python, `params` should be an object (like a dataclass or SimpleNamespace) - # so that attributes can be accessed via dot notation (e.g., params.PhiDOT2). + x: float, params: CommonVars) -> complex: # For black holes kappa and lambda is 1 kappa1 = 1.0 @@ -130,602 +101,605 @@ def hGO_2_m_2(mass: float, Nu: float, r: float, rDOT: float, lambda1 = 1.0 lambda2 = 1.0 - delta = np.sqrt(1 - 4 * Nu) + # delta = np.sqrt(1 - 4 * eta) + delta = params.delta - combination_a = (PhiDOT * r + Complex(0, 1) * rDOT) + 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_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 (mass / r + params.PhiDOT2 * params.r2 + - Complex(0, 2) * PhiDOT * r * rDOT - params.rDOT2) + return (total_mass / r + PhiDOT2 * r2 + + complex(0, 2) * PhiDOT * r * rDOT - rDOT2) elif vpnorder == 2: - return ((21 * params.Mtot2 * (-10 + Nu) - - 27 * (-1 + 3 * Nu) * params.r2 * combination_b * combination_a3 + - mass * r * - ((11 + 156 * Nu) * params.PhiDOT2 * params.r2 + - Complex(0, 10) * (5 + 27 * Nu) * PhiDOT * r * rDOT - - 3 * (15 + 32 * Nu) * params.rDOT2)) / - (42. * params.r2)) + 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 ((params.Mtot2 * (Complex(0, -1) * rDOT * - ((3 + 3 * delta - 8 * Nu) * S1z + - (3 - 3 * delta - 8 * Nu) * S2z) + + 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 * Nu) * S1z + - (-3 + 3 * delta + 5 * Nu) * S2z))) / - (3. * params.r2)) + ((-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 - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x2p5) / 3.) + - # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - Nu * (S1z + S2z)) * params.x2p5) / 3.) + # - ((-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 * params.Mtot3 * (3028 + 1267 * Nu + 158 * params.eta2) + - 9 * (83 - 589 * Nu + 1111 * params.eta2) * params.r3 * + return ((6 * total_mass3 * (3028 + 1267 * eta + 158 * eta2) + + 9 * (83 - 589 * eta + 1111 * eta2) * r3 * combination_b2 * combination_a4 + - params.Mtot2 * r * - ((-11891 - 36575 * Nu + 13133 * params.eta2) * params.PhiDOT2 * - params.r2 + - Complex(0, 8) * (-773 - 3767 * Nu + 2852 * params.eta2) * + total_mass2 * r * + ((-11891 - 36575 * eta + 13133 * eta2) * PhiDOT2 * + r2 + + complex(0, 8) * (-773 - 3767 * eta + 2852 * eta2) * PhiDOT * r * rDOT - - 6 * (-619 + 2789 * Nu + 934 * params.eta2) * params.rDOT2) - - 3 * mass * params.r2 * - (2 * (-835 - 19 * Nu + 2995 * params.eta2) * params.PhiDOT4 * - params.r4 + - Complex(0, 6) * (-433 - 721 * Nu + 1703 * params.eta2) * - params.PhiDOT3 * params.r3 * rDOT + - 6 * (-33 + 1014 * Nu + 232 * params.eta2) * params.PhiDOT2 * - params.r2 * params.rDOT2 + - Complex(0, 4) * (-863 + 1462 * Nu + 2954 * params.eta2) * - PhiDOT * r * params.rDOT3 - - 3 * (-557 + 664 * Nu + 1712 * params.eta2) * params.rDOT4)) / - (1512. * params.r3) + - (3 * params.Mtot3 * - (S1z * (4 * Nu * S2z + (1 + delta - 2 * Nu) * S1z * kappa1) - - (-1 + delta + 2 * Nu) * params.S2z2 * kappa2)) / - (4. * params.r3)) + 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 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + - # ((kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x3) + # - ((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 ((params.Mtot2 * Nu * - (2 * mass * (Complex(0, -702) * PhiDOT * r + rDOT) + + return ((total_mass2 * eta * + (2 * total_mass * (complex(0, -702) * PhiDOT * r + rDOT) + 3 * r * - (Complex(0, -316) * params.PhiDOT3 * params.r3 - - 847 * params.PhiDOT2 * params.r2 * rDOT + - Complex(0, 184) * PhiDOT * r * params.rDOT2 - - 122 * params.rDOT3))) / - (105. * params.r3) + (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*Nu*(-S1z + S2z) + 101*Nu*(S1z + S2z) + 132*params.eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) + # + ((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 - ((params.Mtot2 * - (mass * - ((238 + delta * (238 - 141 * Nu) + Nu * (-181 + 474 * Nu)) * + ((total_mass2 * + (total_mass * + ((238 + delta * (238 - 141 * eta) + eta * (-181 + 474 * eta)) * PhiDOT * r * S1z + - Complex(0, 8) * - (55 + delta * (55 - 19 * Nu) + - 2 * Nu * (-50 + 43 * Nu)) * + complex(0, 8) * + (55 + delta * (55 - 19 * eta) + + 2 * eta * (-50 + 43 * eta)) * rDOT * S1z + - (238 + delta * (-238 + 141 * Nu) + Nu * (-181 + 474 * Nu)) * + (238 + delta * (-238 + 141 * eta) + eta * (-181 + 474 * eta)) * PhiDOT * r * S2z + - Complex(0, 8) * - (55 + delta * (-55 + 19 * Nu) + - 2 * Nu * (-50 + 43 * Nu)) * + complex(0, 8) * + (55 + delta * (-55 + 19 * eta) + + 2 * eta * (-50 + 43 * eta)) * rDOT * S2z) - - r * (PhiDOT * r * params.rDOT2 * - (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * Nu + - 188 * params.eta2) * + r * (PhiDOT * r * rDOT2 * + (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * eta + + 188 * eta2) * S1z) + - (18 * (-1 + delta) + 5 * (63 + 55 * delta) * Nu - - 188 * params.eta2) * + (18 * (-1 + delta) + 5 * (63 + 55 * delta) * eta - + 188 * eta2) * S2z) - - Complex(0, 2) * params.rDOT3 * - ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * Nu - - 4 * params.eta2) * + complex(0, 2) * rDOT3 * + ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * eta - + 4 * eta2) * S1z + - (-27 + 27 * delta + 30 * Nu - 42 * delta * Nu - - 4 * params.eta2) * + (-27 + 27 * delta + 30 * eta - 42 * delta * eta - + 4 * eta2) * S2z) + - Complex(0, 2) * params.PhiDOT2 * params.r2 * rDOT * - ((51 + 88 * Nu * (-3 + 5 * Nu) + - delta * (51 + 62 * Nu)) * + complex(0, 2) * PhiDOT2 * r2 * rDOT * + ((51 + 88 * eta * (-3 + 5 * eta) + + delta * (51 + 62 * eta)) * S1z + - (51 + 88 * Nu * (-3 + 5 * Nu) - - delta * (51 + 62 * Nu)) * + (51 + 88 * eta * (-3 + 5 * eta) - + delta * (51 + 62 * eta)) * S2z) + - params.PhiDOT3 * params.r3 * - ((120 * (1 + delta) + (-483 + 83 * delta) * Nu + - 234 * params.eta2) * + PhiDOT3 * r3 * + ((120 * (1 + delta) + (-483 + 83 * delta) * eta + + 234 * eta2) * S1z + - (120 + 3 * Nu * (-161 + 78 * Nu) - - delta * (120 + 83 * Nu)) * + (120 + 3 * eta * (-161 + 78 * eta) - + delta * (120 + 83 * eta)) * S2z)))) / - (84. * params.r3))) + (84. * r3))) elif vpnorder == 6: - return ((4 * params.Mtot4 * - (-8203424 + 2180250 * params.eta2 + 592600 * params.eta3 + - 15 * Nu * (-5503804 + 142065 * M_PI2)) - - 2700 * (-507 + 6101 * Nu - 25050 * params.eta2 + 34525 * params.eta3) * - params.r4 * combination_b3 * combination_a5 + - params.Mtot3 * r * - (params.PhiDOT2 * - (337510808 - 198882000 * params.eta2 + - 56294600 * params.eta3 + - Nu * (183074880 - 6392925 * M_PI2)) * - params.r2 + - Complex(0, 110) * PhiDOT * - (-5498800 - 785120 * params.eta2 + 909200 * params.eta3 + - 3 * Nu * (-1849216 + 38745 * M_PI2)) * + 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 * params.eta2 - 5092400 * params.eta3 + - 45 * Nu * (2794864 + 142065 * M_PI2)) * - params.rDOT2) - - 20 * params.Mtot2 * params.r2 * - ((-986439 + 1873255 * Nu - 9961400 * params.eta2 + - 6704345 * params.eta3) * - params.PhiDOT4 * params.r4 + - Complex(0, 4) * - (-273687 - 978610 * Nu - 4599055 * params.eta2 + - 2783005 * params.eta3) * - params.PhiDOT3 * params.r3 * rDOT + - (-181719 + 19395325 * Nu + 8237980 * params.eta2 + - 2612735 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT2 + - Complex(0, 8) * - (-234312 + 1541140 * Nu + 1230325 * params.eta2 + - 1828625 * params.eta3) * - PhiDOT * r * params.rDOT3 - + (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 * Nu + 2004715 * params.eta2 + - 1810425 * params.eta3) * - params.rDOT4) + - 300 * mass * params.r3 * + (-370268 + 1085140 * eta + 2004715 * eta2 + + 1810425 * eta3) * + rDOT4) + + 300 * total_mass * r3 * (4 * - (12203 - 36427 * Nu - 27334 * params.eta2 + - 149187 * params.eta3) * - params.PhiDOT6 * params.r6 + - Complex(0, 2) * - (44093 - 68279 * Nu - 295346 * params.eta2 + - 541693 * params.eta3) * - params.PhiDOT5 * params.r5 * rDOT + + (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 * Nu + 247505 * params.eta2 + - 394771 * params.eta3) * - params.PhiDOT4 * params.r4 * params.rDOT2 + - Complex(0, 2) * - (97069 - 383990 * Nu - 8741 * params.eta2 + - 1264800 * params.eta3) * - params.PhiDOT3 * params.r3 * params.rDOT3 + - (-42811 + 53992 * Nu + 309136 * params.eta2 - - 470840 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT4 + - Complex(0, 2) * - (51699 - 252256 * Nu + 131150 * params.eta2 + - 681160 * params.eta3) * - PhiDOT * r * params.rDOT5 - + (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 * Nu + 26920 * params.eta2 + - 207200 * params.eta3) * - params.rDOT6)) / - (3.3264e6 * params.r4) + (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)*Nu - 9*(15 + 7*delta)*kappa1*Nu + 12*(7 - 17*kappa1)*params.eta2)*params.S1z2 + 2*S1z*(Complex(0,-42)*(1 + delta - 2*Nu) - 84*(1 + delta - Nu)*M_PI + Nu*(-271 + 288*Nu)*S2z) + S2z*(12*(7 - 17*kappa2)*params.eta2*S2z + 4*(-1 + delta)*(Complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + Nu*(168*(Complex(0,1) + M_PI) + 7*delta*(17 + 9*kappa2)*S2z - 9*(7 + 15*kappa2)*S2z)))*params.x4)/63. + # + (((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 * - (params.Mtot3 * - (2 * mass * - (S1z * (Complex(0, 14) * (1 + delta - 2 * Nu) + - 42 * (1 + delta - 2 * Nu) * Nu * S1z + + (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 * Nu) + - Nu * (-773 + 108 * Nu)) * + (438 + delta * (438 + 103 * eta) + + eta * (-773 + 108 * eta)) * S1z) + 2 * - (Complex(0, -7) * (-1 + delta + 2 * Nu) + - (995 - 192 * Nu) * Nu * S1z) * + (complex(0, -7) * (-1 + delta + 2 * eta) + + (995 - 192 * eta) * eta * S1z) * S2z - - (42 * Nu * (-1 + delta + 2 * Nu) + - kappa2 * (-438 + (773 - 108 * Nu) * Nu + - delta * (438 + 103 * Nu))) * - params.S2z2) + - r * (params.rDOT2 * - (Complex(0, 56) * (1 + delta - 2 * Nu) * S1z + - (-56 * (1 + delta - 2 * Nu) * Nu + + (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) * Nu + - 24 * params.eta2)) * - params.S1z2 + - 4 * Nu * (-3 + 44 * Nu) * S1z * S2z + - S2z * (Complex(0, -56) * (-1 + delta + 2 * Nu) + - 56 * Nu * (-1 + delta + 2 * Nu) * S2z + + 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 * Nu + 24 * params.eta2 + - delta * (-291 + 308 * Nu)) * + (291 - 890 * eta + 24 * eta2 + + delta * (-291 + 308 * eta)) * S2z)) + - params.PhiDOT2 * params.r2 * - (Complex(0, 196) * (1 + delta - 2 * Nu) * S1z + - (56 * Nu * (7 + 7 * delta + Nu) + + PhiDOT2 * r2 * + (complex(0, 196) * (1 + delta - 2 * eta) * S1z + + (56 * eta * (7 + 7 * delta + eta) + kappa1 * (-153 * (1 + delta) - - 2 * (62 + 215 * delta) * Nu + - 804 * params.eta2)) * - params.S1z2 + - 8 * (60 - 187 * Nu) * Nu * S1z * S2z + - S2z * (Complex(0, -196) * (-1 + delta + 2 * Nu) + - 56 * Nu * (7 - 7 * delta + Nu) * S2z + + 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 * Nu * (-31 + 201 * Nu) + - delta * (153 + 430 * Nu)) * + (-153 + 4 * eta * (-31 + 201 * eta) + + delta * (153 + 430 * eta)) * S2z)) + 2 * PhiDOT * r * rDOT * - (Complex(0, -1) * + (complex(0, -1) * (117 * (1 + delta) * kappa1 - - 434 * (1 + delta) * Nu + - (-23 + 211 * delta) * kappa1 * Nu + - 2 * (14 - 195 * kappa1) * params.eta2) * - params.S1z2 + + 434 * (1 + delta) * eta + + (-23 + 211 * delta) * kappa1 * eta + + 2 * (14 - 195 * kappa1) * eta2) * + S1z2 + 4 * S1z * - (35 * (1 + delta - 2 * Nu) + - Complex(0, 1) * (9 - 209 * Nu) * Nu * S2z) + - S2z * (-140 * (-1 + delta + 2 * Nu) + - Complex(0, 1) * - (-14 * Nu * (-31 + 31 * delta + 2 * Nu) + + (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) * Nu + - 390 * params.eta2)) * + (23 + 211 * delta) * eta + + 390 * eta2)) * S2z))))) / - params.r4)) + r4)) # + Henry et al. QC spinning hereditary terms - # (((-8 * M_PI * ((1 + delta - Nu) * S1z + S2z - (delta + Nu) * S2z) * params.x4) / 3.)) + # (((-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*params.eta3*(S1z + S2z) + Nu*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*params.S1z3 - 1008*params.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*params.S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*params.S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*params.eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*params.S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. + # ((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 - ((params.Mtot2 * - (-3 * mass * r * - (Complex(0, -16) * params.rDOT3 * - (Complex(0, 12) * Nu * (-16703 + 4427 * Nu) + - 35 * Nu * - (4578 + Nu * (-4288 + 765 * Nu) + - delta * (3748 + 802 * Nu)) * + ((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 * Nu * (1874 + 401 * Nu)) + - Nu * (4578 + Nu * (-4288 + 765 * Nu))) * + (delta * (942 - 2 * eta * (1874 + 401 * eta)) + + eta * (4578 + eta * (-4288 + 765 * eta))) * S2z - 32970 * (S1z + delta * S1z + S2z)) + - 4 * PhiDOT * r * params.rDOT2 * - (-338520 * params.eta3 * (S1z + S2z) + + 4 * PhiDOT * r * rDOT2 * + (-338520 * eta3 * (S1z + S2z) + 48930 * (S1z + delta * S1z + S2z - delta * S2z) + - Nu * (Complex(0, 3420696) - + eta * (complex(0, 3420696) - 35 * (14154 + 21167 * delta) * S1z - 495390 * S2z + 740845 * delta * S2z) + - params.eta2 * (Complex(0, -612336) + + eta2 * (complex(0, -612336) + 245 * (3566 - 1565 * delta) * S1z + 245 * (3566 + 1565 * delta) * S2z)) + - params.PhiDOT3 * params.r3 * - (2515380 * params.eta3 * (S1z + S2z) - - 5 * Nu * - (Complex(0, 1859936) + + 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 * params.eta2 * - (Complex(0, 381348) + + 4 * eta2 * + (complex(0, 381348) + 35 * (-18505 + 1777 * delta) * S1z - 35 * (18505 + 1777 * delta) * S2z)) + - Complex(0, 8) * params.PhiDOT2 * params.r2 * rDOT * - (779100 * params.eta3 * (S1z + S2z) + - 5 * Nu * - (Complex(0, 828806) + + 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) + - params.eta2 * (Complex(0, -976002) + + eta2 * (complex(0, -976002) + 35 * (-29599 + 3109 * delta) * S1z - 35 * (29599 + 3109 * delta) * S2z))) + - 3 * params.r2 * - (Complex(0, 4) * params.PhiDOT2 * params.r2 * params.rDOT3 * - (Complex(0, 2) * (65451 - 350563 * Nu) * Nu + - 105 * Nu * - (6020 + delta * (3114 + 411 * Nu) + - Nu * (-4513 + 9136 * Nu)) * + 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 + Nu * (1038 + 137 * Nu)) + - Nu * (6020 + Nu * (-4513 + 9136 * Nu))) * + (-3 * delta * (-408 + eta * (1038 + 137 * eta)) + + eta * (6020 + eta * (-4513 + 9136 * eta))) * S2z - 128520 * (S1z + delta * S1z + S2z)) + - PhiDOT * r * params.rDOT4 * - (Complex(0, -128) * Nu * (2487 + 18334 * Nu) - - 105 * Nu * - (8689 + 8 * Nu * (-687 + 1402 * Nu) + - delta * (2143 + 8212 * Nu)) * + PhiDOT * r * rDOT4 * + (complex(0, -128) * eta * (2487 + 18334 * eta) - + 105 * eta * + (8689 + 8 * eta * (-687 + 1402 * eta) + + delta * (2143 + 8212 * eta)) * S1z + 105 * - (Nu * (-8689 + 8 * (687 - 1402 * Nu) * Nu) + - delta * (-1470 + Nu * (2143 + 8212 * Nu))) * + (eta * (-8689 + 8 * (687 - 1402 * eta) * eta) + + delta * (-1470 + eta * (2143 + 8212 * eta))) * S2z + 154350 * (S1z + delta * S1z + S2z)) - - Complex(0, 6) * params.rDOT5 * - (-11760 * params.eta3 * (S1z + S2z) + - 4 * params.eta2 * - (Complex(0, 57338) + + complex(0, 6) * rDOT5 * + (-11760 * eta3 * (S1z + S2z) + + 4 * eta2 * + (complex(0, 57338) + 35 * (569 + 301 * delta) * S1z + 35 * (569 - 301 * delta) * S2z) - - 16 * Nu * - (Complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + + 16 * eta * + (complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + 35 * (72 - 71 * delta) * S2z) + 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + - params.PhiDOT5 * params.r5 * - (2263380 * params.eta3 * (S1z + S2z) + - 3 * Nu * - (Complex(0, 653432) + + 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 * params.eta2 * - (Complex(0, -291268) + + 4 * eta2 * + (complex(0, -291268) + 105 * (-7669 + 165 * delta) * S1z - 105 * (7669 + 165 * delta) * S2z)) + - Complex(0, 14) * params.PhiDOT4 * params.r4 * rDOT * - (385170 * params.eta3 * (S1z + S2z) + - 15 * Nu * - (Complex(0, 16914) + (8633 + 2267 * delta) * S1z + + 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 * params.eta2 * - (Complex(0, -67904) + + 2 * eta2 * + (complex(0, -67904) + 15 * (-13932 + 679 * delta) * S1z - 15 * (13932 + 679 * delta) * S2z)) + - 6 * params.PhiDOT3 * params.r3 * params.rDOT2 * - (-6720 * params.eta3 * (S1z + S2z) + - 5 * Nu * - (Complex(0, -241704) + + 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) + - params.eta2 * (Complex(0, -364384) + + eta2 * (complex(0, -364384) + 35 * (5059 - 4753 * delta) * S1z + 35 * (5059 + 4753 * delta) * S2z))) + - 14 * params.Mtot2 * - (Complex(0, 30) * rDOT * - (15280 * params.eta3 * (S1z + S2z) - - 4 * params.eta2 * - (Complex(0, -111) + 3562 * S1z + 682 * delta * S1z + - 945 * kappa1 * params.S1z3 + + 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) * params.S1z2) * + 945 * (-2 + kappa1) * S1z2) * S2z + - 945 * (-2 + kappa2) * S1z * params.S2z2 + - 945 * kappa2 * params.S2z3) + - Nu * (Complex(0, -27520) + + 945 * (-2 + kappa2) * S1z * S2z2 + + 945 * kappa2 * S2z3) + + eta * (complex(0, -27520) + 378 * (5 * (1 + delta) * kappa1 + 3 * (3 + delta) * lambda1) * - params.S1z3 + + S1z3 + (29749 - 13605 * delta) * S2z - - 1512 * (1 + delta) * kappa1 * params.S1z2 * S2z - + 1512 * (1 + delta) * kappa1 * S1z2 * S2z - 378 * (5 * (-1 + delta) * kappa2 + 3 * (-3 + delta) * lambda2) * - params.S2z3 + + S2z3 + S1z * (29749 + 13605 * delta + 1512 * (-1 + delta) * kappa2 * - params.S2z2)) + + S2z2)) + 2 * (-8009 * (1 + delta) * S1z - - 567 * (1 + delta) * lambda1 * params.S1z3 + + 567 * (1 + delta) * lambda1 * S1z3 + (-1 + delta) * S2z * - (8009 + 567 * lambda2 * params.S2z2))) + + (8009 + 567 * lambda2 * S2z2))) + PhiDOT * r * - (285840 * params.eta3 * (S1z + S2z) - - 30 * params.eta2 * - (Complex(0, 11504) + 1890 * kappa1 * params.S1z3 + + (285840 * eta3 * (S1z + S2z) - + 30 * eta2 * + (complex(0, 11504) + 1890 * kappa1 * S1z3 + 23090 * S2z - 1823 * delta * S2z + - 1890 * (-2 + kappa1) * params.S1z2 * S2z + - 1890 * kappa2 * params.S2z3 + + 1890 * (-2 + kappa1) * S1z2 * S2z + + 1890 * kappa2 * S2z3 + S1z * (23090 + 1823 * delta + - 1890 * (-2 + kappa2) * params.S2z2)) + + 1890 * (-2 + kappa2) * S2z2)) + 30 * (689 * (1 + delta) * S1z - - 1134 * (1 + delta) * lambda1 * params.S1z3 + + 1134 * (1 + delta) * lambda1 * S1z3 + (-1 + delta) * S2z * - (-689 + 1134 * lambda2 * params.S2z2)) + - 2 * Nu * - (Complex(0, 415432) - 66840 * 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) * - params.S1z3 - + S1z3 - 10736 * delta * S2z - - 2457 * (1 + delta) * kappa1 * params.S1z2 * + 2457 * (1 + delta) * kappa1 * S1z2 * S2z + 2457 * (-1 + delta) * kappa2 * S1z * - params.S2z2 - + S2z2 - 189 * (5 * (-1 + delta) * kappa2 + 6 * (-3 + delta) * lambda2) * - params.S2z3)))))) / - (317520. * params.r4)) + S2z3)))))) / + (317520. * r4)) # + Henry et al. QC spinning hereditary terms - # (2 * M_PI * (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + S2z * (4 * Nu * S1z - kappa2 * (-1 + delta + 2 * Nu) * S2z)) * params.x4p5) + # (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) - -# hQC_l_m() functions contain only the non-spinning hereditary terms at -# particular PN order. -def hQC_2_m_2(mass: float, Nu: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: KeplerVars) -> complex: +@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 * mass / np.exp(0.5) - r0 = b0 + # 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 * Nu) + # 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 * Nu) / 21.) * params.x2) + # return ((-5.095238095238095 + (55 * eta) / 21.) * x2) if vpnorder == 3: - # return (4 * M_PI * params.x2p5) - return (complex(0, 0.6666666666666666) * params.x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) + # 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 * Nu) / 108. + (2047 * params.eta2) / 756.) * params.x3) + # return ((-2.874338624338624 - (1069 * eta) / 108. + (2047 * eta2) / 756.) * x3) elif vpnorder == 5: - # return ((complex(0,-48)*Nu - (214*M_PI)/21. + (68*Nu*M_PI)/21.)*params.x3p5) - # return ((2 * (-107 + 34 * Nu) * M_PI * params.x3p5) / 21.) # This is old implementation - return (complex(0, 0.015873015873015872) * (-107 + 34 * Nu) * params.x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x))) + # 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 ((params.x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * Nu) * M_PI) - 13696 * np.log(16 * x))) / 1680.) # This is the old implementation. + # 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 (params.x4 * (-45.216145124716554 + (456 * EulerGamma) / 35. - 16 * EulerGamma * EulerGamma - + 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) * Nu * S1z + complex(0, 5.333333333333333) * EulerGamma * Nu * S1z - - (8 * M_PI * S1z) / 3. + (8 * Nu * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - - complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * Nu * S2z + - complex(0, 5.333333333333333) * EulerGamma * Nu * S2z - (8 * M_PI * S2z) / 3. + (8 * Nu * M_PI * S2z) / 3. + - (912 * np.log(2)) / 35. - 64 * EulerGamma * np.log(2) + complex(0, 32) * M_PI * np.log(2) - - complex(0, 10.666666666666666) * S1z * np.log(2) + complex(0, 10.666666666666666) * Nu * S1z * np.log(2) - - complex(0, 10.666666666666666) * S2z * np.log(2) + complex(0, 10.666666666666666) * Nu * S2z * np.log(2) - - 64 * np.log(2) * np.log(2) + (88 * np.log(b0)) / 3. - 32 * EulerGamma * np.log(b0) + - complex(0, 16) * M_PI * np.log(b0) - complex(0, 5.333333333333333) * S1z * np.log(b0) + - complex(0, 5.333333333333333) * Nu * S1z * np.log(b0) - complex(0, 5.333333333333333) * S2z * np.log(b0) + - complex(0, 5.333333333333333) * Nu * S2z * np.log(b0) - 64 * np.log(2) * np.log(b0) - - 16 * np.log(b0) * np.log(b0) - (1712 * np.log(r0)) / 105. + (684 * np.log(x)) / 35. - - 48 * EulerGamma * np.log(x) + complex(0, 24) * M_PI * np.log(x) - complex(0, 8) * S1z * np.log(x) + - complex(0, 8) * Nu * S1z * np.log(x) - complex(0, 8) * S2z * np.log(x) + complex(0, 8) * Nu * S2z * np.log(x) - - 96 * np.log(2) * np.log(x) - 48 * np.log(b0) * np.log(x) - 36 * np.log(x) * np.log(x) - + 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 * np.log(2) + 12 * np.log(b0) + 18 * np.log(x)))) + 24 * LOG2 + 12 * logb0 + 18 * logx))) elif vpnorder == 7: - # return (((-2173 - 4990 * Nu + 1120 * params.eta2) * M_PI * params.x4p5) / 378.) # This is the old implementation. - return (complex(0, 0.0004409171075837742) * params.x4p5 * (23903 - 26076 * EulerGamma + 57452 * Nu - - 59880 * EulerGamma * Nu - 18728 * params.eta2 + 13440 * EulerGamma * params.eta2 + - complex(0, 13038) * M_PI + complex(0, 29940) * Nu * M_PI - complex(0, 6720) * params.eta2 * M_PI - - 8316 * kappa1 * params.S1z2 - 8316 * delta * kappa1 * params.S1z2 + 9072 * EulerGamma * kappa1 * params.S1z2 + - 9072 * delta * EulerGamma * kappa1 * params.S1z2 + 16632 * kappa1 * Nu * params.S1z2 - - 18144 * EulerGamma * kappa1 * Nu * params.S1z2 - complex(0, 4536) * kappa1 * M_PI * params.S1z2 - - complex(0, 4536) * delta * kappa1 * M_PI * params.S1z2 + complex(0, 9072) * kappa1 * Nu * M_PI * params.S1z2 - - 33264 * Nu * S1z * S2z + 36288 * EulerGamma * Nu * S1z * S2z - complex(0, 18144) * Nu * M_PI * S1z * S2z - - 8316 * kappa2 * params.S2z2 + 8316 * delta * kappa2 * params.S2z2 + 9072 * EulerGamma * kappa2 * params.S2z2 - - 9072 * delta * EulerGamma * kappa2 * params.S2z2 + 16632 * kappa2 * Nu * params.S2z2 - - 18144 * EulerGamma * kappa2 * Nu * params.S2z2 - complex(0, 4536) * kappa2 * M_PI * params.S2z2 + - complex(0, 4536) * delta * kappa2 * M_PI * params.S2z2 + complex(0, 9072) * kappa2 * Nu * M_PI * params.S2z2 - - 52152 * np.log(2) - 119760 * Nu * np.log(2) + 26880 * params.eta2 * np.log(2) + - 18144 * kappa1 * params.S1z2 * np.log(2) + 18144 * delta * kappa1 * params.S1z2 * np.log(2) - - 36288 * kappa1 * Nu * params.S1z2 * np.log(2) + 72576 * Nu * S1z * S2z * np.log(2) + - 18144 * kappa2 * params.S2z2 * np.log(2) - 18144 * delta * kappa2 * params.S2z2 * np.log(2) - - 36288 * kappa2 * Nu * params.S2z2 * np.log(2) + 12 * (-2173 - 4990 * Nu + 1120 * params.eta2 + - 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - - 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(b0) + 18 * (-2173 - 4990 * Nu + - 1120 * params.eta2 + 756 * kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + 3024 * Nu * S1z * S2z - - 756 * kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) * np.log(x))) + # 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 * (params.x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * Nu) + - 12 * (628830397253 + 1854914893791 * Nu + 421984442880 * np.log(2)) + 415134720 * EulerGamma * (6099 - 40277 * Nu + complex(0, 70) * (107 - 13 * Nu) * M_PI + 280 * (-107 + 13 * Nu) * np.log(2)) + - 35 * (-28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) + - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2 - 955450349568 * Nu * np.log(2) + - 3321077760 * (-107 + 13 * Nu) * np.log(2) * np.log(2) - complex(0, 5930496) * M_PI * (6099 - 74773 * Nu + 280 * (-107 + 13 * Nu) * np.log(2))) - 5700491596800 * np.log(mass) + - 6966444925440 * np.log(x) + 69189120 * (420 * (-107 + 13 * Nu) * np.log(b0) * np.log(b0) + - 420 * (-107 + 13 * Nu) * np.log(mass) * np.log(mass) - 14 * np.log(mass) * (-11803 * Nu + - 60 * EulerGamma * (-107 + 13 * Nu) + complex(0, 30) * (107 - 13 * Nu) * M_PI + - 120 * (-107 + 13 * Nu) * np.log(2) + 90 * (-107 + 13 * Nu) * np.log(x)) + 14 * np.log(b0) * (5885 - 6420 * EulerGamma - 11803 * Nu + 780 * EulerGamma * Nu + complex(0, 3210) * M_PI - - complex(0, 390) * Nu * M_PI + 120 * (-107 + 13 * Nu) * np.log(2) + (6420 - 780 * Nu) * np.log(mass) + - 90 * (-107 + 13 * Nu) * np.log(x)) + 5 * np.log(x) * (-74149 * Nu + 252 * EulerGamma * (-107 + 13 * Nu) - - complex(0, 126) * (-107 + 13 * Nu) * M_PI + 504 * (-107 + 13 * Nu) * np.log(2) + - 189 * (-107 + 13 * Nu) * np.log(x)) + 84672 * Nu * np.log(x0))))) + 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 ((params.x5 * (276756480 * EulerGamma * (11449 + 19105 * Nu) - 12 * (846557506853 + 1008017482431 * Nu) + 35 * (28 * params.eta2 * (5385456111 + 5 * Nu * (-163158374 + 26251249 * Nu)) - complex(0, 3953664) * (11449 + 109657 * Nu) * M_PI - 135135 * (54784 + 5 * Nu * (1951 + 6560 * Nu)) * M_PI2) + 138378240 * (11449 + 19105 * Nu) * np.log(16 * x))) / 7.62810048e10) + # 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) - -def hl_2_m_2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, - PhiDOT: float, R: float, vpnorder: int, S1z: float, - S2z: float, x: float, params: KeplerVars) -> complex: - - if vpnorder < 0 or vpnorder > 8: - raise ValueError("Error in hl_2_m_2: Input PN order parameter should be between [0, 8].") - - else: - # Calculate the leading amplitude coefficient - amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms - waveform_modes = ( - hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + - hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) - ) - - # cpolar(r, theta) in C is equivalent to rect(r, theta) in Python - phase_factor = rect(1.0, -2 * Phi) - - return amplitude * waveform_modes * phase_factor - -def hl_2_m_min2(mass: float, Nu: float, r: float, rDOT: float, Phi: float, - PhiDOT: float, R: float, vpnorder: int, S1z: float, - S2z: float, x: float, params: KeplerVars) -> complex: - - if vpnorder < 0 or vpnorder > 8: - raise ValueError("Error in hl_2_m_min2: Input PN order parameter should be between [0, 8].") - - else: - # Calculate the leading amplitude coefficient - amplitude = (4 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Sum the GO and QC terms, then apply the complex conjugate for the m = -2 mode - waveform_modes = ( - hGO_2_m_2(mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + - hQC_2_m_2(mass, Nu, vpnorder, x, S1z, S2z, params) - ).conjugate() - - # Calculate the phase factor with positive 2 * Phi - phase_factor = rect(1.0, 2 * Phi) - - return amplitude * waveform_modes * phase_factor # H21 +@register_hgo_lm(2, 1) def hGO_2_m_1( - mass: float, - Nu: float, + total_mass: float, + eta: float, r: float, rDOT: float, PhiDOT: float, @@ -733,155 +707,185 @@ def hGO_2_m_1( S1z: float, S2z: float, x: float, - params: KeplerVars, + params: CommonVars ) -> complex: - delta = np.sqrt(1 - 4 * Nu) + delta = params.delta kappa1 = 1.0 kappa2 = 1.0 - r0 = 2 * mass / np.exp(0.5) + # 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 * mass * PhiDOT + return 0.6666666666666666j * delta * total_mass * PhiDOT elif vpnorder == 2: return ( - (-0.5j * params.Mtot2 * + (-0.5j * total_mass2 * ((1 + delta) * S1z + (-1 + delta) * S2z)) - / params.r2 + / r2 ) elif vpnorder == 3: return ( - (0.023809523809523808j * delta * mass * PhiDOT * - (4 * mass * (-9 + 11 * Nu) + - r * ((19 - 24 * Nu) * params.PhiDOT2 * params.r2 + - 2j * (83 + 2 * Nu) * PhiDOT * r * rDOT + - 2 * (-33 + 10 * Nu) * params.rDOT2))) + (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 * params.Mtot2 * - (2 * mass * - ((77 + 59 * Nu + 11 * delta * (7 + Nu)) * S1z + - (-77 - 59 * Nu + 11 * delta * (7 + Nu)) * S2z) + + (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) * Nu * S1z + - 147 * (-1 + delta) * S2z + (83 + 13 * delta) * Nu * S2z) + - params.rDOT2 * - ((105 * (1 + delta) - 4 * (13 + 15 * delta) * Nu) * S1z + - (-105 + 15 * delta * (7 - 4 * Nu) + 52 * Nu) * S2z) + - 4 * params.PhiDOT2 * params.r2 * - ((-21 - 21 * delta + 66 * Nu + 4 * delta * Nu) * S1z + - (21 - 21 * delta - 66 * Nu + 4 * delta * Nu) * S2z)))) - / params.r3 + (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 * mass * PhiDOT * - (10 * params.Mtot2 * (31 - 205 * Nu + 111 * params.eta2) - - 2 * mass * r * - ((-197 + 5 * Nu + 660 * params.eta2) * params.PhiDOT2 * params.r2 + - 1j * (-3167 - 5278 * Nu + 201 * params.eta2) * PhiDOT * r * rDOT + - 8 * (202 + 587 * Nu - 177 * params.eta2) * params.rDOT2) + - 3 * params.r2 * - ((152 - 692 * Nu + 333 * params.eta2) * params.PhiDOT4 * params.r4 + - 2j * (308 - 1607 * Nu + 111 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT - - 3 * (75 - 560 * Nu + 68 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 - - 2j * (-265 + 526 * Nu + 18 * params.eta2) * PhiDOT * r * params.rDOT3 + - (-241 + 550 * Nu - 264 * params.eta2) * params.rDOT4))) - / params.r2 + (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 = ( - (params.Mtot3 * + (total_mass3 * (-4 * rDOT * - (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + - kappa2 * (-1 + delta + 2 * Nu) * params.S2z2) - + (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) * Nu) * params.S1z2 - - 12 * delta * Nu * S1z * S2z + - (9 + kappa2 - 2 * (9 + 4 * kappa2) * Nu + - delta * (-9 - kappa2 + 6 * kappa2 * Nu)) * params.S2z2))) - / (6.0 * params.r3) + 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 * params.Mtot2 * Nu * PhiDOT * - (mass * (195 * PhiDOT * r - 946j * rDOT) + + (delta * total_mass2 * eta * PhiDOT * + (total_mass * (195 * PhiDOT * r - 946j * rDOT) + 9 * r * - (270 * params.PhiDOT3 * params.r3 - - 483j * params.PhiDOT2 * params.r2 * rDOT - - 580 * PhiDOT * r * params.rDOT2 + - 42j * params.rDOT3))) - / (315.0 * params.r2) + (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 * params.Mtot2 * - (3 * params.r2 * - (4j * PhiDOT * r * params.rDOT3 * - ((-315 * (1 + delta) + 2 * (251 + 463 * delta) * Nu + - 4 * (15 + delta) * params.eta2) * S1z + - (315 - 315 * delta - 502 * Nu + 926 * delta * Nu + - 4 * (-15 + delta) * params.eta2) * S2z) + - 12 * params.PhiDOT2 * params.r2 * params.rDOT2 * - ((189 * (1 + delta) - 2 * (521 + 293 * delta) * Nu + - 7 * (55 + 23 * delta) * params.eta2) * S1z + - (189 * (-1 + delta) + 2 * (521 - 293 * delta) * Nu + - 7 * (-55 + 23 * delta) * params.eta2) * S2z) + - params.rDOT4 * - ((567 * (1 + delta) - 16 * (77 + 64 * delta) * Nu + - 8 * (177 + 173 * delta) * params.eta2) * S1z + - (567 * (-1 + delta) + 16 * (77 - 64 * delta) * Nu + - 8 * (-177 + 173 * delta) * params.eta2) * S2z) - - 4j * params.PhiDOT3 * params.r3 * rDOT * - ((936 * (1 + delta) - 5 * (979 + 215 * delta) * Nu + - 2 * (1353 + 293 * delta) * params.eta2) * S1z + - (936 * (-1 + delta) + 5 * (979 - 215 * delta) * Nu + - 2 * (-1353 + 293 * delta) * params.eta2) * S2z) + - 4 * params.PhiDOT4 * params.r4 * - ((-252 * (1 + delta) + (1315 + 857 * delta) * Nu + - 4 * (-285 + 43 * delta) * params.eta2) * S1z + - (252 + 5 * Nu * (-263 + 228 * Nu) + - delta * (-252 + Nu * (857 + 172 * Nu))) * S2z)) - - 2 * mass * r * + 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) * Nu + - (10635 + 139 * delta) * params.eta2) * S1z + - (2043 * (-1 + delta) + (-37 + 2597 * delta) * Nu + - (-10635 + 139 * delta) * params.eta2) * S2z) + - params.PhiDOT2 * params.r2 * - ((-765 - Nu * (667 + 7773 * Nu) + - delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S1z + - (765 + Nu * (667 + 7773 * Nu) + - delta * (-765 + 7 * Nu * (-533 + 245 * Nu))) * S2z) + - 4 * params.rDOT2 * - ((-234 * (1 + delta) - 4 * (560 + 901 * delta) * Nu + - (483 + 1111 * delta) * params.eta2) * S1z + - (234 + 7 * (320 - 69 * Nu) * Nu + - delta * (-234 + Nu * (-3604 + 1111 * Nu))) * S2z)) + - 2 * params.Mtot2 * - (1134 * kappa1 * (-1 - delta + (3 + delta) * Nu) * params.S1z3 + - 1134 * (1 + delta) * (-2 + kappa1) * Nu * params.S1z2 * S2z + + ((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 * Nu - 9172 * delta * Nu + - 231 * params.eta2 + 775 * delta * params.eta2 + - 1134 * (-1 + delta) * (-2 + kappa2) * Nu * params.S2z2) + - S2z * (5661 - 5661 * delta + 17156 * Nu - 9172 * delta * Nu - - 231 * params.eta2 + 775 * delta * params.eta2 + - 1134 * kappa2 * (1 - delta + (-3 + delta) * Nu) * - params.S2z2))) - ) / params.r4 + (-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 @@ -889,323 +893,260 @@ def hGO_2_m_1( elif vpnorder == 7: spin_terms = ( - -23760 * params.Mtot3 * - (params.PhiDOT3 * params.r4 * - (-12j * (-35 + 107 * Nu) * S1z + - 5 * (126 - 462 * Nu + 80 * params.eta2 + - kappa1 * (-2 - 79 * Nu + 153 * params.eta2)) * params.S1z2 + - S2z * (-420j + 1284j * Nu + + -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) * Nu * S2z - - 5 * (80 + 153 * kappa2) * params.eta2 * S2z)) - - 1j * params.PhiDOT2 * params.r3 * rDOT * - (-24j * (-35 + 202 * Nu) * S1z + - 5 * (-14 * Nu * (3 + 58 * Nu) + - kappa1 * (-409 + 1096 * Nu + 6 * params.eta2)) * params.S1z2 + + 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) * params.eta2 * S2z + - Nu * (4848j + 210 * S2z - 5480 * kappa2 * S2z))) - - 4j * r * params.rDOT3 * - (3j * (26 + 57 * Nu) * S1z + - 5 * (4 * params.eta2 + - kappa1 * (-7 + 49 * Nu + 15 * params.eta2)) * params.S1z2 - + 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) * params.eta2 * S2z + - Nu * (171j + 245 * kappa2 * S2z))) + - 2j * mass * rDOT * - (-4j * (-78 + 769 * Nu) * S1z + - 5 * ((14 - 59 * Nu) * Nu + - kappa1 * (-70 - 77 * Nu + 18 * params.eta2)) * params.S1z2 + + 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) * params.eta2 * S2z + - Nu * (3076j - 70 * S2z + 385 * kappa2 * S2z))) + - PhiDOT * params.r2 * params.rDOT2 * - (6j * (-62 + 219 * Nu) * S1z - - 5 * (189 - 756 * Nu + 388 * params.eta2 + - kappa1 * (98 - 266 * Nu + 276 * params.eta2)) * params.S1z2 + + 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) * params.eta2 * S2z - - 2 * Nu * (657j + 35 * (54 + 19 * kappa2) * S2z))) + - mass * PhiDOT * r * - (8j * (-61 + 480 * Nu) * S1z + - 5 * (-392 + 448 * Nu + 474 * params.eta2 + - kappa1 * (-11 + 150 * Nu + 58 * params.eta2)) * params.S1z2 - + 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) * params.eta2 * S2z + - 10 * Nu * (384j + (224 + 75 * kappa2) * S2z)))) + 10 * (237 + 29 * kappa2) * eta2 * S2z + + 10 * eta * (384j + (224 + 75 * kappa2) * S2z)))) ) orbital_terms = ( - delta * mass * - (-240 * mass * PhiDOT * params.r3 * - ((197936 - 139360 * Nu - 367105 * params.eta2 + - 253245 * params.eta3) * params.PhiDOT4 * params.r4 + - 1j * (279236 - 483940 * Nu - 2817805 * params.eta2 + - 459180 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT - - 6 * (38627 + 89295 * Nu - 492740 * params.eta2 + - 75975 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 - - 1j * (-731008 + 2287930 * Nu + 981060 * params.eta2 + - 10275 * params.eta3) * PhiDOT * r * params.rDOT3 + - (-327667 + 436705 * Nu + 659790 * params.eta2 - - 438255 * params.eta3) * params.rDOT4) + - 900 * PhiDOT * params.r4 * - (2 * (-2594 + 27609 * Nu - 74032 * params.eta2 + - 25974 * params.eta3) * params.PhiDOT6 * params.r6 + - 4j * (-5730 + 58833 * Nu - 137842 * params.eta2 + - 17123 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + - 2 * (-114 - 41622 * Nu + 147569 * params.eta2 + - 4196 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + - 4j * (-9554 + 70788 * Nu - 156227 * params.eta2 + - 5810 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + - (17619 - 138450 * Nu + 322600 * params.eta2 - - 80816 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - - 2j * (8793 - 52230 * Nu + 69340 * params.eta2 + - 2536 * params.eta3) * PhiDOT * r * params.rDOT5 + - 2 * (3957 - 24534 * Nu + 42584 * params.eta2 - - 20800 * params.eta3) * params.rDOT6) - - 2 * params.Mtot3 * + 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 * (Nu * (-14 + 31 * Nu) + 7 * kappa1 * (10 + 31 * Nu)) * params.S1z2 + - 2 * S1z * (-156j + 155 * params.eta2 * S2z + - 2 * Nu * (613j + 390 * S2z)) + + (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 * params.eta2 * S2z + - Nu * (2452j + 35 * (-2 + 31 * kappa2) * S2z))) + + 155 * eta2 * S2z + + eta * (2452j + 35 * (-2 + 31 * kappa2) * S2z))) + PhiDOT * r * - (8946400 * params.eta3 - + (8946400 * eta3 - 8 * (6991786 + 724680j * S1z + - 7425 * (392 + 11 * kappa1) * params.S1z2 + + 7425 * (392 + 11 * kappa1) * S1z2 + 724680j * S2z + - 7425 * (392 + 11 * kappa2) * params.S2z2) - - 3600 * params.eta2 * - (-628 + 33 * (-19 + 92 * kappa1) * params.S1z2 - + 7425 * (392 + 11 * kappa2) * S2z2) - + 3600 * eta2 * + (-628 + 33 * (-19 + 92 * kappa1) * S1z2 - 7326 * S1z * S2z + - 33 * (-19 + 92 * kappa2) * params.S2z2) + - 15 * Nu * + 33 * (-19 + 92 * kappa2) * S2z2) + + 15 * eta * (994455 * M_PI2 + 8 * (-2249485 + - 7920 * (-21 + 8 * kappa1) * params.S1z2 + + 7920 * (-21 + 8 * kappa1) * S1z2 + 283536j * S2z + - 7920 * (-21 + 8 * kappa2) * params.S2z2 - + 7920 * (-21 + 8 * kappa2) * S2z2 - 1584 * S1z * (-179j + 170 * S2z))))) + - 3 * params.Mtot2 * r * - (31680j * params.rDOT3 * - (5 * (4 * params.eta2 + 7 * kappa1 * (-1 + 5 * Nu)) * params.S1z2 + - S1z * (78j + 40 * params.eta2 * S2z + - Nu * (327j + 420 * 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 * params.eta2 * S2z + - Nu * (327j + 175 * kappa2 * S2z))) - - 22 * PhiDOT * r * params.rDOT2 * - (2553200 * params.eta3 - + 20 * eta2 * S2z + + eta * (327j + 175 * kappa2 * S2z))) - + 22 * PhiDOT * r * rDOT2 * + (2553200 * eta3 - 24 * (268267 + 5580j * S1z + - 525 * (27 + 14 * kappa1) * params.S1z2 + + 525 * (27 + 14 * kappa1) * S1z2 + 5580j * S2z + - 525 * (27 + 14 * kappa2) * params.S2z2) - - 200 * params.eta2 * - (39445 + 72 * (-4 + 21 * kappa1) * params.S1z2 - + 525 * (27 + 14 * kappa2) * S2z2) - + 200 * eta2 * + (39445 + 72 * (-4 + 21 * kappa1) * S1z2 - 3600 * S1z * S2z + - 72 * (-4 + 21 * kappa2) * params.S2z2) + - 25 * Nu * + 72 * (-4 + 21 * kappa2) * S2z2) + + 25 * eta * (23247 * M_PI2 + 8 * (-69259 + 1026j * S1z + - 126 * (27 + 5 * kappa1) * params.S1z2 + + 126 * (27 + 5 * kappa1) * S1z2 + 1026j * S2z + - 126 * (27 + 5 * kappa2) * params.S2z2))) + - params.PhiDOT3 * params.r3 * - (10071200 * params.eta3 + + 126 * (27 + 5 * kappa2) * S2z2))) + + PhiDOT3 * r3 * + (10071200 * eta3 + 96 * (-421183 - 34650j * S1z + - 825 * (-63 + kappa1) * params.S1z2 - + 825 * (-63 + kappa1) * S1z2 - 34650j * S2z + - 825 * (-63 + kappa2) * params.S2z2) - - 400 * params.eta2 * - (64177 + 792 * (-5 + 6 * kappa1) * params.S1z2 - + 825 * (-63 + kappa2) * S2z2) - + 400 * eta2 * + (64177 + 792 * (-5 + 6 * kappa1) * S1z2 - 17424 * S1z * S2z + - 792 * (-5 + 6 * kappa2) * params.S2z2) + - 15 * Nu * + 792 * (-5 + 6 * kappa2) * S2z2) + + 15 * eta * (426195 * M_PI2 + 8 * (-509635 + - 330 * (210 + 83 * kappa1) * params.S1z2 + + 330 * (210 + 83 * kappa1) * S1z2 + 29304j * S2z + - 330 * (210 + 83 * kappa2) * params.S2z2 - + 330 * (210 + 83 * kappa2) * S2z2 - 792 * S1z * (-37j + 70 * S2z)))) - - 2j * params.PhiDOT2 * params.r2 * rDOT * - (-8330400 * params.eta3 + + 2j * PhiDOT2 * r2 * rDOT * + (-8330400 * eta3 + 8 * (-2810116 - 415800j * S1z + - 1012275 * kappa1 * params.S1z2 - + 1012275 * kappa1 * S1z2 - 415800j * S2z + - 1012275 * kappa2 * params.S2z2) + - 4800 * params.eta2 * - (13411 + 33 * (19 + 12 * kappa1) * params.S1z2 + + 1012275 * kappa2 * S2z2) + + 4800 * eta2 * + (13411 + 33 * (19 + 12 * kappa1) * S1z2 + 462 * S1z * S2z + - 33 * (19 + 12 * kappa2) * params.S2z2) + - 5 * Nu * + 33 * (19 + 12 * kappa2) * S2z2) + + 5 * eta * (1278585 * M_PI2 - 8 * (5139685 + - 990 * (-21 + 139 * kappa1) * params.S1z2 - + 990 * (-21 + 139 * kappa1) * S1z2 - 313632j * S2z + - 990 * (-21 + 139 * kappa2) * params.S2z2 - + 990 * (-21 + 139 * kappa2) * S2z2 - 3564 * S1z * (88j + 185 * S2z)))))) ) log_term = ( - -13559040 * delta * params.Mtot3 * PhiDOT * r * - (2 * mass - 3 * params.PhiDOT2 * params.r3 + - 6j * PhiDOT * params.r2 * rDOT + - 6 * r * params.rDOT2) * + -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) - ) / params.r4 + ) / r4 else: - return 0.0 + 0.0j - + return complex(0.0, 0.0) + +@register_hqc_lm(2, 1) def hQC_2_m_1( - mass: float, - Nu: float, + total_mass: float, + eta: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: KeplerVars + params:CommonVars ) -> complex: - delta: float = np.sqrt(1.0 - 4.0 * Nu) + # delta: float = np.sqrt(1.0 - 4.0 * eta) EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - r0: float = b0 + # 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 + np.log(64.0) + 6.0 * np.log(b0) + 9.0 * np.log(x)) + 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 * params.x3 * log_term_base) / 9.0 + 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 * params.x3p5 * log_term_base) / 6.0 + return (spin_factor * x3p5 * log_term_base) / 6.0 elif vpnorder == 6: - return -0.007936507936507936 * (delta * (-17.0 + 6.0 * Nu) * params.x4 * log_term_base) + return -0.007936507936507936 * (delta * (-17.0 + 6.0 * eta) * x4 * log_term_base) elif vpnorder == 7: # Heavily nested 3.5PN (order 7) term - term1 = (params.x4p5 * ( - -98 * S1z + 84 * EulerGamma * S1z + 3017 * Nu * S1z - 2586 * EulerGamma * Nu * S1z - - 42j * M_PI * S1z + 1293j * Nu * M_PI * S1z + 98 * S2z - 84 * EulerGamma * S2z - - 3017 * Nu * S2z + 2586 * EulerGamma * Nu * S2z + 42j * M_PI * S2z - 1293j * Nu * M_PI * S2z + - 84 * S1z * np.log(2) - 2586 * Nu * S1z * np.log(2) - 84 * S2z * np.log(2) + 2586 * Nu * S2z * np.log(2) + - 84 * S1z * np.log(b0) - 2586 * Nu * S1z * np.log(b0) - 84 * S2z * np.log(b0) + 2586 * Nu * S2z * np.log(b0) + - 126 * S1z * np.log(x) - 3879 * Nu * S1z * np.log(x) - 126 * S2z * np.log(x) + 3879 * Nu * S2z * np.log(x) + + 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 * Nu * S1z) / 12.0 - (29 * EulerGamma * Nu * S1z) / 14.0 - - 0.16666666666666666j * M_PI * S1z + 1.0357142857142858j * Nu * M_PI * S1z - - (7 * S2z) / 18.0 + (EulerGamma * S2z) / 3.0 + (29 * Nu * S2z) / 12.0 - (29 * EulerGamma * Nu * S2z) / 14.0 - - 0.16666666666666666j * M_PI * S2z + 1.0357142857142858j * Nu * M_PI * S2z + - 1.7523809523809524j * np.log(2) - 2.6666666666666665j * EulerGamma * np.log(2) - - (4 * M_PI * np.log(2)) / 3.0 + (S1z * np.log(2)) / 3.0 - (29 * Nu * S1z * np.log(2)) / 14.0 + - (S2z * np.log(2)) / 3.0 - (29 * Nu * S2z * np.log(2)) / 14.0 - 1.3333333333333333j * np.log(2)**2 - - 1.3333333333333333j * np.log(b0)**2 - 1.3587301587301588j * np.log(r0) + - (np.log(b0) * (392j - 336j * EulerGamma - 168 * M_PI + 42 * S1z - 261 * Nu * S1z + 42 * S2z - 261 * Nu * S2z - 336j * np.log(2) - 504j * np.log(x))) / 126.0 + - 2.6285714285714286j * np.log(x) - 4j * EulerGamma * np.log(x) - 2 * M_PI * np.log(x) + - (S1z * np.log(x)) / 2.0 - (87 * Nu * S1z * np.log(x)) / 28.0 + (S2z * np.log(x)) / 2.0 - - (87 * Nu * S2z * np.log(x)) / 28.0 - 4j * np.log(2) * np.log(x) - 3j * np.log(x)**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 0.0 + 0.0j + return complex(0.0, 0.0) -def hl_2_m_1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_2_m_1: Input PN order parameter should be between [0, 8]." - ) - - # Calculate the pre-factor: (4 * M * eta * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - go_component: complex = hGO_2_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_2_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - phase_factor: complex = np.exp(-1j * Phi) - - # Combine terms - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_2_m_min1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_2_m_min1: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(M_PI / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # These are the same functions used for the m=1 mode - go_component: complex = hGO_2_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_2_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # In C: conj(hGO + hQC) - # In Python: use the .conjugate() method or np.conj() - amplitude_term: complex = (go_component + qc_component).conjugate() - - # In C: cpolar(1, 1 * Phi) -> exp(i * Phi) - # Note the sign change from the m=1 mode - phase_factor: complex = np.exp(1j * Phi) - - return pre_factor * amplitude_term * phase_factor +############ l = 3 ############### # H33 +@register_hgo_lm(3, 3) def hGO_3_m_3( - mass: float, - Nu: float, + total_mass: float, + eta: float, r: float, rDOT: float, PhiDOT: float, @@ -1213,12 +1154,13 @@ def hGO_3_m_3( S1z: float, S2z: float, x: float, - params: KeplerVars, + params:CommonVars ) -> complex: - delta = np.sqrt(1 - 4 * Nu) kappa1 = 1.0 kappa2 = 1.0 - r0 = 2 * mass / np.exp(0.5) + r0 = params.r0 + + delta = params.delta combination_a = 1j * PhiDOT * r - rDOT combination_a3 = combination_a ** 3 @@ -1232,154 +1174,186 @@ def hGO_3_m_3( combination_c2 = combination_c ** 2 combination_d = -1j * PhiDOT * r + rDOT - combination_d2 = combination_d ** 2 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 + - mass * (-7j * PhiDOT * r + 4 * rDOT))) + total_mass * (-7j * PhiDOT * r + 4 * rDOT))) / (2.0 * r) ) elif vpnorder == 3: return ( (np.sqrt(0.11904761904761904) * delta * - (6 * (-5 + 19 * Nu) * params.r2 * combination_b4 * + (6 * (-5 + 19 * eta) * r2 * combination_b4 * (1j * PhiDOT * r + rDOT) + - 2 * params.Mtot2 * - (-3j * (-101 + 43 * Nu) * PhiDOT * r + - (-109 + 86 * Nu) * rDOT) + - 3 * mass * r * - (-12j * (1 + 4 * Nu) * params.PhiDOT3 * params.r3 + - 6 * (14 + 31 * Nu) * params.PhiDOT2 * params.r2 * rDOT + - 3j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 - - 4 * (8 + 17 * Nu) * params.rDOT3))) - / (36.0 * params.r2) + 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) * params.Mtot2 * - (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) + - r * (2 * params.rDOT2 * - (6 * (1 + delta) * S1z - 5 * (5 + 3 * delta) * Nu * S1z + - (-6 + delta * (6 - 15 * Nu) + 25 * Nu) * S2z) + - params.PhiDOT2 * params.r2 * - (-24 * (1 + delta) * S1z + (119 + 33 * delta) * Nu * S1z + - (24 - 119 * Nu + 3 * delta * (-8 + 11 * Nu)) * S2z) + + (-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) * Nu * S1z + - (18 - 77 * Nu + 3 * delta * (-6 + 13 * Nu)) * S2z)))) - / params.r3 + (-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 * Nu + 3387 * params.eta2) * params.r3 * + (30 * (183 - 1579 * eta + 3387 * eta2) * r3 * combination_c2 * combination_d5 + - 10 * params.Mtot3 * - (-1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r + - 4 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + - 2 * params.Mtot2 * r * - (-11j * (-5353 - 13493 * Nu + 4671 * params.eta2) * params.PhiDOT3 * params.r3 + - (-75243 - 142713 * Nu + 192821 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT + - 220j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 - - 10 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + - 3 * mass * params.r2 * - (2j * (-7633 + 9137 * Nu + 28911 * params.eta2) * params.PhiDOT5 * params.r5 - - 4 * (-8149 + 1576 * Nu + 43533 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT - - 2j * (-9297 - 19517 * Nu + 64839 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - - 32 * (-1288 + 3667 * Nu + 4056 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 - - 5j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 + - 20 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) - / (1584.0 * np.sqrt(210) * params.r3) + 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) * params.Mtot3 * + 0.125j * np.sqrt(1.0714285714285714) * total_mass3 * (7 * PhiDOT * r + 2j * rDOT) * - (kappa1 * (-1 - delta + 2 * (2 + delta) * Nu) * params.S1z2 + - S2z * (-4 * delta * Nu * S1z + - kappa2 * (1 - delta + 2 * (-2 + delta) * Nu) * S2z)) - / params.r3 + (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 * params.Mtot2 * Nu * - (668 * params.Mtot2 + - 2 * mass * r * - (4081 * params.PhiDOT2 * params.r2 + - 297j * PhiDOT * r * rDOT - 452 * params.rDOT2) + - 5 * params.r2 * - (1329 * params.PhiDOT4 * params.r4 - - 2926j * params.PhiDOT3 * params.r3 * rDOT - - 384 * params.PhiDOT2 * params.r2 * params.rDOT2 - - 408j * PhiDOT * r * params.rDOT3 + - 200 * params.rDOT4))) - / (36.0 * np.sqrt(210) * params.r4) + -(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 * params.Mtot2 * - (10 * params.Mtot2 * - ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + - 8 * (12 + 47 * delta) * params.eta2) * S1z + - (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + - 8 * (-12 + 47 * delta) * params.eta2) * S2z) + - 2 * mass * r * - (2 * params.PhiDOT2 * params.r2 * - ((1320 * (1 + delta) - 2 * (4469 + 211 * delta) * Nu + - (8709 + 2777 * delta) * params.eta2) * S1z + - (1320 * (-1 + delta) + 8938 * Nu - 422 * delta * Nu + - (-8709 + 2777 * delta) * params.eta2) * S2z) + + -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) * Nu + - (8911 + 5273 * delta) * params.eta2) * S1z + - (2000 * (-1 + delta) + (9147 - 3173 * delta) * Nu + - (-8911 + 5273 * delta) * params.eta2) * S2z) + - 10 * params.rDOT2 * - ((-105 * (1 + delta) + (541 + 77 * delta) * Nu - - 2 * (462 + 247 * delta) * params.eta2) * S1z + - (105 + Nu * (-541 + 924 * Nu) + - delta * (-105 + (77 - 494 * Nu) * Nu)) * S2z)) - - 3 * params.r2 * - (-3 * params.PhiDOT2 * params.r2 * params.rDOT2 * - ((480 * (1 + delta) - (1711 + 1889 * delta) * Nu + - 2 * (-1161 + 757 * delta) * params.eta2) * S1z + - (480 * (-1 + delta) + (1711 - 1889 * delta) * Nu + - 2 * (1161 + 757 * delta) * params.eta2) * S2z) + - 2 * params.PhiDOT4 * params.r4 * - ((350 * (1 + delta) - 4 * (404 + 461 * delta) * Nu + - (883 + 769 * delta) * params.eta2) * S1z + - (350 * (-1 + delta) + 4 * (404 - 461 * delta) * Nu + - (-883 + 769 * delta) * params.eta2) * S2z) + - 2j * params.PhiDOT3 * params.r3 * rDOT * - ((660 * (1 + delta) - (4061 + 2899 * delta) * Nu + - (2643 + 4789 * delta) * params.eta2) * S1z + - (660 * (-1 + delta) + (4061 - 2899 * delta) * Nu + - (-2643 + 4789 * delta) * params.eta2) * S2z) + - 10 * params.rDOT4 * - ((-30 * (1 + delta) + (187 + 101 * delta) * Nu - - 2 * (159 + 61 * delta) * params.eta2) * S1z + - (30 + Nu * (-187 + 318 * Nu) + - delta * (-30 + (101 - 122 * Nu) * Nu)) * S2z) + - 2j * PhiDOT * r * params.rDOT3 * - ((90 + Nu * (-1321 + 5118 * Nu) + - delta * (90 + Nu * (-319 + 714 * Nu))) * S1z + - (-90 + (1321 - 5118 * Nu) * Nu + - delta * (90 + Nu * (-319 + 714 * Nu))) * S2z))) - / (np.sqrt(210) * params.r4) + ((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 @@ -1388,373 +1362,303 @@ def hGO_3_m_3( M_PI2 = M_PI ** 2 spin_terms = ( - 504504 * params.Mtot3 * - (2 * mass * + 504504 * total_mass3 * + (2 * total_mass * (rDOT * - (S1z * (108 - 498 * Nu + - 5j * (-24 - 5 * kappa1 + 3 * (76 + kappa1) * Nu + - 4 * (-111 + 13 * kappa1) * params.eta2) * S1z) + - 6 * (-18 + 83 * Nu) * S2z - - 5j * (-24 - 5 * kappa2 + 3 * (76 + kappa2) * Nu + - 4 * (-111 + 13 * kappa2) * params.eta2) * params.S2z2) + + (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 * Nu) + - 5 * (-24 + 399 * kappa1 + 48 * (7 - 19 * Nu) * Nu + - kappa1 * Nu * (-1629 + 188 * Nu)) * S1z) + - 3j * (-99 + 581 * Nu) * S2z - - 5 * (-24 + 399 * kappa2 + 48 * (7 - 19 * Nu) * Nu + - kappa2 * Nu * (-1629 + 188 * Nu)) * params.S2z2)) + - r * (3 * PhiDOT * r * params.rDOT2 * + (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) * params.eta2 * S1z - - 30 * Nu * (50j + 20 * S1z + 73 * kappa1 * S1z)) + - 12j * (-18 + 125 * Nu) * S2z - - 5 * (109 * kappa2 - 6 * (20 + 73 * kappa2) * Nu + - 8 * (45 + 8 * kappa2) * params.eta2) * params.S2z2) + - 2 * params.rDOT3 * + 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 * Nu * (11j + 2 * Nu * S1z + - kappa1 * (17 + 8 * Nu) * S1z)) + - 6 * (9 - 55 * Nu) * S2z + - 5j * (12 * params.eta2 + - kappa2 * (-29 + 6 * Nu * (17 + 8 * Nu))) * params.S2z2) + - 6 * params.PhiDOT2 * params.r2 * rDOT * - (S1z * (297 - 465 * Nu + - 5j * (6 * (20 - 87 * Nu) * Nu + - kappa1 * (-50 + 3 * Nu * (47 + 76 * Nu))) * S1z) + - 3 * (-99 + 155 * Nu) * S2z - - 5j * (6 * (20 - 87 * Nu) * Nu + - kappa2 * (-50 + 3 * Nu * (47 + 76 * Nu))) * params.S2z2) + - params.PhiDOT3 * params.r3 * - (3j * (531 - 1295 * Nu) * S1z + - 10 * (-33 * kappa1 + 6 * (30 + 13 * kappa1) * Nu + - 4 * (-96 + 67 * kappa1) * params.eta2) * params.S1z2 + + 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 * Nu * (777j - - 4 * (90 + 39 * kappa2 - 192 * Nu + - 134 * kappa2 * Nu) * S2z))))) + 5 * eta * (777j - + 4 * (90 + 39 * kappa2 - 192 * eta + + 134 * kappa2 * eta) * S2z))))) ) orbital_terms = ( delta * - (-17640 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * - params.r4 * combination_b6 * combination_e3 + - 168 * params.Mtot2 * params.r2 * - (1j * (-7508635 + 7 * Nu * (-1318438 + Nu * (-10231834 + 9667755 * Nu))) * - params.PhiDOT5 * params.r5 + - 7 * (1235591 + Nu * (884445 + (23935218 - 26913443 * Nu) * Nu)) * - params.PhiDOT4 * params.r4 * rDOT - - 1j * (8961149 + 7 * Nu * (-31755709 + Nu * (-11134798 + 22187331 * Nu))) * - params.PhiDOT3 * params.r3 * params.rDOT2 - - (-36806435 + 7 * Nu * (33178545 + Nu * (24565078 + 22873537 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT3 - - 5j * (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * - PhiDOT * r * params.rDOT4 + - 5 * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * - params.rDOT5) + - 1764 * mass * params.r3 * - (-2j * (239087 + Nu * (-1206515 + Nu * (422631 + 3979375 * Nu))) * - params.PhiDOT7 * params.r7 + - 2 * (621284 + Nu * (-2279907 + 2 * Nu * (-1180187 + 5876531 * Nu))) * - params.PhiDOT6 * params.r6 * rDOT + - 2j * (39270 + Nu * (1235486 - 5319747 * Nu + 4406349 * params.eta2)) * - params.PhiDOT5 * params.r5 * params.rDOT2 + - 8 * (349111 + 4 * Nu * (-519370 + 33 * Nu * (10939 + 42635 * Nu))) * - params.PhiDOT4 * params.r4 * params.rDOT3 + - 2j * (1212607 + 3 * Nu * (-2012698 - 67827 * Nu + 7955628 * params.eta2)) * - params.PhiDOT3 * params.r3 * params.rDOT4 + - 4 * (201135 + 2 * Nu * (-773107 + Nu * (1214819 + 1157652 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT5 + - 5j * (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * - PhiDOT * r * params.rDOT6 - - 40 * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * - params.rDOT7) + - 2 * params.Mtot4 * + (-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 * params.eta3 + + (269279500 * eta3 + 2 * (-174108226 + 63063 * S1z * (108j + 5 * (24 + 5 * kappa1) * S1z) + 63063 * S2z * (108j + 5 * (24 + 5 * kappa2) * S2z)) - - 21 * Nu * + 21 * eta * (103100846 + 1846845 * M_PI2 + 1693692j * S2z - 6006 * (S1z * (-282j + 5 * (-180 + 7 * kappa1) * S1z) - 980 * S1z * S2z + - 5 * (-180 + 7 * kappa2) * params.S2z2)) - - 2940 * params.eta2 * + 5 * (-180 + 7 * kappa2) * S2z2)) - + 2940 * eta2 * (-122855 + - 4719 * ((-6 + 7 * kappa1) * params.S1z2 - + 4719 * ((-6 + 7 * kappa1) * S1z2 - 26 * S1z * S2z + - (-6 + 7 * kappa2) * params.S2z2))) + + (-6 + 7 * kappa2) * S2z2))) + 1j * PhiDOT * r * - (-1176172480 * params.eta3 + + (-1176172480 * eta3 + 8 * (-74084729 + 189189 * S1z * (99j + 5 * (-8 + 133 * kappa1) * S1z) + 189189 * S2z * (99j + 5 * (-8 + 133 * kappa2) * S2z)) + - 35280 * params.eta2 * + 35280 * eta2 * (56255 + - 429 * ((-22 + 65 * kappa1) * params.S1z2 - + 429 * ((-22 + 65 * kappa1) * S1z2 - 174 * S1z * S2z + - (-22 + 65 * kappa2) * params.S2z2)) - - 147 * Nu * + (-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) * params.S2z2)))) + - params.Mtot3 * r * - (-12j * PhiDOT * r * params.rDOT2 * - (-1035895280 * params.eta3 - + 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 * Nu * + 77 * eta * (42451610 + 1511055 * M_PI2 + 1749384j * S2z + 6552 * (S1z * (267j + 25 * (6 + 11 * kappa1) * S1z) - 5 * S1z * S2z + - 25 * (6 + 11 * kappa2) * params.S2z2)) + - 490 * params.eta2 * + 25 * (6 + 11 * kappa2) * S2z2)) + + 490 * eta2 * (-5802767 + - 5148 * ((-6 + 23 * kappa1) * params.S1z2 - + 5148 * ((-6 + 23 * kappa1) * S1z2 - 58 * S1z * S2z + - (-6 + 23 * kappa2) * params.S2z2))) + - 4 * params.rDOT3 * - (-1359334480 * params.eta3 - + (-6 + 23 * kappa2) * S2z2))) + + 4 * rDOT3 * + (-1359334480 * eta3 - 4 * (-150254558 + 63063 * S1z * (54j + 145 * kappa1 * S1z) + 63063 * S2z * (54j + 145 * kappa2 * S2z)) + - 231 * Nu * + 231 * eta * (8490448 + 503685 * M_PI2 + 242424j * S2z + 2184 * (S1z * (111j + 110 * kappa1 * S1z) + 70 * S1z * S2z + - 110 * kappa2 * params.S2z2)) + - 11760 * params.eta2 * + 110 * kappa2 * S2z2)) + + 11760 * eta2 * (-312980 + - 429 * ((3 + 25 * kappa1) * params.S1z2 - + 429 * ((3 + 25 * kappa1) * S1z2 - 44 * S1z * S2z + - (3 + 25 * kappa2) * params.S2z2))) + - 6 * params.PhiDOT2 * params.r2 * rDOT * - (2368900688 * params.eta3 + + (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 * params.eta2 * + 1176 * eta2 * (2423171 + - 4290 * ((-3 + 41 * kappa1) * params.S1z2 - + 4290 * ((-3 + 41 * kappa1) * S1z2 - 88 * S1z * S2z + - (-3 + 41 * kappa2) * params.S2z2)) + - 539 * Nu * + (-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) * params.S2z2))) + - 1j * params.PhiDOT3 * params.r3 * - (-4538040136 * params.eta3 - + 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 * params.eta2 * + 2352 * eta2 * (7332973 + - 12870 * ((5 + 23 * kappa1) * params.S1z2 - + 12870 * ((5 + 23 * kappa1) * S1z2 - 36 * S1z * S2z + - (5 + 23 * kappa2) * params.S2z2)) + - 21 * Nu * + (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) * params.S2z2)))))) + 40 * (15 + kappa2) * S2z2)))))) ) log_term = ( - 74954880 * delta * params.Mtot3 * - (22j * mass * PhiDOT * r + - 59j * params.PhiDOT3 * params.r4 + 8 * mass * rDOT + - 66 * params.PhiDOT2 * params.r3 * rDOT + - 24j * PhiDOT * params.r2 * params.rDOT2 - - 4 * r * params.rDOT3) * + 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) * params.r4) + / (2.4216192e7 * np.sqrt(210) * r4) ) else: - return 0.0 + 0.0j - + return complex(0, 0) + +@register_hqc_lm(3, 3) def hQC_3_m_3( - mass: float, - Nu: float, + total_mass: float, + eta: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: KeplerVars + params: CommonVars, ) -> complex: - delta: float = np.sqrt(1.0 - 4.0 * Nu) + delta: float = params.delta EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - r0: float = b0 + + 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 * params.x3 * ( + (3.0 * np.sqrt(0.04285714285714286) * delta * x3 * ( -97.0 + 60.0 * EulerGamma - 30.0j * M_PI + - 60.0 * np.log(2.0) + 60.0 * np.log(3.0) + - 60.0 * np.log(b0) + 90.0 * np.log(x) + 60.0 * LOG2 + 60.0 * LOG3 + + 60.0 * logb0 + 90.0 * logx )) / 4.0 ) elif vpnorder == 6: - numerator_6 = (delta * params.x4 * ( - 188568.0 - 116640.0 * EulerGamma - 70537.0 * Nu + 43740.0 * EulerGamma * Nu + - 58320.0j * M_PI - 21870.0j * Nu * M_PI - - 116640.0 * np.log(2.0) + 43740.0 * Nu * np.log(2.0) - - 116640.0 * np.log(3.0) + 43740.0 * Nu * np.log(3.0) + - 14580.0 * (-8.0 + 3.0 * Nu) * np.log(b0) + - 21870.0 * (-8.0 + 3.0 * Nu) * np.log(x) + 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 * np.log(b0) * ( + 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 * Nu + 5.0 * delta * Nu) * S1z + - 20.0 * S2z - 20.0 * delta * S2z - 95.0 * Nu * S2z + 25.0 * delta * Nu * S2z + - 120.0j * delta * np.log(2.0) + 120.0j * delta * np.log(3.0) + - 180.0j * delta * np.log(x) + 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 = (params.x4p5 * ( + 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 * Nu * S1z - 643223.0 * delta * Nu * S1z + 1508220.0 * EulerGamma * Nu * S1z + - 396900.0 * delta * EulerGamma * Nu * S1z + 158760.0j * M_PI * S1z + 158760.0j * delta * M_PI * S1z - - 754110.0j * Nu * M_PI * S1z - 198450.0j * delta * Nu * M_PI * 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 * Nu * S2z - 643223.0 * delta * Nu * S2z - - 1508220.0 * EulerGamma * Nu * S2z + 396900.0 * delta * EulerGamma * Nu * 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 * Nu * M_PI * S2z - 198450.0j * delta * Nu * M_PI * S2z - - 2490264.0j * delta * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + - 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - - 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * Nu * S1z * np.log(2.0) + - 396900.0 * delta * Nu * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - - 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * Nu * S2z * np.log(2.0) + - 396900.0 * delta * Nu * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - - 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + - 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - - 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + - 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - - 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * Nu * S2z * np.log(3.0) + - 396900.0 * delta * Nu * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + - 952560.0j * delta * (np.log(3.0)**2) + 952560.0j * delta * (np.log(b0)**2) + - 589680.0j * delta * np.log(r0) - 3735396.0j * delta * np.log(x) + - 2857680.0j * delta * EulerGamma * np.log(x) + 1428840.0 * delta * M_PI * np.log(x) - - 476280.0 * S1z * np.log(x) - 476280.0 * delta * S1z * np.log(x) + - 2262330.0 * Nu * S1z * np.log(x) + 595350.0 * delta * Nu * S1z * np.log(x) + - 476280.0 * S2z * np.log(x) - 476280.0 * delta * S2z * np.log(x) - - 2262330.0 * Nu * S2z * np.log(x) + 595350.0 * delta * Nu * S2z * np.log(x) + - 2857680.0j * delta * np.log(2.0) * np.log(x) + - 2857680.0j * delta * np.log(3.0) * np.log(x) + - 2143260.0j * delta * (np.log(x)**2) + + 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 0.0 + 0.0j - -def hl_3_m_3( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R - # Note: This specific pre-factor is identical across several modes in the C source - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - go_component: complex = hGO_3_m_3( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_3( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) - # The higher 'm' index means the phase evolves 3 times faster than the orbit - phase_factor: complex = np.exp(-3j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_3_m_min3( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_3: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * eta * sqrt(pi/5)) / R - # Note: This specific pre-factor is identical across several modes in the C source - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # We assume hGO_3_m_3 and hQC_3_m_3 are defined elsewhere in your package - go_component: complex = hGO_3_m_3( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_3( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - # In C: conj(hGO + hQC) - # In Python: use the .conjugate() method or np.conj() - amplitude_term: complex = (go_component + qc_component).conjugate() - - # cpolar(1, -3 * Phi) -> exp(-i * 3 * Phi) - # The higher 'm' index means the phase evolves 3 times faster than the orbit - phase_factor: complex = np.exp(3j * Phi) - - return pre_factor * amplitude_term * phase_factor + return complex(0, 0) # H32 +@register_hgo_lm(3, 2) def hGO_3_m_2( - mass: float, - Nu: float, + total_mass: float, + eta: float, r: float, rDOT: float, PhiDOT: float, @@ -1762,147 +1666,174 @@ def hGO_3_m_2( S1z: float, S2z: float, x: float, - params: KeplerVars, + params: CommonVars, ) -> complex: - delta = np.sqrt(1 - 4 * Nu) + 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) * mass * (-1 + 3 * Nu) * PhiDOT * + -(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) * params.Mtot2 * Nu * + (np.sqrt(0.7142857142857143) * total_mass2 * eta * (4 * PhiDOT * r + 1j * rDOT) * (S1z + S2z)) - / (3.0 * params.r2) + / (3.0 * r2) ) elif vpnorder == 4: return ( - -(mass * PhiDOT * - (2 * mass * - ((167 - 925 * Nu + 1615 * params.eta2) * PhiDOT * r + - 5j * (-82 + 239 * Nu + 55 * params.eta2) * rDOT) - + -(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 * Nu + 355 * params.eta2) * params.PhiDOT3 * params.r3 - - 60j * (-8 + 25 * Nu + params.eta2) * params.PhiDOT2 * params.r2 * rDOT + - 12 * (-23 + 70 * Nu + 10 * params.eta2) * PhiDOT * r * params.rDOT2 + - 5j * (-13 + 38 * Nu + 10 * params.eta2) * params.rDOT3))) + (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 = ( - (params.Mtot2 * Nu * PhiDOT * - (7j * mass + - r * (49j * params.PhiDOT2 * params.r2 + - 90 * PhiDOT * r * rDOT - 6j * params.rDOT2))) - / (4.0 * np.sqrt(35) * params.r2) + (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) * params.Mtot2 * - (2j * mass * rDOT * - ((-12 + Nu * (97 + 4 * Nu) + delta * (-12 + 5 * Nu)) * S1z + - (-12 + delta * (12 - 5 * Nu) + Nu * (97 + 4 * Nu)) * S2z) + - 4 * mass * PhiDOT * r * - (-((12 + delta * (12 - 23 * Nu) + Nu * (53 + 8 * Nu)) * S1z) - - (12 + Nu * (53 + 8 * Nu) + delta * (-12 + 23 * Nu)) * S2z) - + (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 * params.eta2 * PhiDOT * r * - (4 * params.PhiDOT2 * params.r2 - - 2j * PhiDOT * r * rDOT + params.rDOT2) * + (16 * eta2 * PhiDOT * r * + (4 * PhiDOT2 * r2 - + 2j * PhiDOT * r * rDOT + rDOT2) * (S1z + S2z) + - 30j * params.PhiDOT2 * params.r2 * rDOT * + 30j * PhiDOT2 * r2 * rDOT * (S1z + delta * S1z + S2z - delta * S2z) + - Nu * (-4 * params.PhiDOT3 * params.r3 * + eta * (-4 * PhiDOT3 * r3 * ((5 + 17 * delta) * S1z + (5 - 17 * delta) * S2z) - - 1j * params.PhiDOT2 * params.r2 * rDOT * + 1j * PhiDOT2 * r2 * rDOT * ((189 + 17 * delta) * S1z + (189 - 17 * delta) * S2z) + - 20 * PhiDOT * r * params.rDOT2 * + 20 * PhiDOT * r * rDOT2 * (-((-3 + delta) * S1z) + (3 + delta) * S2z) - - 4j * params.rDOT3 * + 4j * rDOT3 * ((-4 + delta) * S1z - (4 + delta) * S2z))))) - / (72.0 * params.r3) + / (72.0 * r3) ) return term1 + term2 elif vpnorder == 6: term1 = ( - -(mass * PhiDOT * - (4 * params.Mtot2 * - (2 * (5377 + 6438 * Nu - 79866 * params.eta2 + 37348 * params.eta3) * + -(total_mass * PhiDOT * + (4 * total_mass2 * + (2 * (5377 + 6438 * eta - 79866 * eta2 + 37348 * eta3) * PhiDOT * r - - 5j * (-4115 + 18399 * Nu - 20276 * params.eta2 + 7 * params.eta3) * + 5j * (-4115 + 18399 * eta - 20276 * eta2 + 7 * eta3) * rDOT) - - 4 * mass * r * - ((4599 - 15737 * Nu + 36259 * params.eta2 + 108563 * params.eta3) * - params.PhiDOT3 * params.r3 - - 1j * (-34053 + 59698 * Nu + 192949 * params.eta2 + 16193 * params.eta3) * - params.PhiDOT2 * params.r2 * rDOT + - (-59058 + 77983 * Nu + 322468 * params.eta2 - 4264 * params.eta3) * - PhiDOT * r * params.rDOT2 + - 5j * (-3387 + 8518 * Nu + 8968 * params.eta2 + 884 * params.eta3) * - params.rDOT3) + - 3 * params.r2 * - (4 * (-710 + 3892 * Nu - 10655 * params.eta2 + 24000 * params.eta3) * - params.PhiDOT5 * params.r5 + - 11j * (-1484 + 11693 * Nu - 25006 * params.eta2 + 428 * params.eta3) * - params.PhiDOT4 * params.r4 * rDOT + - 4 * (4161 - 25618 * Nu + 29489 * params.eta2 + 22078 * params.eta3) * - params.PhiDOT3 * params.r3 * params.rDOT2 + - 44j * (-151 + 1067 * Nu - 2419 * params.eta2 + 57 * params.eta3) * - params.PhiDOT2 * params.r2 * params.rDOT3 + - 4 * (2041 - 11680 * Nu + 19334 * params.eta2 + 3368 * params.eta3) * - PhiDOT * r * params.rDOT4 + - 5j * (477 - 2624 * Nu + 3862 * params.eta2 + 1160 * params.eta3) * - params.rDOT5))) - / (4752.0 * np.sqrt(35) * params.r2) + 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) * params.Mtot3 * - (2 * mass * - (2j * (1 + delta - 2 * Nu) * S1z + - Nu * ((1 + delta) * (-6 + kappa1) + 12 * Nu) * params.S1z2 + - 8 * Nu * (-1 + 3 * Nu) * S1z * S2z + - S2z * (-2j * (-1 + delta + 2 * Nu) + - Nu * (-6 - delta * (-6 + kappa2) + kappa2 + 12 * Nu) * S2z)) + - r * (4 * params.rDOT2 * - (2j * (1 + delta - 2 * Nu) * S1z + - Nu * ((1 + delta) * (-2 + kappa1) + 4 * Nu) * params.S1z2 + - 8 * params.eta2 * S1z * S2z + - S2z * (-2j * (-1 + delta + 2 * Nu) + - Nu * (-2 - delta * (-2 + kappa2) + kappa2 + 4 * Nu) * S2z)) + - 2 * params.PhiDOT2 * params.r2 * - (14j * (1 + delta - 2 * Nu) * S1z + + (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)) * Nu + - 4 * (1 + 9 * kappa1) * params.eta2) * params.S1z2 - - 64 * params.eta2 * S1z * S2z + - S2z * (-14j * (-1 + delta + 2 * Nu) + - 2 * Nu * (-13 + 13 * delta + 2 * Nu) * S2z + - kappa2 * (6 + delta * (-6 + 11 * Nu) + - Nu * (-23 + 36 * Nu)) * S2z)) + + (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 * Nu) * S1z - - 1j * (8 * Nu * (-2 - 2 * delta + Nu) + - kappa1 * (3 + delta * (3 + 11 * Nu) + - Nu * (5 + 18 * Nu))) * params.S1z2 + - 20j * (-3 + Nu) * Nu * S1z * S2z + - S2z * (-40 * (-1 + delta + 2 * Nu) - - 1j * (8 * Nu * (-2 + 2 * delta + Nu) + - kappa2 * (3 - delta * (3 + 11 * Nu) + - Nu * (5 + 18 * Nu))) * S2z))))) - / (24.0 * params.r4) + (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 @@ -1910,258 +1841,195 @@ def hGO_3_m_2( elif vpnorder == 7: return ( -0.000014029180695847363 * - (params.Mtot2 * - (3 * params.r2 * - (-120 * params.eta3 * - (3565 * params.PhiDOT5 * params.r5 + - 2321j * params.PhiDOT4 * params.r4 * rDOT + - 8244 * params.PhiDOT3 * params.r3 * params.rDOT2 - - 869j * params.PhiDOT2 * params.r2 * params.rDOT3 - - 56 * PhiDOT * r * params.rDOT4 - - 120j * params.rDOT5) * + (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 * params.PhiDOT2 * params.r2 * - (6 * params.PhiDOT3 * params.r3 + - 77j * params.PhiDOT2 * params.r2 * rDOT - - 72 * PhiDOT * r * params.rDOT2 + - 6j * params.rDOT3) * + 2475 * PhiDOT2 * r2 * + (6 * PhiDOT3 * r3 + + 77j * PhiDOT2 * r2 * rDOT - + 72 * PhiDOT * r * rDOT2 + + 6j * rDOT3) * (S1z + delta * S1z + S2z - delta * S2z) - - 3 * Nu * - (22j * params.PhiDOT4 * params.r4 * rDOT * + 3 * eta * + (22j * PhiDOT4 * r4 * rDOT * (36322j + 5 * (2993 + 3893 * delta) * S1z + 5 * (2993 - 3893 * delta) * S2z) - - 25j * params.rDOT5 * + 25j * rDOT5 * ((1053 + 443 * delta) * S1z + (1053 - 443 * delta) * S2z) + - 44j * params.PhiDOT2 * params.r2 * params.rDOT3 * + 44j * PhiDOT2 * r2 * rDOT3 * (-5444j + 5 * (1424 + 849 * delta) * S1z + 7120 * S2z - 4245 * delta * S2z) - - 20 * PhiDOT * r * params.rDOT4 * + 20 * PhiDOT * r * rDOT4 * (-1782j + (5963 + 2969 * delta) * S1z + 5963 * S2z - 2969 * delta * S2z) + - 4 * params.PhiDOT5 * params.r5 * + 4 * PhiDOT5 * r5 * (-86889j + 10 * (2063 + 225 * delta) * S1z + 20630 * S2z - 2250 * delta * S2z) + - 4 * params.PhiDOT3 * params.r3 * params.rDOT2 * + 4 * PhiDOT3 * r3 * rDOT2 * (234861j + 40 * (-1824 + 97 * delta) * S1z - 40 * (1824 + 97 * delta) * S2z)) + - 2 * params.eta2 * - (params.PhiDOT5 * params.r5 * + 2 * eta2 * + (PhiDOT5 * r5 * (-1549757j + 300 * (1448 + 1311 * delta) * S1z + 300 * (1448 - 1311 * delta) * S2z) + - 11j * params.PhiDOT4 * params.r4 * rDOT * + 11j * PhiDOT4 * r4 * rDOT * (329548j + 15 * (4113 + 1411 * delta) * S1z + 61695 * S2z - 21165 * delta * S2z) + - 22j * params.PhiDOT2 * params.r2 * params.rDOT3 * + 22j * PhiDOT2 * r2 * rDOT3 * (-23971j + 15 * (3829 + 243 * delta) * S1z + 57435 * S2z - 3645 * delta * S2z) + - 150j * params.rDOT5 * + 150j * rDOT5 * ((-503 + 92 * delta) * S1z - (503 + 92 * delta) * S2z) + - 10 * PhiDOT * r * params.rDOT4 * + 10 * PhiDOT * r * rDOT4 * (4565j + 6 * (-6327 + 991 * delta) * S1z - 6 * (6327 + 991 * delta) * S2z) + - 21 * params.PhiDOT3 * params.r3 * params.rDOT2 * + 21 * PhiDOT3 * r3 * rDOT2 * (161403j + 10 * (-1897 + 1471 * delta) * S1z - 10 * (1897 + 1471 * delta) * S2z))) - - 6 * mass * r * - (60 * params.eta3 * - (2417 * params.PhiDOT3 * params.r3 + - 7258j * params.PhiDOT2 * params.r2 * rDOT - - 4381 * PhiDOT * r * params.rDOT2 + - 480j * params.rDOT3) * + 6 * total_mass * r * + (60 * eta3 * + (2417 * PhiDOT3 * r3 + + 7258j * PhiDOT2 * r2 * rDOT - + 4381 * PhiDOT * r * rDOT2 + + 480j * rDOT3) * (S1z + S2z) - 165 * - (1161 * params.PhiDOT3 * params.r3 - - 536j * params.PhiDOT2 * params.r2 * rDOT - - 2412 * PhiDOT * r * params.rDOT2 - - 270j * params.rDOT3) * + (1161 * PhiDOT3 * r3 - + 536j * PhiDOT2 * r2 * rDOT - + 2412 * PhiDOT * r * rDOT2 - + 270j * rDOT3) * (S1z + delta * S1z + S2z - delta * S2z) + - 2 * Nu * - (1j * params.PhiDOT2 * params.r2 * rDOT * + 2 * eta * + (1j * PhiDOT2 * r2 * rDOT * (1015784j + 5 * (30849 + 88721 * delta) * S1z + 5 * (30849 - 88721 * delta) * S2z) + - params.PhiDOT3 * params.r3 * + PhiDOT3 * r3 * (-173371j + 5 * (61569 + 10789 * delta) * S1z + 307845 * S2z - 53945 * delta * S2z) - - 5 * PhiDOT * r * params.rDOT2 * + 5 * PhiDOT * r * rDOT2 * (-115368j + (177417 + 52307 * delta) * S1z + 177417 * S2z - 52307 * delta * S2z) + - 100j * params.rDOT3 * + 100j * rDOT3 * ((-1545 + 181 * delta) * S1z - (1545 + 181 * delta) * S2z)) + - params.eta2 * - (20 * PhiDOT * r * params.rDOT2 * + eta2 * + (20 * PhiDOT * r * rDOT2 * (-11187j - 48074 * S1z + 11057 * delta * S1z - 48074 * S2z - 11057 * delta * S2z) + - 725j * params.rDOT3 * + 725j * rDOT3 * (-73 * S1z + 31 * delta * S1z - 73 * S2z - 31 * delta * S2z) + - params.PhiDOT3 * params.r3 * + PhiDOT3 * r3 * (603141j - 543040 * S1z + 404620 * delta * S1z - 20 * (27152 + 20231 * delta) * S2z) + - 1j * params.PhiDOT2 * params.r2 * rDOT * + 1j * PhiDOT2 * r2 * rDOT * (-1798104j - 648485 * S1z + 105755 * delta * S1z - 5 * (129697 + 21151 * delta) * S2z))) + - 10 * params.Mtot2 * - (24 * params.eta3 * + 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 * Nu * rDOT * - (297 * (1 + delta) * kappa1 * params.S1z3 + - 297 * (1 + delta) * kappa1 * params.S1z2 * S2z + + 30j * eta * rDOT * + (297 * (1 + delta) * kappa1 * S1z3 + + 297 * (1 + delta) * kappa1 * S1z2 * S2z + S1z * (17261 - 1641 * delta - - 297 * (-1 + delta) * kappa2 * params.S2z2) + + 297 * (-1 + delta) * kappa2 * S2z2) + S2z * (17261 + 1641 * delta - - 297 * (-1 + delta) * kappa2 * params.S2z2)) + - 8 * Nu * PhiDOT * r * + 297 * (-1 + delta) * kappa2 * S2z2)) + + 8 * eta * PhiDOT * r * (-7315j - - 4455 * (1 + delta) * kappa1 * params.S1z3 + + 4455 * (1 + delta) * kappa1 * S1z3 + 3 * (3881 - 7757 * delta) * S2z - - 4455 * (1 + delta) * kappa1 * params.S1z2 * S2z + - 4455 * (-1 + delta) * kappa2 * params.S2z3 + + 4455 * (1 + delta) * kappa1 * S1z2 * S2z + + 4455 * (-1 + delta) * kappa2 * S2z3 + 3 * S1z * (3881 + 7757 * delta + - 1485 * (-1 + delta) * kappa2 * params.S2z2)) + - 3 * params.eta2 * + 1485 * (-1 + delta) * kappa2 * S2z2)) + + 3 * eta2 * (-5j * rDOT * - (S1z * (18793 + 223 * delta + 1188 * kappa1 * params.S1z2) + - (18793 - 223 * delta + 1188 * (-2 + kappa1) * params.S1z2) * S2z + - 1188 * (-2 + kappa2) * S1z * params.S2z2 + - 1188 * kappa2 * params.S2z3) + + (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 * params.S1z3 + + 5940 * kappa1 * S1z3 + (-23359 + 5563 * delta + - 5940 * (-2 + kappa1) * params.S1z2) * S2z + - 5940 * (-2 + kappa2) * S1z * params.S2z2 + - 5940 * kappa2 * params.S2z3))))) - / (np.sqrt(35) * params.r4) + 5940 * (-2 + kappa1) * S1z2) * S2z + + 5940 * (-2 + kappa2) * S1z * S2z2 + + 5940 * kappa2 * S2z3))))) + / (np.sqrt(35) * r4) ) else: - return 0.0 + 0.0j - + return complex(0.0, 0.0) + +@register_hqc_lm(3, 2) def hQC_3_m_2( - mass: float, - Nu: float, + total_mass: float, + eta: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: KeplerVars + 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 - b0: float = 2.0 * mass / np.exp(0.5) + 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 * np.pi + - np.log(4096.0) + 6.0 * np.log(b0) + 9.0 * np.log(x) + -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 * Nu) * params.x3p5 * common_log_term + -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) * Nu * (S1z + S2z) * params.x4 * common_log_term + 0.8888888888888888j * np.sqrt(0.7142857142857143) * eta * (S1z + S2z) * x4 * common_log_term ) elif vpnorder == 7: numerator = ( - -0.024691358024691357j * (193.0 - 680.0 * Nu + 230.0 * params.eta2) * params.x4p5 * common_log_term + -0.024691358024691357j * (193.0 - 680.0 * eta + 230.0 * eta2) * x4p5 * common_log_term ) return numerator / np.sqrt(35.0) else: - return 0.0 + 0.0j - -def hl_3_m_2( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package - go_component: complex = hGO_3_m_2( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_2( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) - phase_factor: complex = np.exp(-2j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_3_m_min2( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_3_m_2: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_3_m_2 and hQC_3_m_2 are assumed to be defined in your package - go_component: complex = hGO_3_m_2( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_2( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - amplitude_term: complex = (go_component + qc_component).conjugate() - - # cpolar(1, -2 * Phi) -> exp(-i * 2 * Phi) - phase_factor: complex = np.exp(2j * Phi) - - return pre_factor * amplitude_term * phase_factor + return complex(0, 0) # H31 +@register_hgo_lm(3, 1) def hGO_3_m_1( - mass: float, - Nu: float, + total_mass: float, + eta: float, r: float, rDOT: float, PhiDOT: float, @@ -2169,12 +2037,12 @@ def hGO_3_m_1( S1z: float, S2z: float, x: float, - params: KeplerVars, + params: CommonVars, ) -> complex: - delta = np.sqrt(1 - 4 * Nu) + delta = params.delta kappa1 = 1.0 kappa2 = 1.0 - r0 = 2 * mass / np.exp(0.5) + r0 = params.r0 combination_a = PhiDOT * r + 1j * rDOT combination_a2 = combination_a ** 2 @@ -2187,9 +2055,41 @@ def hGO_3_m_1( 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 * (mass * (7j * PhiDOT * r - 12 * rDOT) - + delta * (total_mass * (7j * PhiDOT * r - 12 * rDOT) - 6j * r * combination_b * combination_a2) / (6.0 * np.sqrt(14) * r) ) @@ -2197,419 +2097,427 @@ def hGO_3_m_1( elif vpnorder == 3: return ( delta * - (6j * (-5 + 19 * Nu) * params.r2 * combination_b2 * combination_a3 + - 2 * params.Mtot2 * - (1j * (-101 + 43 * Nu) * PhiDOT * r + - (109 - 86 * Nu) * rDOT) + - 3 * mass * r * - (-4j * (-9 + 14 * Nu) * params.PhiDOT3 * params.r3 + - 6 * (2 + 9 * Nu) * params.PhiDOT2 * params.r2 * rDOT - - 1j * (33 + 62 * Nu) * PhiDOT * r * params.rDOT2 + - 4 * (8 + 17 * Nu) * params.rDOT3)) - / (36.0 * np.sqrt(14) * params.r2) + (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 * params.Mtot2 * - (4 * mass * (-1 + 5 * Nu) * ((1 + delta) * S1z + (-1 + delta) * S2z) - - r * (2 * params.rDOT2 * - (-6 * (1 + delta) * S1z + 5 * (5 + 3 * delta) * Nu * S1z + + 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) * Nu * S2z) + - params.PhiDOT2 * params.r2 * - ((24 + 24 * delta - 87 * Nu + 31 * delta * Nu) * S1z + - (-24 + 24 * delta + 87 * Nu + 31 * delta * Nu) * 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 * Nu + 35 * delta * Nu) * S1z + - (-6 + 6 * delta + 31 * Nu + 35 * delta * Nu) * S2z))) - / (np.sqrt(14) * params.r3) + ((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 * Nu + 3387 * params.eta2) * params.r3 * + (-18j * (183 - 1579 * eta + 3387 * eta2) * r3 * combination_b3 * combination_a4 + - 2 * params.Mtot3 * - (1j * (26473 - 27451 * Nu + 9921 * params.eta2) * PhiDOT * r - - 12 * (623 - 732 * Nu + 1913 * params.eta2) * rDOT) + - 2 * params.Mtot2 * r * - (-1j * (-8641 - 59189 * Nu + 31959 * params.eta2) * params.PhiDOT3 * params.r3 + - (-32635 - 29345 * Nu + 29541 * params.eta2) * params.PhiDOT2 * params.r2 * rDOT - - 44j * (-256 + 781 * Nu + 840 * params.eta2) * PhiDOT * r * params.rDOT2 + - 6 * (-756 + 8238 * Nu + 7357 * params.eta2) * params.rDOT3) + - 3 * mass * params.r2 * - (2j * (-2479 - 4505 * Nu + 16785 * params.eta2) * params.PhiDOT5 * params.r5 + - 4 * (817 + 1220 * Nu - 7449 * params.eta2) * params.PhiDOT4 * params.r4 * rDOT + - 6j * (-1679 + 1469 * Nu + 12233 * params.eta2) * params.PhiDOT3 * params.r3 * params.rDOT2 - - 32 * (-460 + 421 * Nu + 2514 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT3 + - 1j * (-9851 + 17954 * Nu + 40968 * params.eta2) * PhiDOT * r * params.rDOT4 - - 12 * (-771 + 1126 * Nu + 3616 * params.eta2) * params.rDOT5)) - / (4752.0 * np.sqrt(14) * params.r3) + 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 = ( - params.Mtot3 * + total_mass3 * (kappa1 * - (-1j * (-13 - 13 * delta + 68 * Nu + 42 * delta * Nu) * PhiDOT * r - - 34 * (1 + delta) * rDOT + 4 * (26 + 9 * delta) * Nu * rDOT) * - params.S1z2 + - S2z * (12 * delta * Nu * (7j * PhiDOT * r - 6 * rDOT) * S1z + + (-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 * Nu + 42 * delta * Nu) * PhiDOT * r + - 2 * (17 - 17 * delta - 52 * Nu + 18 * delta * Nu) * rDOT) * + (-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) * params.r3) + / (24.0 * np.sqrt(14) * r3) ) return term1 + term2 elif vpnorder == 6: term1 = ( - delta * params.Mtot2 * Nu * - (668 * params.Mtot2 - - 2 * mass * r * - (727 * params.PhiDOT2 * params.r2 - - 99j * PhiDOT * r * rDOT + 452 * params.rDOT2) + - params.r2 * - (-499 * params.PhiDOT4 * params.r4 + - 1534j * params.PhiDOT3 * params.r3 * rDOT + - 3072 * params.PhiDOT2 * params.r2 * params.rDOT2 - - 680j * PhiDOT * r * params.rDOT3 + - 1000 * params.rDOT4)) - / (180.0 * np.sqrt(14) * params.r4) + 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 * params.Mtot2 * - (2 * params.Mtot2 * - ((252 * (1 + delta) - (1277 + 1279 * delta) * Nu + - 8 * (12 + 47 * delta) * params.eta2) * S1z + - (252 * (-1 + delta) + (1277 - 1279 * delta) * Nu + - 8 * (-12 + 47 * delta) * params.eta2) * S2z) + - 3 * params.r2 * - (2 * params.rDOT4 * - ((30 + Nu * (-187 + 318 * Nu) + - delta * (30 + Nu * (-101 + 122 * Nu))) * S1z + - (-30 + (187 - 318 * Nu) * Nu + - delta * (30 + Nu * (-101 + 122 * Nu))) * S2z) + - 2 * params.PhiDOT4 * params.r4 * - ((90 - Nu * (28 + 579 * Nu) + - delta * (90 + Nu * (-800 + 551 * Nu))) * S1z + - (-90 + Nu * (28 + 579 * Nu) + - delta * (90 + Nu * (-800 + 551 * Nu))) * S2z) + - 2j * PhiDOT * r * params.rDOT3 * - ((186 - Nu * (745 + 354 * Nu) + - delta * (186 + Nu * (-191 + 554 * Nu))) * S1z + - (-186 + Nu * (745 + 354 * Nu) + - delta * (186 + Nu * (-191 + 554 * Nu))) * S2z) + - 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * - ((32 + Nu * (-451 + 230 * Nu) + - delta * (32 + Nu * (691 + 626 * Nu))) * S1z + - (-32 + (451 - 230 * Nu) * Nu + - delta * (32 + Nu * (691 + 626 * Nu))) * S2z) + - 2j * params.PhiDOT3 * params.r3 * rDOT * - ((-12 + Nu * (-341 + 315 * Nu) + - delta * (-12 + Nu * (-91 + 1213 * Nu))) * S1z + - (12 + (341 - 315 * Nu) * Nu + - delta * (-12 + Nu * (-91 + 1213 * Nu))) * S2z)) - - 2 * mass * r * - (2 * params.PhiDOT2 * params.r2 * - ((-312 * (1 + delta) + 2 * (827 - 923 * delta) * Nu + - 5 * (-201 + 131 * delta) * params.eta2) * S1z + - (-312 * (-1 + delta) - 2 * (827 + 923 * delta) * Nu + - 5 * (201 + 131 * delta) * params.eta2) * S2z) + - 2 * params.rDOT2 * - ((105 + Nu * (-541 + 924 * Nu) + - delta * (105 + Nu * (-77 + 494 * Nu))) * S1z + - (-105 + (541 - 924 * Nu) * Nu + - delta * (105 + Nu * (-77 + 494 * Nu))) * S2z) + + 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 * Nu * (439 + 597 * Nu) + - delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S1z + - (-1104 + 7 * Nu * (439 + 597 * Nu) + - delta * (1104 + Nu * (-3071 + 3083 * Nu))) * S2z))) - / (np.sqrt(14) * params.r4) + ((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 = np.pi ** 2 + M_PI2 = M_PI ** 2 spin_terms = ( - 1513512 * params.Mtot3 * - (2 * mass * + 1513512 * total_mass3 * + (2 * total_mass * (PhiDOT * r * - (1j * (-97 + 631 * Nu) * S1z + - 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + - 3 * kappa1 * (-39 + Nu * (149 + 4 * Nu))) * params.S1z2 + - S2z * (97j - 631j * Nu - - 5 * (8 + 16 * Nu * (-7 + 15 * Nu) + - 3 * kappa2 * (-39 + Nu * (149 + 4 * Nu))) * S2z)) + + (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 * Nu) * S1z - - 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + - kappa1 * (155 + Nu * (-373 + 164 * Nu))) * params.S1z2 + - S2z * (36 - 166 * Nu + - 5j * (-4 * (6 + Nu * (-25 + 7 * Nu)) + - kappa2 * (155 + Nu * (-373 + 164 * Nu))) * S2z))) + - r * (2 * params.rDOT3 * - (S1z * (18 - 110 * Nu - - 5j * (69 * kappa1 - 214 * kappa1 * Nu + - 4 * (5 + 4 * kappa1) * params.eta2) * S1z) + - 2 * (-9 + 55 * Nu) * S2z + - 5j * (69 * kappa2 - 214 * kappa2 * Nu + - 4 * (5 + 4 * kappa2) * params.eta2) * params.S2z2) + - params.PhiDOT3 * params.r3 * - (S1z * (255j - 1403j * Nu + - 10 * (28 * (3 - 8 * Nu) * Nu + - kappa1 * (51 + 2 * Nu * (-91 + 118 * Nu))) * S1z) + - 1j * (-255 + 1403 * Nu) * S2z - - 10 * (28 * (3 - 8 * Nu) * Nu + - kappa2 * (51 + 2 * Nu * (-91 + 118 * Nu))) * params.S2z2) + - 2 * params.PhiDOT2 * params.r2 * rDOT * - (S1z * (255 - 1079 * Nu + - 5j * (2 * (60 - 361 * Nu) * Nu + - kappa1 * (6 + Nu * (-47 + 164 * Nu))) * S1z) + - (-255 + 1079 * Nu) * S2z - - 5j * (2 * (60 - 361 * Nu) * Nu + - kappa2 * (6 + Nu * (-47 + 164 * Nu))) * params.S2z2) + - PhiDOT * r * params.rDOT2 * - (4j * (-114 + 781 * Nu) * S1z + - 5 * (-213 * kappa1 - 72 * Nu + 278 * kappa1 * Nu + - 8 * (7 + 44 * kappa1) * params.eta2) * params.S1z2 + + (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 * Nu * (-1562j - 5 * (-36 + 139 * kappa2 + - 4 * (7 + 44 * kappa2) * Nu) * S2z))))) + 2 * eta * (-1562j - 5 * (-36 + 139 * kappa2 + + 4 * (7 + 44 * kappa2) * eta) * S2z))))) ) orbital_terms = ( delta * - (52920 * (-4083 + Nu * (58311 + Nu * (-269240 + 405617 * Nu))) * - params.r4 * combination_b4 * combination_a5 + - 840 * params.Mtot2 * params.r2 * - ((-2555489 + 7 * Nu * (820078 + Nu * (-6623390 + 4948497 * Nu))) * - params.PhiDOT5 * params.r5 + - 1j * (3537631 + 7 * Nu * (-2817653 + Nu * (-7052042 + 4017147 * Nu))) * - params.PhiDOT4 * params.r4 * rDOT + - 3 * (-1428997 + 7 * Nu * (-1230747 + Nu * (-237418 + 4061717 * Nu))) * - params.PhiDOT3 * params.r3 * params.rDOT2 + - 1j * (-5153011 + 7 * Nu * (-2375327 + 9 * Nu * (218846 + 1640185 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT3 + - (-7761899 + 7 * Nu * (2892563 + 5998602 * Nu + 7493619 * params.eta2)) * - PhiDOT * r * params.rDOT4 + - 3j * (-2422057 + 7 * Nu * (501045 + Nu * (2033141 + 2771816 * Nu))) * - params.rDOT5) - - 8820 * mass * params.r3 * - (2 * (111737 + Nu * (-366573 + Nu * (-618923 + 2278593 * Nu))) * - params.PhiDOT7 * params.r7 + - 2j * (101844 + Nu * (-273675 - 871630 * Nu + 2069774 * params.eta2)) * - params.PhiDOT6 * params.r6 * rDOT + - 2 * (341322 + Nu * (-1429938 + Nu * (-1206083 + 7690681 * Nu))) * - params.PhiDOT5 * params.r5 * params.rDOT2 + - 8j * (90241 + 2 * Nu * (-206022 + Nu * (-62113 + 1003558 * Nu))) * - params.PhiDOT4 * params.r4 * params.rDOT3 + - 2 * (410547 + Nu * (-2269686 + Nu * (762091 + 8400052 * Nu))) * - params.PhiDOT3 * params.r3 * params.rDOT4 + - 4j * (217935 + 2 * Nu * (-573699 + 5 * Nu * (18671 + 445748 * Nu))) * - params.PhiDOT2 * params.r2 * params.rDOT5 + - (333969 + 2 * Nu * (-981471 + 4 * Nu * (154039 + 750016 * Nu))) * - PhiDOT * r * params.rDOT6 + - 24j * (13245 + 2 * Nu * (-37005 + Nu * (14251 + 130160 * Nu))) * - params.rDOT7) + - 2 * params.Mtot4 * + (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 * params.eta3 + + (38468500 * eta3 + 648648j * (S1z + S2z) - - 90090 * ((-24 + 155 * kappa1) * params.S1z2 + - (-24 + 155 * kappa2) * params.S2z2) - - 420 * params.eta2 * + 90090 * ((-24 + 155 * kappa1) * S1z2 + + (-24 + 155 * kappa2) * S2z2) - + 420 * eta2 * (-122855 + - 3003 * ((2 + 11 * kappa1) * params.S1z2 - + 3003 * ((2 + 11 * kappa1) * S1z2 - 18 * S1z * S2z + - (2 + 11 * kappa2) * params.S2z2)) + - 3 * Nu * + (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) * params.S2z2))) + + 5 * (-52 + 63 * kappa2) * S2z2))) + PhiDOT * r * - (1176172480 * params.eta3 + + (1176172480 * eta3 + 8 * (74084729 - 189189 * S1z * (97j + 5 * (-8 + 117 * kappa1) * S1z) - 189189 * S2z * (97j + 5 * (-8 + 117 * kappa2) * S2z)) - - 176400 * params.eta2 * + 176400 * eta2 * (11251 + - 429 * ((2 + 13 * kappa1) * params.S1z2 - + 429 * ((2 + 13 * kappa1) * S1z2 - 22 * S1z * S2z + - (2 + 13 * kappa2) * params.S2z2)) + - 147 * Nu * + (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) * params.S2z2)))) - - 3 * params.Mtot3 * r * - (-4j * params.rDOT3 * - (601018232 - 1359334480 * params.eta3 - + 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 * Nu * + 231 * eta * (8490448 + 503685 * M_PI2 + 80808j * S2z + 2184 * (S1z * (37j + 190 * kappa1 * S1z) + 70 * S1z * S2z + - 190 * kappa2 * params.S2z2)) + - 58800 * params.eta2 * + 190 * kappa2 * S2z2)) + + 58800 * eta2 * (-62596 + - 429 * ((-1 + 5 * kappa1) * params.S1z2 - + 429 * ((-1 + 5 * kappa1) * S1z2 - 12 * S1z * S2z + - (-1 + 5 * kappa2) * params.S2z2))) - - 14j * params.PhiDOT2 * params.r2 * rDOT * - (-229522160 * params.eta3 + + (-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 * params.eta2 * + 2520 * eta2 * (100913 + - 286 * ((-31 + 5 * kappa1) * params.S1z2 - + 286 * ((-31 + 5 * kappa1) * S1z2 - 72 * S1z * S2z + - (-31 + 5 * kappa2) * params.S2z2)) + - 7 * Nu * + (-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) * params.S2z2))) + - 4 * PhiDOT * r * params.rDOT2 * - (-1095987374 + 1035895280 * params.eta3 + + 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 * params.eta2 * + 490 * eta2 * (-5802767 + - 5148 * ((2 + 23 * kappa1) * params.S1z2 - + 5148 * ((2 + 23 * kappa1) * S1z2 - 42 * S1z * S2z + - (2 + 23 * kappa2) * params.S2z2)) - - 77 * Nu * + (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) * params.S2z2))) + - 7 * params.PhiDOT3 * params.r3 * - (512893080 * params.eta3 - + 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 * params.eta2 * + 560 * eta2 * (2457671 + - 2574 * ((11 + 53 * kappa1) * params.S1z2 - + 2574 * ((11 + 53 * kappa1) * S1z2 - 84 * S1z * S2z + - (11 + 53 * kappa2) * params.S2z2)) + - 3 * Nu * + (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) * params.S2z2)))))) + 40 * (-21 + 20 * kappa2) * S2z2)))))) ) log_term = ( - 74954880 * delta * params.Mtot3 * - (mass * (-22j * PhiDOT * r - 24 * rDOT) + + 74954880 * delta * total_mass3 * + (total_mass * (-22j * PhiDOT * r - 24 * rDOT) + 3 * r * - (7j * params.PhiDOT3 * params.r3 + - 14 * params.PhiDOT2 * params.r2 * rDOT - - 8j * PhiDOT * r * params.rDOT2 + - 4 * params.rDOT3)) * + (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) * params.r4) + / (3.6324288e8 * np.sqrt(14) * r4) ) else: - return 0.0 + 0.0j - + return complex(0, 0) + +@register_hqc_lm(3, 1) def hQC_3_m_1( - mass: float, - Nu: float, - vpnorder: int, - x: float, - S1z: float, - S2z: float, - params: KeplerVars + mass: float, + eta: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: CommonVars ) -> complex: - - delta: float = np.sqrt(1.0 - 4.0 * Nu) - EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) - r0: float = b0 + + 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 * params.x3 * ( - -97.0 + 60.0 * EulerGamma - 30.0j * np.pi + - 60.0 * np.log(2.0) + 60.0 * np.log(b0) + 90.0 * np.log(x) + 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 * params.x4 * ( - -1552.0 + 960.0 * EulerGamma - 6487.0 * Nu + 420.0 * EulerGamma * Nu - - 480.0j * np.pi - 210.0j * Nu * np.pi + 960.0 * np.log(2.0) + - 420.0 * Nu * np.log(2.0) + 60.0 * (16.0 + 7.0 * Nu) * np.log(b0) + - 90.0 * (16.0 + 7.0 * Nu) * np.log(x) + (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 * ( - params.x4p5 * ( - 53132.0j * delta - 92232.0j * delta * EulerGamma + 35280.0j * delta * EulerGamma**2 - - 46116.0 * delta * np.pi + 35280.0 * delta * EulerGamma * np.pi - 2940.0j * delta * np.pi**2 + - 57036.0 * S1z + 57036.0 * delta * S1z - 35280.0 * EulerGamma * S1z - 35280.0 * delta * EulerGamma * S1z - - 114513.0 * Nu * S1z - 143031.0 * delta * Nu * S1z + 97020.0 * EulerGamma * Nu * S1z + - 114660.0 * delta * EulerGamma * Nu * S1z + 17640.0j * np.pi * S1z + 17640.0j * delta * np.pi * S1z - - 48510.0j * Nu * np.pi * S1z - 57330.0j * delta * Nu * np.pi * S1z - 57036.0 * S2z + - 57036.0 * delta * S2z + 35280.0 * EulerGamma * S2z - 35280.0 * delta * EulerGamma * S2z + - 114513.0 * Nu * S2z - 143031.0 * delta * Nu * S2z - 97020.0 * EulerGamma * Nu * S2z + - 114660.0 * delta * EulerGamma * Nu * S2z - 17640.0j * np.pi * S2z + 17640.0j * delta * np.pi * S2z + - 48510.0j * Nu * np.pi * S2z - 57330.0j * delta * Nu * np.pi * S2z - 92232.0j * delta * np.log(2.0) + - 70560.0j * delta * EulerGamma * np.log(2.0) + 35280.0 * delta * np.pi * np.log(2.0) - - 35280.0 * S1z * np.log(2.0) - 35280.0 * delta * S1z * np.log(2.0) + 97020.0 * Nu * S1z * np.log(2.0) + - 114660.0 * delta * Nu * S1z * np.log(2.0) + 35280.0 * S2z * np.log(2.0) - 35280.0 * delta * S2z * np.log(2.0) - - 97020.0 * Nu * S2z * np.log(2.0) + 114660.0 * delta * Nu * S2z * np.log(2.0) + - 35280.0j * delta * np.log(2.0)**2 - 2490264.0j * delta * np.log(3.0) + - 1905120.0j * delta * EulerGamma * np.log(3.0) + 952560.0 * delta * np.pi * np.log(3.0) - - 317520.0 * S1z * np.log(3.0) - 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * Nu * S1z * np.log(3.0) + - 396900.0 * delta * Nu * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - 317520.0 * delta * S2z * np.log(3.0) - - 1508220.0 * Nu * S2z * np.log(3.0) + 396900.0 * delta * Nu * S2z * np.log(3.0) + - 1905120.0j * delta * np.log(2.0) * np.log(3.0) + 952560.0j * delta * np.log(3.0)**2 + - 35280.0j * delta * np.log(b0)**2 + 21840.0j * delta * np.log(r0) - 138348.0j * delta * np.log(x) + - 105840.0j * delta * EulerGamma * np.log(x) + 52920.0 * delta * np.pi * np.log(x) - 52920.0 * S1z * np.log(x) - - 52920.0 * delta * S1z * np.log(x) + 145530.0 * Nu * S1z * np.log(x) + 171990.0 * delta * Nu * S1z * np.log(x) + - 52920.0 * S2z * np.log(x) - 52920.0 * delta * S2z * np.log(x) - 145530.0 * Nu * S2z * np.log(x) + - 171990.0 * delta * Nu * S2z * np.log(x) + 105840.0j * delta * np.log(2.0) * np.log(x) + - 79380.0j * delta * np.log(x)**2 + - 588.0 * np.log(b0) * ( - -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * np.pi + - 15.0 * (-4.0 - 4.0 * delta + 11.0 * Nu + 13.0 * delta * Nu) * S1z + - 60.0 * S2z - 60.0 * delta * S2z - 165.0 * Nu * S2z + 195.0 * delta * Nu * S2z + - 120.0j * delta * np.log(2.0) + 180.0j * delta * np.log(x) + 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 ) ) ) @@ -2617,99 +2525,26 @@ def hQC_3_m_1( return numerator_7 / np.sqrt(14.0) else: - return 0.0 + 0.0j - -def hl_3_m_1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - # Maintaining the logic of the C-source error check - raise ValueError( - "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module - go_component: complex = hGO_3_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -1 * Phi) -> exp(-i * Phi) - phase_factor: complex = np.exp(-1j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_3_m_min1( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - # Maintaining the logic of the C-source error check - raise ValueError( - "Error in hl_3_m_1: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # Assuming hGO_3_m_1 and hQC_3_m_1 are already translated in your module - go_component: complex = hGO_3_m_1( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params - ) - qc_component: complex = hQC_3_m_1( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - amplitude_factor = (go_component + qc_component).conjugate() - - # cpolar(1, 1 * Phi) -> exp(i * Phi) - phase_factor: complex = np.exp(1j * Phi) + return complex(0, 0) - return pre_factor * amplitude_factor * phase_factor +############ l = 4 ############### # H44 +@register_hgo_lm(4, 4) def hGO_4_m_4( - mass: float, - Nu: float, + total_mass: float, + eta: float, r: float, rDOT: float, PhiDOT: float, vpnorder: int, S1z: float, S2z: float, - params: KeplerVars, + x: float, + params: CommonVars, ) -> complex: - delta = np.sqrt((1 - 4 * Nu) + 0j) + delta = params.delta kappa1 = 1.0 kappa2 = 1.0 @@ -2721,135 +2556,162 @@ def hGO_4_m_4( 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 * Nu) * - (7 * params.Mtot2 + 6 * params.r2 * combination_a4 + - 3 * mass * r * - (17 * params.PhiDOT2 * params.r2 + - 18j * PhiDOT * r * rDOT - 6 * params.rDOT2)) - / (36.0 * params.r2) + 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 * params.Mtot3 * (314 - 987 * Nu + 195 * params.eta2) - - 60 * (23 - 159 * Nu + 291 * params.eta2) * params.r3 * + (40 * total_mass3 * (314 - 987 * eta + 195 * eta2) - + 60 * (23 - 159 * eta + 291 * eta2) * r3 * combination_b * combination_a5 + - params.Mtot2 * r * - ((53143 - 199660 * Nu + 127500 * params.eta2) * params.PhiDOT2 * params.r2 + - 24j * (967 - 4615 * Nu + 5935 * params.eta2) * PhiDOT * r * rDOT - - 10 * (290 - 2033 * Nu + 4365 * params.eta2) * params.rDOT2) - - 3 * mass * params.r2 * - ((613 - 920 * Nu + 6420 * params.eta2) * params.PhiDOT4 * params.r4 - - 8j * (-976 + 1745 * Nu + 3150 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + - 2 * (-6141 + 8980 * Nu + 31500 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 4j * (-1853 + 1730 * Nu + 13230 * params.eta2) * PhiDOT * r * params.rDOT3 - - 20 * (-83 + 30 * Nu + 762 * params.eta2) * params.rDOT4)) - / (1584.0 * np.sqrt(35) * params.r3) + 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 = ( - params.Mtot2 * Nu * - (6 * mass * (-43j * PhiDOT * r + 9 * rDOT) + - r * (-734j * params.PhiDOT3 * params.r3 + - 129 * params.PhiDOT2 * params.r2 * rDOT + - 156j * PhiDOT * r * params.rDOT2 - - 26 * params.rDOT3)) - / (24.0 * np.sqrt(35) * params.r3) + 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 = ( - params.Mtot2 * - (-3j * params.PhiDOT2 * params.r3 * rDOT * - ((-250 + 1221 * Nu - 1512 * params.eta2 + - delta * (-250 + 849 * Nu)) * S1z + - (-250 + delta * (250 - 849 * Nu) + 1221 * Nu - - 1512 * params.eta2) * S2z) - - 2 * mass * PhiDOT * r * - ((-130 + 757 * Nu - 1224 * params.eta2 + - delta * (-130 + 513 * Nu)) * S1z + - (-130 + delta * (130 - 513 * Nu) + 757 * Nu - - 1224 * params.eta2) * S2z) - - 2j * mass * rDOT * - ((-100 + 577 * Nu - 864 * params.eta2 + - delta * (-100 + 333 * Nu)) * S1z + - (-100 + delta * (100 - 333 * Nu) + 577 * Nu - - 864 * params.eta2) * S2z) - - 6 * params.PhiDOT3 * params.r4 * - ((-65 + 263 * Nu - 291 * params.eta2 + - delta * (-65 + 282 * Nu)) * S1z + - (-65 + delta * (65 - 282 * Nu) + 263 * Nu - - 291 * params.eta2) * S2z) + - 12 * PhiDOT * params.r2 * params.rDOT2 * - ((-40 + 201 * Nu - 252 * params.eta2 + - delta * (-40 + 129 * Nu)) * S1z + - (-40 + delta * (40 - 129 * Nu) + 201 * Nu - - 252 * params.eta2) * S2z) + - 6j * r * params.rDOT3 * - ((-20 + 107 * Nu - 144 * params.eta2 + - delta * (-20 + 63 * Nu)) * S1z + - (-20 + delta * (20 - 63 * Nu) + 107 * Nu - - 144 * params.eta2) * S2z)) - / (72.0 * np.sqrt(35) * params.r3) + 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 * params.Mtot4 * - (-4477296 + 12734393 * Nu - 6895 * params.eta2 + 1043805 * params.eta3) + - 3150 * (-367 + 4337 * Nu - 17462 * params.eta2 + 23577 * params.eta3) * - params.r4 * combination_b2 * combination_a6 + - 2 * params.Mtot3 * r * - ((-36967579 + 245501977 * Nu - 459916170 * params.eta2 + - 150200680 * params.eta3) * params.PhiDOT2 * params.r2 + - 4j * (7571073 - 10780154 * Nu - 56898800 * params.eta2 + - 43665510 * params.eta3) * PhiDOT * r * rDOT - - 10 * (1283609 - 5800627 * Nu + 3725295 * params.eta2 + - 4771935 * params.eta3) * params.rDOT2) - - params.Mtot2 * params.r2 * - ((-28258134 + 3245207 * Nu + 144051250 * params.eta2 + - 136991820 * params.eta3) * params.PhiDOT4 * params.r4 - - 24j * (2371982 - 7733376 * Nu - 7948185 * params.eta2 + - 9074870 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + - 7 * (6557973 - 50558069 * Nu + 59901380 * params.eta2 + - 104752320 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 168j * (52044 - 1084807 * Nu + 1849450 * params.eta2 + - 4171730 * params.eta3) * PhiDOT * r * params.rDOT3 - - 35 * (1083 - 1246819 * Nu + 2524240 * params.eta2 + - 5995845 * params.eta3) * params.rDOT4) - - 105 * mass * params.r3 * - ((116396 - 551405 * Nu + 560658 * params.eta2 + - 293036 * params.eta3) * params.PhiDOT6 * params.r6 + - 2j * (158192 - 670661 * Nu + 177718 * params.eta2 + - 2163976 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + - (-393665 + 1322392 * Nu + 1589680 * params.eta2 - - 8622660 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 - - 8j * (-23048 + 209397 * Nu - 487057 * params.eta2 + - 260396 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 - - (630647 - 3391000 * Nu + 2501958 * params.eta2 + - 7664096 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 - - 2j * (218975 - 1037408 * Nu + 148970 * params.eta2 + - 3699480 * params.eta3) * PhiDOT * r * params.rDOT5 + - 10 * (10233 - 44864 * Nu - 13050 * params.eta2 + - 203280 * params.eta3) * params.rDOT6)) - / (1.44144e6 * np.sqrt(35) * params.r4) + (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) * params.Mtot3 * (-1 + 3 * Nu) * - (12 * mass + - r * (53 * params.PhiDOT2 * params.r2 + - 26j * PhiDOT * r * rDOT - 8 * params.rDOT2)) * - (kappa1 * (1 + delta - 2 * Nu) * params.S1z2 + - S2z * (4 * Nu * S1z + kappa2 * S2z - delta * kappa2 * S2z - - 2 * kappa2 * Nu * S2z)) - / (48.0 * params.r4) + 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 @@ -2857,247 +2719,183 @@ def hGO_4_m_4( elif vpnorder == 7: # Henry et al. ecc+spin terms return ( - params.Mtot2 * - (14 * params.Mtot2 * - (120 * params.eta3 * + 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 * Nu * + 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 * params.eta2 * + 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 * params.r2 * - (1680 * params.eta3 * - (2833 * params.PhiDOT5 * params.r5 + - 18796j * params.PhiDOT4 * params.r4 * rDOT - - 13185 * params.PhiDOT3 * params.r3 * params.rDOT2 + - 1186j * params.PhiDOT2 * params.r2 * params.rDOT3 - - 5863 * PhiDOT * r * params.rDOT4 - - 2100j * params.rDOT5) * + 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 * params.PhiDOT5 * params.r5 + - 1195j * params.PhiDOT4 * params.r4 * rDOT - - 1950 * params.PhiDOT3 * params.r3 * params.rDOT2 - - 442j * params.PhiDOT2 * params.r2 * params.rDOT3 - - 384 * PhiDOT * r * params.rDOT4 - - 184j * params.rDOT5) * + (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 * params.eta2 * - (2 * params.PhiDOT5 * params.r5 * + 6 * eta2 * + (2 * PhiDOT5 * r5 * (-2459811j + 35 * (26517 + 10223 * delta) * S1z + (928095 - 357805 * delta) * S2z) - - 10j * params.rDOT5 * + 10j * rDOT5 * (35291j + 28 * (2183 + 1155 * delta) * S1z + (61124 - 32340 * delta) * S2z) + - 1j * params.PhiDOT2 * params.r2 * params.rDOT3 * + 1j * PhiDOT2 * r2 * rDOT3 * (917901j + 1120 * (-191 + 1616 * delta) * S1z - 1120 * (191 + 1616 * delta) * S2z) + - 5 * params.PhiDOT3 * params.r3 * params.rDOT2 * + 5 * PhiDOT3 * r3 * rDOT2 * (85426j + 7 * (-148363 + 4365 * delta) * S1z - 7 * (148363 + 4365 * delta) * S2z) - - 4 * PhiDOT * r * params.rDOT4 * + 4 * PhiDOT * r * rDOT4 * (280067j + 70 * (5844 + 4817 * delta) * S1z - 70 * (-5844 + 4817 * delta) * S2z) + - 1j * params.PhiDOT4 * params.r4 * rDOT * + 1j * PhiDOT4 * r4 * rDOT * (10375501j + 70 * (65831 + 22871 * delta) * S1z - 70 * (-65831 + 22871 * delta) * S2z)) + - Nu * (-40j * params.rDOT5 * + eta * (-40j * rDOT5 * (12203j + 42 * (843 + 656 * delta) * S1z - 42 * (-843 + 656 * delta) * S2z) + - 4j * params.PhiDOT2 * params.r2 * params.rDOT3 * + 4j * PhiDOT2 * r2 * rDOT3 * (-266071j + 210 * (-2279 + 1010 * delta) * S1z - 210 * (2279 + 1010 * delta) * S2z) - - 16 * PhiDOT * r * params.rDOT4 * + 16 * PhiDOT * r * rDOT4 * (58753j + 105 * (2030 + 1947 * delta) * S1z - 105 * (-2030 + 1947 * delta) * S2z) + - params.PhiDOT5 * params.r5 * + PhiDOT5 * r5 * (-8997592j + 105 * (39959 + 15835 * delta) * S1z - 105 * (-39959 + 15835 * delta) * S2z) - - 10 * params.PhiDOT3 * params.r3 * params.rDOT2 * + 10 * PhiDOT3 * r3 * rDOT2 * (254228j + 21 * (65293 + 34551 * delta) * S1z - 21 * (-65293 + 34551 * delta) * S2z) + - 2j * params.PhiDOT4 * params.r4 * rDOT * + 2j * PhiDOT4 * r4 * rDOT * (12351083j + 105 * (43193 + 37913 * delta) * S1z - 105 * (-43193 + 37913 * delta) * S2z))) + - mass * r * - (2520 * params.eta3 * - (8036 * params.PhiDOT3 * params.r3 + - 41814j * params.PhiDOT2 * params.r2 * rDOT - - 30537 * PhiDOT * r * params.rDOT2 - - 9064j * params.rDOT3) * + total_mass * r * + (2520 * eta3 * + (8036 * PhiDOT3 * r3 + + 41814j * PhiDOT2 * r2 * rDOT - + 30537 * PhiDOT * r * rDOT2 - + 9064j * rDOT3) * (S1z + S2z) - 210 * - (11849 * params.PhiDOT3 * params.r3 + - 31868j * params.PhiDOT2 * params.r2 * rDOT + - 3572 * PhiDOT * r * params.rDOT2 + - 1508j * params.rDOT3) * + (11849 * PhiDOT3 * r3 + + 31868j * PhiDOT2 * r2 * rDOT + + 3572 * PhiDOT * r * rDOT2 + + 1508j * rDOT3) * (S1z + delta * S1z + S2z - delta * S2z) - - 12 * params.eta2 * - (1j * params.PhiDOT2 * params.r2 * rDOT * + 12 * eta2 * + (1j * PhiDOT2 * r2 * rDOT * (-1150397j + 35 * (154765 + 157449 * delta) * S1z + 5416775 * S2z - 5510715 * delta * S2z) - - PhiDOT * r * params.rDOT2 * + PhiDOT * r * rDOT2 * (2306552j + 35 * (4931 + 110733 * delta) * S1z + 172585 * S2z - 3875655 * delta * S2z) + - params.PhiDOT3 * params.r3 * + PhiDOT3 * r3 * (12461121j + 35 * (18331 + 87381 * delta) * S1z + 641585 * S2z - 3058335 * delta * S2z) - - 25j * params.rDOT3 * + 25j * rDOT3 * (36676j + 35 * (137 + 1053 * delta) * S1z + 4795 * S2z - 36855 * delta * S2z)) + - Nu * (-200j * params.rDOT3 * + eta * (-200j * rDOT3 * (-2501j + 42 * (-308 + 51 * delta) * S1z - 42 * (308 + 51 * delta) * S2z) + - 2 * PhiDOT * r * params.rDOT2 * + 2 * PhiDOT * r * rDOT2 * (-1951984j - 105 * (-37907 + 14661 * delta) * S1z + 105 * (37907 + 14661 * delta) * S2z) + - 8j * params.PhiDOT2 * params.r2 * rDOT * + 8j * PhiDOT2 * r2 * rDOT * (-10005028j + 105 * (40991 + 31689 * delta) * S1z - 105 * (-40991 + 31689 * delta) * S2z) + - params.PhiDOT3 * params.r3 * + PhiDOT3 * r3 * (88418488j + 105 * (57793 + 266391 * delta) * S1z - 105 * (-57793 + 266391 * delta) * S2z)))) - / (332640.0 * np.sqrt(35) * params.r4) + / (332640.0 * np.sqrt(35) * r4) ) else: - return 0.0 + 0.0j - + return complex(0, 0) + +@register_hqc_lm(4, 4) def hQC_4_m_4( mass: float, - Nu: float, + eta: float, vpnorder: int, x: float, S1z: float, S2z: float, - params: KeplerVars + params: CommonVars ) -> complex: EulerGamma: float = 0.5772156649015329 - b0: float = 2.0 * mass / np.exp(0.5) + 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 * params.x3p5 * ( - 1888.0 - 960.0 * EulerGamma - 5661.0 * Nu + 2880.0 * EulerGamma * Nu + - 480.0j * np.pi - 1440.0j * Nu * np.pi - 2880.0 * np.log(2.0) + - 8640.0 * Nu * np.log(2.0) + 960.0 * (-1.0 + 3.0 * Nu) * np.log(b0) + - 1440.0 * (-1.0 + 3.0 * Nu) * np.log(x) + (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 * params.x4p5 * ( - -752360448.0 + 382556160.0 * EulerGamma + 2620364605.0 * Nu - - 1333248000.0 * EulerGamma * Nu - 898312500.0 * params.eta2 + - 458035200.0 * EulerGamma * params.eta2 - 191278080.0j * np.pi + - 666624000.0j * Nu * np.pi - 229017600.0j * params.eta2 * np.pi + - 1147668480.0 * np.log(2.0) - 3999744000.0 * Nu * np.log(2.0) + - 1374105600.0 * params.eta2 * np.log(2.0) + - 215040.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(b0) + - 322560.0 * (1779.0 - 6200.0 * Nu + 2130.0 * params.eta2) * np.log(x) + (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 0.0 + 0.0j - -def hl_4_m_4( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure - go_component: complex = hGO_4_m_4( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params - ) - qc_component: complex = hQC_4_m_4( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - # cpolar(1, -4 * Phi) -> exp(-i * 4 * Phi) - phase_factor: complex = np.exp(-4j * Phi) - - return pre_factor * (go_component + qc_component) * phase_factor - -def hl_4_m_min4( - mass: float, - Nu: float, - r: float, - rDOT: float, - Phi: float, - PhiDOT: float, - R: float, - vpnorder: int, - S1z: float, - S2z: float, - x: float, - params: KeplerVars - ) -> complex: - - if vpnorder > 8: - raise ValueError( - "Error in hl_4_m_4: Input PN order parameter should be between [0, 8]." - ) - - # Pre-factor: (4 * M * Nu * sqrt(pi/5)) / R - pre_factor: float = (4.0 * mass * Nu * np.sqrt(np.pi / 5.0)) / R - - # Evaluate the General Orbit (GO) and Quasi-Circular (QC) components - # hGO_4_m_4 and hQC_4_m_4 are assuming your existing structure - go_component: complex = hGO_4_m_4( - mass, Nu, r, rDOT, PhiDOT, vpnorder, S1z, S2z, params - ) - qc_component: complex = hQC_4_m_4( - mass, Nu, vpnorder, x, S1z, S2z, params - ) - - amplitude_factor = (go_component + qc_component).conjugate() - - # cpolar(1, 4 * Phi) -> exp(i * 4 * Phi) - phase_factor: complex = np.exp(4j * Phi) - - return pre_factor * amplitude_factor * phase_factor + return complex(0, 0) # H43 +@register_hgo_lm(4, 3) def hGO_4_m_3( - mass: float, - Nu: float, + total_mass: float, + eta: float, r: float, rDOT: float, PhiDOT: float, @@ -3105,109 +2903,137 @@ def hGO_4_m_3( S1z: float, S2z: float, x: float, - params: KeplerVars, + params: CommonVars, ) -> complex: - delta = np.sqrt(1 - 4 * Nu) + 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 * mass * (-1 + 2 * Nu) * PhiDOT * - (4 * mass + - r * (23 * params.PhiDOT2 * params.r2 + - 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) + 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) * - params.Mtot2 * Nu * - (4 * mass + - r * (23 * params.PhiDOT2 * params.r2 + - 10j * PhiDOT * r * rDOT - 2 * params.rDOT2)) * + total_mass2 * eta * + (4 * total_mass + + r * (23 * PhiDOT2 * r2 + + 10j * PhiDOT * r * rDOT - 2 * rDOT2)) * ((-1 + delta) * S1z + S2z + delta * S2z) - / params.r3 + / r3 ) elif vpnorder == 5: return ( - 0.0012626262626262627j * delta * mass * PhiDOT * - (2 * params.Mtot2 * (972 - 2293 * Nu + 1398 * params.eta2) + - 2 * mass * r * - ((1788 - 9077 * Nu + 13416 * params.eta2) * params.PhiDOT2 * params.r2 + - 3j * (-2796 + 5299 * Nu + 1622 * params.eta2) * PhiDOT * r * rDOT - - 2 * (-1200 + 2545 * Nu + 162 * params.eta2) * params.rDOT2) - - 3 * params.r2 * - ((-524 - 489 * Nu + 6392 * params.eta2) * params.PhiDOT4 * params.r4 + - 4j * (796 - 1864 * Nu + 133 * params.eta2) * params.PhiDOT3 * params.r3 * rDOT + - 42 * (-51 + 94 * Nu + 56 * params.eta2) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 4j * (-229 + 366 * Nu + 358 * params.eta2) * PhiDOT * r * params.rDOT3 - - 4 * (-43 + 62 * Nu + 80 * params.eta2) * params.rDOT4)) - / (np.sqrt(70) * params.r2) + 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 * params.Mtot2 * Nu * PhiDOT * - (6 * mass * (181 * PhiDOT * r - 89j * rDOT) + - r * (4847 * params.PhiDOT3 * params.r3 - - 7338j * params.PhiDOT2 * params.r2 * rDOT - - 408 * PhiDOT * r * params.rDOT2 + - 112j * params.rDOT3)) - / (180.0 * np.sqrt(70) * params.r2) + 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 * params.Mtot2 * - (2 * params.Mtot2 * - ((-440 + 6801 * Nu - 1428 * params.eta2 + - delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S1z + - (440 - 6801 * Nu + 1428 * params.eta2 + - delta * (-440 - 3193 * Nu + 300 * params.eta2)) * S2z) - - 2 * mass * r * + -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 * Nu + 59 * params.eta2) * S1z - - 5 * (264 - 311 * Nu + 823 * params.eta2) * S1z + - delta * (-1320 + 9093 * Nu + 59 * params.eta2) * S2z + - 5 * (264 - 311 * Nu + 823 * params.eta2) * S2z) - - 2 * params.rDOT2 * - ((220 + 1659 * Nu - 1512 * params.eta2 + - delta * (220 - 3067 * Nu + 240 * params.eta2)) * S1z + - (-220 - 1659 * Nu + 1512 * params.eta2 + - delta * (220 - 3067 * Nu + 240 * params.eta2)) * S2z) + - 2 * params.PhiDOT2 * params.r2 * - ((1826 - 19530 * Nu + 20145 * params.eta2 + - delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S1z + - (-1826 + 19530 * Nu - 20145 * params.eta2 + - delta * (1826 + 1534 * Nu + 567 * params.eta2)) * S2z)) - - 3 * params.r2 * - (3080j * params.PhiDOT3 * params.r3 * 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 * params.eta2 * - (129 * params.PhiDOT2 * params.r2 * params.rDOT2 * + 2 * eta2 * + (129 * PhiDOT2 * r2 * rDOT2 * (41 * S1z + 23 * delta * S1z - 41 * S2z + 23 * delta * S2z) - - 2 * params.rDOT4 * + 2 * rDOT4 * (149 * S1z + 75 * delta * S1z - 149 * S2z + 75 * delta * S2z) + - 2j * PhiDOT * r * params.rDOT3 * + 2j * PhiDOT * r * rDOT3 * (925 * S1z + 491 * delta * S1z - 925 * S2z + 491 * delta * S2z) - - 1j * params.PhiDOT3 * params.r3 * rDOT * + 1j * PhiDOT3 * r3 * rDOT * (-2105 * S1z + 753 * delta * S1z + 2105 * S2z + 753 * delta * S2z) + - params.PhiDOT4 * params.r4 * + PhiDOT4 * r4 * (11617 * S1z + 4847 * delta * S1z - 11617 * S2z + 4847 * delta * S2z)) + - Nu * (16 * params.PhiDOT4 * params.r4 * + eta * (16 * PhiDOT4 * r4 * (-413 * S1z + 127 * delta * S1z + 413 * S2z + 127 * delta * S2z) - - 2 * params.rDOT4 * + 2 * rDOT4 * (-301 * S1z + 213 * delta * S1z + 301 * S2z + 213 * delta * S2z) + - 2j * PhiDOT * r * params.rDOT3 * + 2j * PhiDOT * r * rDOT3 * (-1625 * S1z + 1009 * delta * S1z + 1625 * S2z + 1009 * delta * S2z) + - 3 * params.PhiDOT2 * params.r2 * params.rDOT2 * + 3 * PhiDOT2 * r2 * rDOT2 * (-2587 * S1z + 1267 * delta * S1z + 2587 * S2z + 1267 * delta * S2z) - - 2j * params.PhiDOT3 * params.r3 * rDOT * + 2j * PhiDOT3 * r3 * rDOT * (3635 * S1z + 7981 * delta * S1z - 3635 * S2z + 7981 * delta * S2z)))) - / (np.sqrt(70) * params.r4) + / (np.sqrt(70) * r4) ) return term1 + term2 @@ -3215,129 +3041,192 @@ def hGO_4_m_3( elif vpnorder == 7: # Henry et al. ecc+spin terms spin_terms = ( - 5005 * params.Mtot2 * - (-24 * PhiDOT * params.r2 * params.rDOT2 * - (1j * (-11 + 48 * Nu) * S1z + - 6 * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + - S2z * (11j - 48j * Nu - - 6 * (-5 + 4 * kappa2) * params.eta2 * S2z)) + - 4 * r * params.rDOT3 * - ((-11 + 93 * Nu) * S1z - - 6j * (-5 + 4 * kappa1) * params.eta2 * params.S1z2 + - S2z * (11 - 93 * Nu + - 6j * (-5 + 4 * kappa2) * params.eta2 * S2z)) + - 4 * mass * rDOT * - ((22 - 111 * Nu) * S1z + - 6j * (15 + 8 * kappa1) * params.eta2 * params.S1z2 + - S2z * (-22 + 111 * Nu - - 6j * (15 + 8 * kappa2) * params.eta2 * S2z)) + - 6j * params.PhiDOT2 * params.r3 * rDOT * - (1j * (-121 + 963 * Nu) * S1z + - 6 * (-55 * params.eta2 + - kappa1 * (-5 + 30 * Nu + 4 * params.eta2)) * params.S1z2 + + 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) * params.eta2 * S2z - - 9 * Nu * (107j + 20 * kappa2 * S2z))) + - 2 * mass * PhiDOT * r * - (-1j * (-121 + 633 * Nu) * S1z + - 6 * (180 * params.eta2 + - kappa1 * (9 - 54 * Nu + 28 * params.eta2)) * params.S1z2 - + 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) * params.eta2 * S2z - - 3 * Nu * (211j + 108 * kappa2 * S2z))) + - params.PhiDOT3 * params.r4 * - (-1j * (-649 + 4767 * Nu) * S1z + - 6 * (820 * params.eta2 + - kappa1 * (75 - 450 * Nu + 364 * params.eta2)) * params.S1z2 - + 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) * params.eta2 * S2z - - 3 * Nu * (1589j + 900 * kappa2 * S2z)))) + 24 * (205 + 91 * kappa2) * eta2 * S2z - + 3 * eta * (1589j + 900 * kappa2 * S2z)))) ) orbital_terms = ( delta * - (2 * mass * PhiDOT * params.r3 * - (10 * (234744 - 1010534 * Nu + 1024443 * params.eta2 + - 5451096 * params.eta3) * params.PhiDOT4 * params.r4 - - 3j * (-2426804 + 1512854 * Nu + 4994115 * params.eta2 + - 610960 * params.eta3) * params.PhiDOT3 * params.r3 * rDOT + - (-30341028 + 23936528 * Nu + 89326545 * params.eta2 + - 19329660 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT2 + - 21j * (-668008 + 803028 * Nu + 1908955 * params.eta2 + - 540370 * params.eta3) * PhiDOT * r * params.rDOT3 - - 14 * (-172143 + 155683 * Nu + 680580 * params.eta2 + - 111840 * params.eta3) * params.rDOT4) - - 105 * PhiDOT * params.r4 * - ((-8280 + 24681 * Nu - 151973 * params.eta2 + - 624074 * params.eta3) * params.PhiDOT6 * params.r6 + - 2j * (-32208 + 248485 * Nu - 524074 * params.eta2 + - 24546 * params.eta3) * params.PhiDOT5 * params.r5 * rDOT + - 2 * (48924 - 239802 * Nu + 137447 * params.eta2 + - 358156 * params.eta3) * params.PhiDOT4 * params.r4 * params.rDOT2 + - 4j * (174 + 24488 * Nu - 102039 * params.eta2 + - 44882 * params.eta3) * params.PhiDOT3 * params.r3 * params.rDOT3 + - 3 * (10455 - 56490 * Nu + 84504 * params.eta2 + - 54016 * params.eta3) * params.PhiDOT2 * params.r2 * params.rDOT4 + - 2j * (11175 - 52698 * Nu + 57436 * params.eta2 + - 60808 * params.eta3) * PhiDOT * r * params.rDOT5 - - 6 * (829 - 3726 * Nu + 3480 * params.eta2 + - 4640 * params.eta3) * params.rDOT6) + - params.Mtot2 * r * - (20020 * params.rDOT3 * (S1z + S2z) * - (-11 + 71 * Nu + 30j * params.eta2 * (S1z + S2z)) - - 52 * PhiDOT * r * params.rDOT2 * - (129150 * params.eta3 + - 7 * Nu * (31961 + 8580j * S1z + 8580j * S2z) - + (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 * params.eta2 * - (33313 + 1980 * params.S1z2 + 3960 * S1z * S2z + 1980 * params.S2z2)) + - params.PhiDOT3 * params.r3 * - (-10566168 - 70869960 * params.eta3 + - 3248245j * S1z + 2252250 * kappa1 * params.S1z2 + - 3248245j * S2z + 2252250 * kappa2 * params.S2z2 - - 7 * Nu * - (4818166 + 2480335j * S1z + 1287000 * kappa1 * params.S1z2 + - 2480335j * S2z + 1287000 * kappa2 * params.S2z2) + - 3850 * params.eta2 * - (38873 + 78 * (7 + 30 * kappa1) * params.S1z2 - + 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) * params.S2z2)) - - 2j * params.PhiDOT2 * params.r2 * rDOT * - (6531280 * params.eta3 + - 3 * (5382288 + 605605j * S1z + 150150 * kappa1 * params.S1z2 + - 605605j * S2z + 150150 * kappa2 * params.S2z2) + - 210 * params.eta2 * - (322144 + 2145 * (1 + 4 * kappa1) * params.S1z2 - + 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) * params.S2z2) - - 21 * Nu * - (2826484 + 85800 * kappa1 * params.S1z2 + - 515515j * S2z + 85800 * kappa2 * params.S2z2 - + 2145 * (1 + 4 * kappa2) * S2z2) - + 21 * eta * + (2826484 + 85800 * kappa1 * S1z2 + + 515515j * S2z + 85800 * kappa2 * S2z2 - 5005 * S1z * (-103j + 60 * S2z)))) - - 26 * params.Mtot3 * + 26 * total_mass3 * (770 * rDOT * - (-90j * params.eta2 * params.S1z2 + - S2z * (-22 + 67 * Nu - 90j * params.eta2 * S2z) + - S1z * (-22 + Nu * (67 + 300j * S2z) - 180j * params.eta2 * S2z)) + + (-90j * eta2 * S1z2 + + S2z * (-22 + 67 * eta - 90j * eta2 * S2z) + + S1z * (-22 + eta * (67 + 300j * S2z) - 180j * eta2 * S2z)) + PhiDOT * r * - (-38076 + 174720 * params.eta3 - - 46585j * S1z - 20790 * kappa1 * params.S1z2 - - 46585j * S2z - 20790 * kappa2 * params.S2z2 - - 1260 * params.eta2 * - (158 + 33 * (5 + 2 * kappa1) * params.S1z2 + + (-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) * params.S2z2) + - 7 * Nu * - (6188 + 11880 * kappa1 * params.S1z2 + - 21505j * S2z + 11880 * kappa2 * params.S2z2 + + 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 * mass * + -1.3875013875013875e-6j * total_mass * (spin_terms + orbital_terms) - / (np.sqrt(70) * params.r4) + / (np.sqrt(70) * r4) ) else: - return Complex(0.0, 0.0) \ No newline at end of file + 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 From f5ae319b1d5b10da765af5e35e0c65bae0cfebb4 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 02:57:43 +0530 Subject: [PATCH 31/36] Remove old code --- .../python_codes/esigma_go_terms_draft.py | 1696 ----------------- 1 file changed, 1696 deletions(-) delete mode 100644 esigmapy/python_codes/esigma_go_terms_draft.py diff --git a/esigmapy/python_codes/esigma_go_terms_draft.py b/esigmapy/python_codes/esigma_go_terms_draft.py deleted file mode 100644 index c24988c..0000000 --- a/esigmapy/python_codes/esigma_go_terms_draft.py +++ /dev/null @@ -1,1696 +0,0 @@ -import numpy as np -from dataclasses import dataclass - -# Storing constants -M_PI = np.pi -M_PI2 = M_PI ** 2 -LOG2 = np.log(2) - -#---------------------------------------------------- -def rect(r: float, phi: float) -> complex: - """Convert polar coordinates to rectangular form.""" - return r * np.exp(1j * phi) -#---------------------------------------------------- - -@dataclass -class CommonVars: - 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): - """Similarly for QC functions""" - def decorator(func): - H_QC_LM_FUNCS[(l, m)] = func - return func - return decorator - -# 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 + np.log(64.0) + 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) - - -# H33 - -# def hGO_3_m_3( -# total_mass: float, -# eta: float, -# r: float, -# rDOT: float, -# PhiDOT: float, -# vpnorder: int, -# S1z: float, -# S2z: float, -# x: float, -# ) -> complex: -# delta = np.sqrt(1 - 4 * eta) -# kappa1 = 1.0 -# kappa2 = 1.0 -# r0 = 2 * total_mass / np.exp(0.5) - -# 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) - -# def hQC_3_m_3( -# 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 - -# 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 * np.log(2.0) + 60.0 * np.log(3.0) + -# 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 * np.log(2.0) + 43740.0 * eta * np.log(2.0) - -# 116640.0 * np.log(3.0) + 43740.0 * eta * np.log(3.0) + -# 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 * np.log(2.0) + 120.0j * delta * np.log(3.0) + -# 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 * np.log(2.0) + 1905120.0j * delta * EulerGamma * np.log(2.0) + -# 952560.0 * delta * M_PI * np.log(2.0) - 317520.0 * S1z * np.log(2.0) - -# 317520.0 * delta * S1z * np.log(2.0) + 1508220.0 * eta * S1z * np.log(2.0) + -# 396900.0 * delta * eta * S1z * np.log(2.0) + 317520.0 * S2z * np.log(2.0) - -# 317520.0 * delta * S2z * np.log(2.0) - 1508220.0 * eta * S2z * np.log(2.0) + -# 396900.0 * delta * eta * S2z * np.log(2.0) + 952560.0j * delta * (np.log(2.0)**2) - -# 2490264.0j * delta * np.log(3.0) + 1905120.0j * delta * EulerGamma * np.log(3.0) + -# 952560.0 * delta * M_PI * np.log(3.0) - 317520.0 * S1z * np.log(3.0) - -# 317520.0 * delta * S1z * np.log(3.0) + 1508220.0 * eta * S1z * np.log(3.0) + -# 396900.0 * delta * eta * S1z * np.log(3.0) + 317520.0 * S2z * np.log(3.0) - -# 317520.0 * delta * S2z * np.log(3.0) - 1508220.0 * eta * S2z * np.log(3.0) + -# 396900.0 * delta * eta * S2z * np.log(3.0) + 1905120.0j * delta * np.log(2.0) * np.log(3.0) + -# 952560.0j * delta * (np.log(3.0)**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 * np.log(2.0) * logx + -# 2857680.0j * delta * np.log(3.0) * logx + -# 2143260.0j * delta * (logx**2) + -# log_b0_term -# )) -# return numerator_7 / (2352.0 * np.sqrt(210.0)) - -# else: -# return complex(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: 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 From 6b0c01351f4823862c1ebf980d5b9c2f258f4f1d Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 02:59:21 +0530 Subject: [PATCH 32/36] Removed duplicate functions, fixed function definition --- esigmapy/python_codes/generator_python.py | 205 +--------------------- 1 file changed, 2 insertions(+), 203 deletions(-) diff --git a/esigmapy/python_codes/generator_python.py b/esigmapy/python_codes/generator_python.py index 3d3c428..14bd2c1 100644 --- a/esigmapy/python_codes/generator_python.py +++ b/esigmapy/python_codes/generator_python.py @@ -1,4 +1,4 @@ -# Copyright (C) 2020 Prayush Kumar, Akash Maurya, Kaushik Paul +# Written by Samanwaya Mukherjee (2026) # """Functions specific to generating ESIGMA waveforms""" @@ -470,207 +470,6 @@ def get_inspiral_esigma_waveform_py( return t, hp, hc -# def _get_window_start(freq_, delta_t_, delta_phi_, direction="forward"): -# """Integrates frequency backward/forward from the start/end until -# `delta_phi_` radians of orbital phase has elapsed. - -# Args: -# freq_ (numpy.array): Frequency time series, uniformly sampled -# delta_t_ (float64): Step size in time -# delta_phi_ (float64): Phase shift in radians to integrate across -# direction (str, optional): Direction of integration in time. -# Defaults to "forward". - -# Returns: -# int: Index in frequency array where `delta_phi` is reached -# """ -# from scipy import integrate - -# if direction == "backward": -# for idx in range(len(freq_) - 2, 0, -1): -# # this could be optimized to not repeat integration -# if abs(integrate.trapezoid(freq_[idx:], dx=delta_t_)) >= abs(delta_phi_): -# return idx -# elif direction == "forward": -# for idx in range(1, len(freq_)): -# # this could be optimized to not repeat integration -# if abs(integrate.trapezoid(freq_[: idx + 1], dx=delta_t_)) >= abs( -# delta_phi_ -# ): -# return idx - - -# def _get_transition_frequency_window( -# orbital_phase, -# orbital_freq, -# delta_t, -# f_mr_transition, # orbital frequency -# num_hyb_orbits, -# keep_f_mr_transition_at_center, -# blend_using_avg_orbital_frequency, -# failsafe, -# verbose=False, -# ): -# """ -# This function first finds where the `orbital_freq` crosses the transition -# frequency `f_mr_transition`, and finds the point in time that is -# `num_hyb_orbits` orbits before that time. This marks the start of the -# hybridization window, and the orbital frequency at that time is returned - -# Parameters: -# ----------- -# orbital_phase -- Orbital phase evolution -# orbital_freq -- Orbital frequency evolution -# delta_t -- Waveform's time grid-spacing (s) -# f_mr_transition -- Inspiral to merger transition frequency -# (Hz). This should be the same (mode/orbital) -# frequency as passed for `orbital_freq`. -# num_hyb_orbits -- number of orbital cycles to blend over. -# 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). -# blend_using_avg_orbital_frequency -- If True, the orbit averaged -# frequency during the inspiral is used to -# blend modes, instead of the modes' -# frequency. -# failsafe -- If True, we make reasonable choices for the -# user, if the inputs to this method lead -# into exceptions. -# verbose -- Verbosity flag - -# Returns: -# -------- -# f_window_mr_transition -- Hybridization frequency window (in Hz). -# """ -# transition_idx = ( -# len(orbital_freq) - 1 - np.argmax(orbital_freq[::-1] < f_mr_transition) -# ) # index at which orbital_freq becomes just smaller than transition orbital -# # frequency, towards the end of waveform - -# if verbose > 4: -# print( -# f"""Transition frequency found at index {transition_idx} -# in the orbital frequency array of length {len(orbital_freq)}""" -# ) - -# if blend_using_avg_orbital_frequency: -# # We integrate `orbital_freq` to obtain `orbital_phase` -# from scipy import integrate - -# orbital_phase = integrate.cumulative_trapezoid( -# orbital_freq, dx=delta_t, initial=0 -# ) -# if len(orbital_phase) != len(orbital_freq): -# raise RuntimeError( -# f"""Something went wrong while integrating orbital frequency. -# They have different lengths: {len(orbital_freq)} and {len(orbital_phase)}.""" -# ) - -# if keep_f_mr_transition_at_center: -# # Orbital cycle based hybridization that keeps f_mr_transition at -# # the hybridization frequency window's midpoint -# if blend_using_avg_orbital_frequency: -# window_start_idx = _get_window_start( -# orbital_freq[: transition_idx + 1], -# delta_t, -# num_hyb_orbits * np.pi, -# direction="backward", -# ) -# window_end_idx = transition_idx + _get_window_start( -# orbital_freq[transition_idx:], -# delta_t, -# num_hyb_orbits * np.pi, -# direction="forward", -# ) -# # FAILSAFE mode behavior -# if window_start_idx is None: -# if failsafe: -# window_start_idx = 0 -# else: -# raise RuntimeError( -# f"""Requested number of orbits to blend over not available -# in the waveform before the transition frequency. Either decrease the number of -# orbits to blend over (currently {num_hyb_orbits}) or increase the -# inspiral-to-merger transition frequency. `window_start_idx` is None.""" -# ) -# if window_end_idx is None: -# if failsafe: -# window_end_idx = len(orbital_freq) - 1 -# else: -# raise RuntimeError( -# f"""Requested number of orbits to blend over not available -# in the waveform after the transition frequency. Either decrease the number of -# orbits to blend over (currently {num_hyb_orbits}) or decrease the -# inspiral-to-merger transition frequency. `window_end_idx` is None.""" -# ) -# else: -# # phase is always a monotonically increasing function, -# # hence using the more efficient np.searchsorted method -# window_start_idx = -1 + np.searchsorted( -# orbital_phase, orbital_phase[transition_idx] - num_hyb_orbits * np.pi -# ) -# window_end_idx = np.searchsorted( -# orbital_phase, orbital_phase[transition_idx] + num_hyb_orbits * np.pi -# ) -# f_window_mr_transition = 2 * np.min( -# [ -# orbital_freq[window_end_idx] - f_mr_transition, -# f_mr_transition - orbital_freq[window_start_idx], -# ] -# ) -# elif not keep_f_mr_transition_at_center: -# # Orbital cycle based hybridization that keeps f_mr_transition at -# # the hybridization frequency window's right end -# if blend_using_avg_orbital_frequency: -# window_start_idx = _get_window_start( -# orbital_freq[: transition_idx + 1], -# delta_t, -# 2 * num_hyb_orbits * np.pi, -# direction="backward", -# ) -# if window_start_idx is None: -# if failsafe: -# window_start_idx = 0 -# else: -# raise RuntimeError( -# f"""Requested number of orbits to blend over not available -# in the waveform before the transition frequency. Either decrease the number of -# orbits to blend over (currently {num_hyb_orbits}) or increase the -# inspiral-to-merger transition frequency. `window_start_idx` is None.""" -# ) -# else: -# # Orbital cycle based hybridization that keeps f_mr_transition -# # at the hybridization frequency window's end -# window_start_idx = ( -# np.searchsorted( -# orbital_phase, -# orbital_phase[transition_idx] - 2 * np.pi * num_hyb_orbits, -# ) -# - 1 -# ) -# f_window_mr_transition = ( -# orbital_freq[transition_idx] - orbital_freq[window_start_idx] -# ) - -# if verbose > 4: -# print( -# f"""The transition is to happen in a window from -# frequencies: [{orbital_freq[window_start_idx]}, {orbital_freq[transition_idx]}]Hz, -# between indices: [{window_start_idx}, {transition_idx}]""" -# ) -# else: -# raise RuntimeError( -# """We should never reach here. Did you set a non-bool value for the -# flag `keep_f_mr_transition_at_center`?""" -# ) - -# if verbose > 4: -# print(f"""f_window_mr_transition = {f_window_mr_transition}""") - -# return f_window_mr_transition - - def get_imr_esigma_modes_py( mass1, mass2, @@ -1190,7 +989,7 @@ def get_imr_esigma_waveform_py( Returned only if return_hybridization_info is True """ - retval = get_imr_esigma_modes( + retval = get_imr_esigma_modes_py( mass1=mass1, mass2=mass2, spin1z=spin1z, From f50aafce5b6c0b81407f239714cff36e84a393f2 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 02:59:50 +0530 Subject: [PATCH 33/36] Fixed import statement --- esigmapy/python_codes/esigma_pn_main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index 4b4d7d7..cd3d9be 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -3,7 +3,7 @@ from scipy.interpolate import CubicSpline import math from .esigma_pn_inspiral import * -from .esigma_go_terms_draft import * +from .esigma_go_terms import * import lal # Constants (LAL equivalents) From 910bb2d4161b05b485cb5240c40695d0abbb38c5 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 03:00:16 +0530 Subject: [PATCH 34/36] Updated notebook --- notebooks/esigma_python.ipynb | 1146 +++++++++++++++------------------ 1 file changed, 509 insertions(+), 637 deletions(-) diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb index ba4a651..c614ed5 100644 --- a/notebooks/esigma_python.ipynb +++ b/notebooks/esigma_python.ipynb @@ -2,21 +2,34 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", - "metadata": { - "scrolled": true - }, - "outputs": [], + "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", - "import esigmapy" + "\n", + "import esigmapy\n", + "from esigmapy.python_codes import generator_python as gp\n", + "from esigmapy.python_codes import esigma_pn_main as espn" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "b962168c", "metadata": {}, "outputs": [ @@ -24,8 +37,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "4\n", - "3\n", + "radiation PN order is 8\n", + "mode PN order is 4\n", ":/home/samanwaya/nrsur7dq4\n" ] } @@ -33,12 +46,14 @@ "source": [ "import os\n", "\n", - "os.environ[\"RadiationPNOrder\"] = '3'\n", + "os.environ[\"RadiationPNOrder\"] = '8'\n", "os.environ[\"ModePNOrder\"] = '4'\n", - "x=os.environ.get(\"ModePNOrder\")\n", - "y=os.environ.get(\"RadiationPNOrder\")\n", - "print('Poof!' if x is None else int(x))\n", - "print('Poof!' if y is None else int(y))\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)" @@ -54,26 +69,54 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "4ec5f8c9", "metadata": {}, - "outputs": [], + "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": [ - "from esigmapy.python_codes import generator_python as gp\n", - "\n", "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", + "eccentricity = 0.3 # starting eccentricity\n", "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)" + "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 ====================" ] }, { @@ -86,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "id": "a666270a-c644-473b-8559-57df6e16217c", "metadata": {}, "outputs": [ @@ -95,33 +138,15 @@ "output_type": "stream", "text": [ "================ Python ================\n", - "time taken = 0.8608009815216064 secs\n", - "3113 False\n", - "Orbital evolution took: 1.09792704499705 seconds\n", - "Modes generation took: 0.4364413240036811 seconds\n", + "Orbital evolution took: 1.5863845809999475 seconds\n", + "Modes generation took: 0.9311780489999819 seconds\n", "================= C =================\n", - "Orbital evolution took: 0.01612507500249194 seconds\n", - "Modes generation took: 0.04842561600526096 seconds\n" + "Orbital evolution took: 0.0230427660001169 seconds\n", + "Modes generation took: 0.0905804829999397 seconds\n" ] } ], "source": [ - "# from esigmapy.python_codes import generator_python as gp\n", - "\n", - "# m1 = 20.0 # masses (in solar masses)\n", - "# m2 = 30.0\n", - "# spin1z = 0.5 # dimensionless spins\n", - "# spin2z = -0.3\n", - "# eccentricity = 0.15 # starting eccentricity\n", - "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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", "print('================ Python ================')\n", "inspiral_modes_py = gp.get_inspiral_esigma_modes_py(\n", " mass1=m1,\n", @@ -152,29 +177,64 @@ " delta_t=delta_t,\n", " modes_to_use=modes_to_use,\n", " verbose=True\n", - ")\n" + ")\n", + "\n", + "# for mode in modes_to_use: \n", + "# inspiral_modes_c[mode] = inspiral_modes_c[mode][2:] # trimming the first two entries to make the arrays same in length\n", + "# inspiral_modes_py[mode] = inspiral_modes_py[mode][2:] # trimming the first entry to discard initial time flucatuations" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "id": "9fc2cdd6", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "907\n", + "908\n" + ] + }, { "data": { - "image/png": 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", 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ctnf69mWMMVYMMmOMMXYHCg8Pl+lnqmPHjnJ2drb59sWLF4vbhw4dKhsMBvPtM2bMELcnJCSYb5szZ47ctm1bWaPRyB999FGBr7djxw7xeEdO165ds/s8Fy5ckJs0aWJ1/xdffNFqWe1p2LChPHjw4Hy3R0ZGiuf5/PPPxfXNmzeL63/99Zd8/fp1cXnnzp3iNWh9jRw5UtyvZ8+ecuPGjeW7YZtlZWXJkydPlkNCQmQvLy+5c+fO8r59+8p8e5H77rtP7tatm3nZJk2aJHt4eJTLtr2Tty9jjLHi4THdjDHG7khUXks+/fRTq9LdlJQUcU5ZScryWXaGphJgLy8v821BQUGYOnWqze7eeVGzMWpy5QhqtGVLeHg4Ro4ciWrVqokxwTTmmsqGp02bJpaPxlcXxvI92fubad1QJrRWrVpiDDKVIFMG9tSpU2LcMGWCT548iREjRth9Pmq09vnnn2Pv3r1ivdG4YhrXTOXS//U2y87ORt26dbFv3z6Rbf79998xatQokfGlUvSy2F6Exmj/+++/IkNf0Lr/r7ZtaW3f0t62jDHGio+DbsYYY3ckahTm4uIimlVZooCEgihTWa4JBU0tWrSwKt+l6Z2II52labwtlfI6Im+JsMnbb78tAkwKhkzlxtSYizpdP/bYY6LEunfv3naf18/PT5QW52UqLabgy7RuqGkXBamkdevWIlil8dFUwt2gQQNcvnxZlEDb61xOnboHDhwounXTgYmkpCQRQNIyUvnyf73N6PThhx9alXPTewkNDRXvryy2FwXLVBr+wgsviOWjdWCabo3QdTooQNvyv9q2pbF9y2LbMsYYKz4+3MkYY+yORIEaBRumBlYmR48eFY24bN2/JFNjURMtei1HTpT1tIUCMmoOlnd8b8eOHfON27WlZcuWuHDhQr5xyKZGWhSgmoIyy+ZZpvmeKVv63nvvWWVL7a0TasZFY6spYDQFeJStpcBw06ZNKO9tdvHiRWRmZpo7hZfF9rp9+zZiYmIwc+ZMsQ5MJxpvnp6eLi5PmDDhP922pbF9y2LbMsYYKz7OdDPGGLsjUeBx//33W91GUzadO3cO9913n9XtNG0TldOWJOgujXJlup2CL8qgUibW5MCBA+LcXpMukzFjxmD+/Pmi5JnKgU1+/fVX8dydO3cW80dTmbHlOhg6dKi4jbLDpvJ6Wn8FNdnauXOneF6yceNG1KxZE82bNxfPsWHDBgwePBjltc2owdojjzwimrFZrsfS3l7Vq1e32T2dyuApqKf1QJns/2rbktLYvmWxbRljjBUfB92MMcbuOLdu3RIdpfNmR2k8K2UK895uyvrZyqY6ioKZkjyeUAdsKmmn0l4qA6aAjbpNT58+XWRJKXgiFND1799flFNbllTT3+mx1CGbSpmpjJiyrhQ4UVkwzeNMwRaN57XMhNJj6GSJ7kdZ4rxTXFkyje+lIJPGBlNg5urqCq1Wa/cxFOhRGTUFdmWxzei1H3jgAbG+KFtbltuL3mufPn3y3b5o0SKxri3/5ui2tbd9Hdm2pLS2b2luW8YYYyXDQTdjjLE7jr2ArKDbqYyYSnjLEzX+2rZtmwh0XnrpJZG1pCzjU089hXfeecfcXIyCKr1eD4PBkO85aIotKiGmYI3G+zZp0kQEZw8++KA52HJkbmYaL503ULNEGdItW7aIebVp6itTSTjNJz18+HCbj6Esr6lBXVlsM1ofNO6YAlAqhy6o8didum0L2r6FbdvS2r6lvW0ZY4yVjEQtzEv4HIwxxtgdyTRPM2UXa9SoIcqVKQAxZRUrM1PJN5U89+zZU2RAv/76a2zevBlbt261uY7Wr18vsqaUvS6LAxxTpkwRjdMo+0tZWVZxti1jjFVmHHQzxhirsD7++GPRvdnSwoULMXny5HJbpjvJ6dOn8dprr4kGX1SOTOOOqVza1hRd5I033hCdsWnKqtJ2/fp11KlTRwTblkEhjUGmwJHdvduWMcYqOw66GWOMMcYYY4yxMsJThjHGGGOMMcYYY2WEg27GGGOMMcYYY6yMcNDNGGOMMcYYY4yVEQ66GWOMMcYYY4yxMsJBN2OMMcYYY4wxVkY46GaMMcYYY4wxxsoIB92MMcbYXSwtLQ0vv/wygoODxRzXbdq0wbJlyxx67IkTJ3DPPfeIx9L8zU2aNMEnn3yCjIyMMl/uyri+Ca9zxhirfDTlvQCMMcYYK757770XR44cwYwZM9CoUSMsWbIEDz30EAwGA8aPH2/3cefPn0e3bt3QuHFjzJo1C/7+/ti9e7cIuo8dO4bVq1fzZinF9c3rnDHGKi9JlmW5vBeCMcYYY0W3fv16DB8+3Bz4mQwaNAjnzp3DjRs3oFarbT72/fffx2effYawsDDUr1/ffPtTTz2Fn376CQkJCfD19eXNUkrrm9c5Y4xVXlxezhhjjAGYN28eXFxc0KFDBxFAmeh0OvTs2RM1atRATEzMHbWuVq5cCU9PT4wdO9bq9kcffRSRkZE4dOiQ3cc6OTmJ8ypVqljd7uPjA5VKBWdn5wq97v7r9V3a65wxxtjdg4NuxhhjDMCwYcOwaNEihIaG4vPPPzevk9dff10EUytWrEC1atVKZV1RkRkFpI6cCnL27Fk0bdoUGo31aLFWrVqZ/27PpEmTRLD3zDPP4OrVq0hNTcXatWtFAP3cc8/Bw8OjQq+74ijJ+i7tdc4YY+zuwUE3Y4wxBqBmzZqiZJgai9GYXUINsr777jvMnDlTjH8mc+fORbt27UTW8uOPPy7Wutu1a5d4vCOn8PBwu88THx+PqlWr5rvddBv93Z46dergwIEDIlCk8nJvb2+MHDlSBIb0nkt73WVnZ4uMMN2XXqtLly7Yv38/ymvdFUdJ1ndpr3PGGGN3D26kxhhjjFmgEunff/9dBEdPPPGEaI71wgsvmP8eFBSEqVOn4rfffiv2emvfvr05OC0MdckuiCRJxfobBaQU8FEG+q+//kJAQIDISk+bNk106F6wYAFKc91R5rlu3brYt28fQkJCxP1GjRolxkFT5/T/ct3t3LkTffv2dbjbOHUoL+n6Lqt1zhhj7M7HQTdjjDGWJ6ijEuYhQ4aIIHH+/PlW64eyucRed+/Ro0dj27Zt5uvp6enYs2cPevToYb6NxgVbBnIF/lDnKWW25OfnZzO7Sk3QiK2srMnbb7+NlJQUnDx50lzW3KtXL9HF/LHHHsPEiRPRu3dvlNa6o9f48MMPzdcpu/vKK6+IkvTWrVs7tN5Ka91Rx/a829WeWrVqlcr6Lqt1zhhj7M7HQTdjjDFmgQJAU8byn3/+KVIWNm8w/tJLL+HWrVvo2rVrvhJpRzOt165dE2XJtrRs2RJLly4VWWTLAPPMmTPivEWLFnaflwK/Zs2a5RtH3LFjR3FOJdBFDQCLsu4uXryIzMxMc+d0R9Zbaa07qlagTHxRlWR9l9U6Z4wxdufjoJsxxhizQGO2KVs7dOhQNGzYsNjr5p133hFZ3FWrVuWbRqq0ysvHjBkjMrZ///03xo0bZ779119/FY/r3Llzgc9LQR6VNVP22IRKwwmVgJfVusvIyMAjjzwipi2zfO3C1ltpl+YXVUnWd1mtc8YYY3cBmqebMcYYY7K8c+dOWaPRyN7e3nLDhg0LXCWPP/64/NFHH9n829SpU+W+ffvKmZmZZb5aBw4cKPv6+so//fSTvH37dnnKlCky/bwvXrzY6n2p1WqxXCarV6+WJUmSu3TpIi9fvlzetm2b/Nlnn8menp5ys2bN5OzsbKvXoefs3bt3idddTk6OPHz4cHnixImywWAot/VWluu7NNZ5YeubMcbY3YO7lzPGGGOAmGeZspdU3ktdycPCwpCUlFTkdfPNN99g48aNWLNmDVxdXct83VIZN2WNabw0jaWmxlxUAj1hwgTzfSiG0+v1MBgM5tuoiRmNoaYO2lTOPWLECJGxfeqpp7B7926rOaMpM2sqyy7JuqPXp3HLlMGmpmGWjcf+6/VWluu7pOu8sPXNGGPs7iJR5F3eC8EYY4yVJ61WK8YJX79+HcePH8fly5dFA68tW7ZgwIABVvc1zQFNcy3XqFFDlEjT9FQUSFJ59c8//4zt27ejSpUqqCjWr18vAsRTp06Jcc3FXXdTpkwRpeMUXFsG1hV1vZXF+maMMXb34aCbMcZYpUdZxx9//FE06aL5o6lztq+vL7p37y4ymtTQyxQkUiaXpgyztHDhQkyePBk+Pj7IysqyarK1Y8cOc6Osu9Ubb7yBiIgILFmypNjrjoJyampGly3Ham/YsEFMo1UR11tZrG/GGGN3Hw66GWOMVWrLli3DQw89hP/973947rnnzLfPnj0bM2bMwO3bt5GcnAw3N7dyXc47Ea87xhhjrHAcdDPGGGOMMcYYY2WEG6kxxhhjjDHGGGNlhINuxhhjjDHGGGOsjHDQzRhjjDHGGGOMlREOuhljjDHGGGOMsTLCQTdjjDHGGGOMMVZGOOhmjDHGCpCWloaXX34ZwcHBYo7pNm3aiKmyHHHy5EkMHz4ctWrVElOOVa1aVcxbvXjx4mKtc5q6TKVS4dtvvy2XbZaamoo333wTgwYNQkBAACRJEvOWl8X63Llzp3h+W6eDBw8Wa/nptadMmYIaNWqIOcHr1auHO9n27dvx2GOPoUmTJvDw8BDLPXr0aBw7dqzUP6tFefyiRYvEdjh69KjN5xkxYoSYk50xxphCYzxnjDHGmA333nsvjhw5IubsbtSoEZYsWSLm9TYYDBg/fnyB6ywpKQk1a9YU96eAKT09HX/88QceeeQRhIeH4/333y/SOqcgR5ZldOzYsVy2VXx8PH766Se0bt0a99xzD37++ecyX5+ff/45+vbta3VbixYtirX8r776Kv7++2/MmTMHtWvXRpUqVXAnmzt3rljnL730Epo1a4a4uDjMnDkTXbp0waZNm9CvX79S+6yWxuMZY4zZITPGGGPMpnXr1sn0U7lkyRKr2wcOHCgHBwfLOp2uWGuuc+fOcs2aNYv8uBkzZshqtVpOS0srly1mMBjEicTFxYl189FHH5XJ+tyxY4e474oVK0pl2bOzs2VPT0/5jTfekO8WMTEx+W5LTU2Vq1WrJvfv379UP6tFefzChQvFfY8cOWLzuYYPHy7Xrl3boffIGGOVAZeXM8YYuyPs3btXlC1T9tHX11eUZYeGhpbrMq1cuRKenp4YO3as1e2PPvooIiMjcejQoWI9r7+/vyhvLirKQjZt2lSUGpcHU3n3nbY+C0PP7+LiIsqnv/rqK/EeKFt8pwsMDMx3G60/ynrfvHmzVNdtWW4be8ME6EQVH4wxVtFx0M0YY6zc0bjg3r17i1LspUuXirJlCir69+8vAqXioDJsnU7n0Mmes2fPiiA3b4DcqlUr898dQeW59DpUHkylzVQa/NZbbxX5PVF5eXFLy0tjfZRUcdbnc889J+7v7e2NwYMHi4MzRUXr+p133hGX16xZgwMHDuD3339HWSqr9U3j+o8fP47mzZuX6me1OI/X6/U23w+9d0u0vi1PNFadhltUr15d9DlgjLGKjoNuxhhj5Wrt2rWYOnWqGEe6YMECDBs2DPfdd58Ye0uB9+rVq833paCVMuCU6aUxp1u2bLH7vLt27YKTk5NDJ3vZNhpPaysoMN1Gf3fEs88+K16HMpevvPIKZs+ejaeeegpFcfv2bVy/ft0q6P6v10dJFWV9UsUDjWWeN28eduzYge+++058Hvr06SMOWhQFNSKjgzdUQTFy5EiR5W7YsKFo9lWcIN4RZbW+6SAE9QZ47733SvWzWpzH03q09X7Wr1+f736mE31+aVvSwYN169aJgymMMVbRcSM1xhhj5erDDz9E/fr1RYBlmfWrW7eu6Ph99epVq4CDsmMUbG7duhUPPPAAwsLC4Ofnl+9527dvL8qxHUHdmu0pqJza0VLrd999F0888QRiY2Px77//4vnnnxeB0+uvvw5Hmd5Lhw4dynV9lJSj67Nt27biZNKzZ0+MGTMGLVu2FB3UKetdFNTxm9bBf6Us1vcHH3wgGvF9//33Nt9LST+rRX38b7/9JrLjedGBpbzl7yb02adgm/4ftGvXrtBlYoyxioCDbsYYY+UmOjoaJ06cEJdpzK0tPj4+4pwylatWrcKVK1fg7u6OUaNGiS7alAmnaZXyovGpNOWRI+yNr6bg1VaGLyEhQZw7WhpLU4bRiVAmn1C586RJk8TUW46Wljs7O5vLfctjfZRUSdcnfRZoOqoff/wRmZmZ4qCMI6gMmqZve+GFF+ze59y5c3j66adx5swZcRCIqhG6d+8uurXT9GXUyZueh5aBuqBTdcaNGzdE8EsHU/IGpaW9vun1pk2bhs8++0wErqW9bovzeAq4LQ8CWVYp2Aq6aflp21FFy5AhQwpcHsYYq0i4vJwxxli5Me2Y07zTlBW0dXr44YfFfaipGgUyNO7bhLKeFCyVVXkvPf+FCxfyjbulwKwkU1d16tRJPKdlFt+RoJsCbtPBifJYHyVVGuvTNF64KA3d6DUzMjLsZrpzcnJE2fn9998vqgYok07XExMT0atXL+zZs0fcjw4Q0UES0/Xdu3ejR48eNpelNNc3BdzU94BOVDVRFuu2rD7rlnN7U6ae3oOtg0KMMVaRcaabMcZYuTFlzyhosZUxs0SZ3bzjP+k6jXUuq/JeKmeeP3++GF8+btw48+2//vqreEznzp1RHDRGWaVSoV69eg4/ht7L6NGjy3V9lFRJ1ycFwdQDgDLIrq6uRTpgQewF3dSZm5rd0RAHQss2a9YsbNy4UcxTnZ2djWvXrokge8qUKfjhhx+g1WrFdSp7L8v1/emnn4pAleZ0/+ijj8ps3ZbVZ53QeqT1RsF2Qe+BMcYqKg66GWOMlRsq4+3bt68IKCiIpB17ymRGRUWJwJTKr6lxFqGsbkpKitXj6TrdbouXl1ehgXxhhg4dioEDB+KZZ54Rr9WgQQPRXZ2CiMWLF0OtVltlNqnbOo1RpxN58sknRSBMme1q1aqJgHjFihVYvnw53njjjXyl5XTwgbq4UzmzJVofdLJsolYe64Ns2LBBjEdPTU0V18+fP4+//vrLXDpPpe721kdR1uf48eNFST4tM02xRpn9mTNnIiYmRmRNHVlvluO5qSzc3kEOmhLLsmKA1K5dW9xOKJtN2W060Xbbv3+/eE66TsFkWa1ver+07qgUmxrmHTx40OrvltOelfSzWpTHFwUdrKBpyGjd0/Rjed8Djdu3N7SEMcYqCg66GWOMlSsal0ydy6kpE435pHG6FGxRWa/lmFjqNk2B+a1btxASEmKexuiRRx4p0+X7559/RKdoCk5ofCt1wqZg5MEHH7S6Hx0soDG/lDE16dq1KxYuXCiyhUlJSSIgpnHXNF2VqWzexDQ1WlBQkENN1MprfVBQRl3UTeggAp1MARZ1BLe3PoqyPqmUng5O0Bhgep9UFUHBL607y4MPBa03EwqQC2raRZncvGOQaby2qbKAstkUqNLz0Dag6/Q+aN1bNnsrbdRsjFDgS6e88k7NVZLPalEeXxT0WaFtdPnyZZtVAZafGcYYq6gkOe83NmOMMXaHoowZNWmi7s3btm0TASZlQCkTerejaZaoSdipU6fE+NrKvj7Kcr2ZULBHWVyqRGjcuDFee+010Uxt5cqVokqBxtzTNGNUnt6vXz9RMk4VGJStHTRokKjMKGiaNsYYY4xwIzXGGGN3jTlz5oiSX+q0TNMSUSa0ogSYFMxRRrEogWNFXh9lud7yoq7w1PWdsrq0LqdPn441a9aIgJuYstmmTC0F35QptjeemzHGGLPEmW7GGGOMMcYYY6yMcKabMcYYY4wxxhgrIxx0M8YYY4wxxhhjZYSDbsYYY4wxxhhjrIxw0M0YY4wxxhhjjJURDroZY4wxxhhjjLEywkE3Y4wxxhhjjDFWRjjoZowxxhhjjDHGyggH3YwxxlgBDAYDPD098dprr5X7evr5558hSZJYHlvS0tLw8ssvIzg4GK6urmjTpg2WLVtW4vsW9/GLFi0Sy0unnTt35nsOWZbRoEED8fc+ffqgsijJuqf1aFqneU8HDx4032/79u147LHH0KRJE3h4eKBGjRoYPXo0jh07VobvjDHGmC0am7cyxhhjTDh37hzS09PRsWPHcl0jEREReP3110WglpycbPM+9957L44cOYIZM2agUaNGWLJkCR566CFx4GD8+PHFvm9JX8vLywsLFizIF1jv2rULV65cEX+vTEq67snnn3+Ovn37Wt3WokUL8+W5c+ciPj4eL730Epo1a4a4uDjMnDkTXbp0waZNm9CvX79Sf1+MMcbskBljjDFm188//yzTz2VYWFi5rqURI0bII0eOlCdNmiR7eHjk+/u6devEci5ZssTq9oEDB8rBwcGyTqcr1n1tcfTxCxcuFPd74oknZDc3Nzk5Odnq/g8//LDctWtXuXnz5nLv3r3lyqCk637Hjh3i8StWrCjwfjExMfluS01NlatVqyb379+/mEvPGGOsOLi8nDHGGDOaP38+WrZsKUp+KWtIGcHDhw/D19cX9evXL7f1tHjxYpEVnjNnjt37rFy5UpSdjx071ur2Rx99FJGRkTh06FCx7lvS1yKUxSVLly4130bZ+r///luUQNvy8ccfi5LpEydOiMywt7c3qlSpgocfflhkbS1dvHhRvEa1atXg4uKCWrVqYeLEicjOzoajJk+ebLdsvzSVdN07KjAwMN9t9LqU9b5582apvAZjjDHHcNDNGGOMAWKM7Ysvvoh77rkHGzZswHPPPYdJkyaJyx06dCjyOqLxyjqdzqFTQWJjY8WyUSlySEiI3fudPXsWTZs2hUZjPXKsVatW5r8X574lfS1CAfP999+PX375xXwbBeAqlQrjxo0r8LXGjBkjxn3/9ddfIhBftWoVBg8eDK1WK/5+6tQpUfpP45k/+eQTsb2mT58uAu6cnByUptLYpiVd9yb0+aTnoHVL62Pv3r2FPoYOdBw/fhzNmzd36DUYY4yVDh7TzRhjrNKjjOt3330nmlmZgkAaL5uUlIR3330XjzzyiHkdhYWFieZU1AyLMuL2UGY675hbe65du4Y6derY/Nuzzz6Lxo0b45lnninwOWj8br169fLdXrVqVfPfi3Pfkr6WCWW0aX3QGHkK+igAp2xvYeO5Kcv95ZdfisuDBg0S2ewJEybgzz//FOevvvqqCD6pIiEgIMD8OPpbaSuNbVrSdU/ZfhqnTePj/fz8xOfxq6++EtfXrVsnAvCCAnXqT/Dee+859B4YY4yVjkoZdO/evVv8QFEHz6ioKFHqRZmNskJH3P/55x9R/ubm5oZu3brhiy++EDtRhI7Wv//++1i/fj2uXr0qflAHDBggshrUMIcxxljZ+vTTT0W2NG/WlUpxiWWm+/Tp0+L7u6CAm7Rv3140y3KEve96Ohjw77//ihJrKrUuTEH3yfu3oty3pK9FevfuLUr0KdimUm5aN9TYqzB5g+cHHnhAVCDs2LFDZMEpEH788cetAu6yUhrbtKTrvm3btuJk0rNnT7EeaFjEm2++aTfo/uCDD/DHH3/g+++/F++DMcbYf6dSBt10lLd169Zi/NR9991X5q9HOwR0dJl26KjkjI4w09H68+fPi2k8MjIyRLkX/SDSciUmJopSwlGjRuHo0aNlvnyMMVaZRUdHixLlb7/9Nt/fbt26Jc4tO5fTfem7ujA0fpamgnJE3lJjQpl0+u144YUXRABHWXdiKpmm605OTuJ3hFDW01aWNCEhwSqTWtT72lKcx1MwSb+7s2fPRlZWlujaTQFjYapXr55vXZlen34v9Xp9gWX3pamk27Q01r0tPj4+GDFiBH788UdkZmaKA/yWpk6dimnTpuGzzz7D888/X+TnZ4wxVjKVckz30KFDxY8PlazZQjs0dLSY5rSknZnOnTvbnF/UURs3bhRH9amcjnbUFi5ciBs3bpjnyqTM9pYtW8TRe8qe0HQedCSa/k73Y4wxVnZMgXVQUFC+v9FUThT0WQZ1FHSbxt8WdsCVgmJHTuHh4fkef/v2bcTExIhsMDVyM51oLDQdPKbLlllgynReuHAh33jiM2fO5JtOqij3taW4j6ffQnpfFBxSAO7oQRFL9JoUtFLwSgGqWq02b8OScCS7X9JtWhrrvqDx5rbeBwXcNBaeTjRUgjHG2H+vUma6C0M7AvRjSWP7KLtA5edDhgwRP4gNGzYs8fOb5lct6Gg23Yd+OOnoNWOMsbJjKkumBlaW5eXUuGv//v0ig2iJysufeuqpMi9FpmCfSqjzoqFHFPxRwzB/f3/z7VRiTN3XqSTd8n38+uuv4vnpAHJx7mtLcR9PB7PfeOMNMdyKSsQdQSXRluXQNJabAlYaw0wZXSpbX7FihcjiWq6P4nwOKEtMc2VTg7eyKi8v6bq3hTL+a9euFVl4y2EPNGyCgm0awvbRRx8V+XkZY4yVErmSo1WwcuVK83Wah1WSJDkiIsLqfjSn5TvvvFPi1zMYDGKe1R49eti9T2Zmpty+fXt5woQJJX49xhhjhX8vd+zYUcx9/d1334l5kKdOnSpXrVpV/EbQZROaZ9rWb8R/yd483aa5nn19feWffvpJ3r59uzxlyhTxHhYvXlys++7cuVNWq9VW66AojzfN033kyJEC35Otebo/+ugj8djatWvLb7zxhrx582b522+/lT09PeXWrVvL2dnZ4n4nT54Ut9WrV8+8LEuXLpUfeughOSUlxfx89FwFzQW+atUqcZ9PPvlE3rdvX6HzZZeEo9vJ1vqn9/XWW2+Jebrps0rP0bhxY1mj0chbtmwx3+/rr78WzzlkyBD5wIED+U6MMcb+Oxx05wm6//zzT/EjRTs0lif6MXvggQfEfa5duybuU9Dpueees7nCn332WbEDcfPmTZt/z8nJkUePHi23bdtW7Nwxxhgre/S9TsEJBW8+Pj7i4OiCBQvE9/m6devM99uzZ4/s5+dXrpukoKA7NTVVfvHFF+Xq1avLzs7OcqtWrUQAWtz7UlBH64AC4OI8vjSC7mPHjontQdvGy8tLBJ0xMTFW9z1//rw8duxYsW1oWWrVqiVPnjxZzsrKMi8rPdeDDz5Y4MEXCmaDgoLEfRMTE+Wy4uh2srX+p0+fLrdp00auUqWKCMgDAgLkMWPGyIcPH7Z6LK3PgvZTGGOM/Xck+geVGJVwW3YvX758uRgjR1Oa0DixvA1UqNyPuo1fuXKlwOelsXY0rYklaoZD84tS9/S6devmeww9L43rpg7m27dvF+PVGGOM3TnmzJkjypupV4cJlSI7OzuX63JVRFQWTeOR4+LiSlQ2Tmh2EBomQOPxaUw1Y4wx9l/iMd150DQc1Ak1NjbWbldVapBCc7Q6io5rUMBNwT01ZCso4A4NDRVj+DjgZoyxOw8FbTSe2rI7NAVzNK0Xu3PR7+qDDz7IATdjjLFyUSmDbpqGJSwszHz92rVrOHnypGhsRlOYUKZ74sSJomMsBeHUaZUyz3R0fNiwYUV+PZryhTrgrl69Gl5eXuZOrNS1nHbcqCHM/fffL6YNo0YoFPSb7kPLxBkUxhi7M8ybN0+c2N3lq6++Ku9FYIwxVolVyvJyyjb37ds33+3USXXRokUi60xTiv3222+IiIgQWeeuXbuKMrfilKXZm4aEpg6j6VOoU7qt7Lfp6Dx1aGWMMcYYY4wxdveplEE3Y4wxxhhjjDH2X7A9ESVjjDHGGGOMMcZKjINuxhhjjDHGGGOsjFSqRmoGgwGRkZGimZm9cdaMMcYYY4wxxlhhaKR2amoqgoODxRSi9lSqoJsC7po1a5b3YjDGGGOMMcYYqyBu3ryJkJAQu3+vVEE3ZbhNK8Xb27u8F4eVIepAv3nzZgwaNEjMq85YRcafd1bZ8GeeVTb8mWeVjfYu2ZdPSUkRSV1TnGlPpQq6TSXlFHBz0F3x/6O6u7uL7Xwn/0dlrDTw551VNvyZZ5UNf+ZZZaO9y/blCxu6zI3UGGOMMcYYY4yxMsJBN2OMMcYYY4wxVkY46GaMMcYYY4wxxspIpRrT7Si9Xi/GEbD/Fo3XUKvVvNoZY4wxxhhjFQYH3XnmWYuOjkZSUlL5bZFKzsfHB9WrV+d51BljjDHGGGMVAgfdFkwBd2BgoOiWV1gXOla6BzwyMjIQGxsrrgcFBfHqZYwxxhhjjN31OOi2KCk3Bdx+fn7lu1UqKTc3N3FOgTdtBy41Z4wxxhhjjN3t7tpGatOnTxeZ6JdffrlUns80hpsy3Kz8mNY/j6lnjDHGGGOMVQR3Zab7yJEj+Omnn9CqVatSf24uKS9fvP4ZY4wxxhirpL5uBKTFQDVoOoAaqCjuukx3WloaJkyYgPnz58PX17e8F4cxxhhjjDHGWGlIixFnUuSxCrU+77pM93PPPYfhw4djwIABmDZtWoH3zc7OFieTlJQUc+ly3vJluk7NvAwGgzhVdlOnTsXq1atx/Pjx//R1ad3TdqDtUZIx3abty2XqrDLgzzurbPgzzyob/syzysLJeG6QnO+KfXlHl++uCrqXLVsmgkAqL3d03DcFj3lt3rw539htjUYjpqqiTHpOTg7uJs8++yyWLl1qfh81atTAyJEj8fbbb8PDw6PQx1PFwOLFi8XBDBM6WEHN5UwHKv4rtO4zMzOxe/du6HS6Ej/fli1bSmW5GLsb8OedVTb8mWeVDX/mWUU32nh+IyoWqHnnf+Zp9qUKFXTfvHkTL730kgiYXV1dHXrMO++8g1dffdV8nQLImjVrYtCgQfD29ra6b1ZWlngNT09Ph5//TuHk5ITBgwfjl19+EUdb9uzZgyeffFJcnjNnjsOdwy3XiYuLi8g0511PZY22Ay1Lr169SrQd6L3Tf9KBAweK9cNYRcafd1bZ8GeeVTb8mWeVgkEHnFAu1qzXCGe0uOP35R1NUN41QfexY8fEVFLt27c330aZWMqI/u9//xOZ2bzlyBQ40ikv2nB5Nx49FzXxUqlU4nQ3oeWmADU4OFhcr127Nnbt2iXKw7du3Yqnn34ar7/+uvn+Z8+eFU3oQkND0b9/f3HbfffdZ35seHi4uaHZH3/8gQ8++ACJiYkYOnSoGEvv5eUl/kbr/I033hAVCPSB69ChA7799lt07NhR/H3nzp3o27evWIa33noL58+fR5s2bbBw4UI0btzY5nuhdU+vbWsbFUdpPQ9jdwP+vLPKhj/zrLLhzzyr0DLTzBdVTm6A9s7/zDu6bHdNdEnB4ZkzZ3Dy5EnziYI8aqpGl0t7TmcaV5yRoyuXE712SVG2mI6KPvbYYyLItUQZ8Z49e6J+/frmUn26T1RUlFXp/pUrV7Bq1SqsXbtWnCiQnzFjhvnvb775Jv7++2/8+uuvouy/QYMGIuOekJBg9XrvvfceZs6ciaNHj4ryd1omxhhjjDHGGDPLTjVf3HAuBjl6VBh3TaabsqstWrSwuo3GK/v5+eW7vTRkavVo9uEmlIfznwyGu3PxN83hw4exZMkScaDi0UcfxYcffihu69SpkwjEafz2V199Je4bEBAgzn18fMSY9rxNzRYtWmTObD/yyCPYtm0bPvvsM6Snp2Pu3Lni75QBJ5QFp5LuBQsWiAy4Cd2/d+/e4jKNM6ex41RGfreV8TPGGGOMMcbKSHZupvtsnA6yj1J5WxHcNZluVjDKRJvGo3ft2lWMif7+++8RFBQkglzKbpvuRwHv2LFjC12lderUMQfchJ6LSvxNWXAK4Lt3725VXkGB/YULF6yex3I+dXoOYnoexhhjjDHGGINPTczwm4ZHc97Aj/pR0Je8+PeOcddkum2hMcNlxc1JLTLO5YFeu6ho7DRlninwpbHdluMLnnjiCZGlpvHWVEY+bty4fN3bHRmjQGOtTdOpmUrgTWO/Tej2vLdZPo/pbzwtG2OMMcYYY8zMxQvHnTrgsEEZqmrgoLvio+CwJCXe/zUqtacx1bYMGzZM/J2C8g0bNojmc3mDYmokVxT0Ws7Ozti7dy/Gjx8vbqPMN43bfvnll0vwThhjjDHGGGOVkdaY4CO5l+5+XF5eCVCTucmTJ4sp1ChYpvLzvGXkNFY7OjpadCl3BAXxzzzzjBi7vXHjRtGZfMqUKWKuuscff7yM3gljjDHGGGOsQoq9iKkJbyHcdTyeVa9GKfSWvmNw0F1JUCCck5Njs3M4dRanBmg0h3nbtm0dfk7qZE5TjVHpert27RAWFoZNmzbB19e3lJeeMcYYY4wxVqFd3YlWujPiYh0pukKVl3PQXQFQB3Ga2qsgNB0YTdc1ceLEfH8bOXKkmLObysNpjm7y8ccfi6nYLFHZuOnvhJq2zZ49G3FxcaI5G5Wam+boJn369BFjvKkzugnN0023UXadMcYYY4wxxoSc3CnDNJK+QpWX3z2DllmxZGdn4+bNm/jggw/wwAMPoFq1arwmGWOMMcYYY3fsPN0a6DnTze4eS5cuRePGjZGcnIwvv/yyvBeHMcYYY4wxxgqcp5uDbnZXoQZq1Jn82LFjqFGjRnkvDmOMMcYYY4zll5NuvujEmW7GGGOMMcYYY6wU6XPMF9XQowL1UeNGaowxxhhjjDHGyplBZ76YBWfoK1DUzY3UGGOMMcYYY4yVr24v4uWz9XBCVwvX5eq4R9ZXmC3CQTdjjDHGGGOMsfJVsyPWGeKglZUUN8/TzRhjjDHGGGOMlRJZlqG1qCnneboZY4wxxhhjjLFSYriyE29rlmCA6jgOG5pgr/xohVm3XF7OGGOMMcYYY6xcSbu/xNOa/eKyFhrsrkCN1FTlvQCsdERHR+OFF15AvXr14OLigpo1a2LkyJHYtm0br2LGGGOMMcbYHU3Wa62nDJMlVBSc6a4AwsPD0b17d/j4+ODLL79Eq1atoNVqsWnTJjz33HO4ePFieS8iY4wxxhhjjNkl63OnDNNAz2O62Z3l2WefhSRJOHz4MDw8PMy3N2/eHI899li5LhtjjDHGGGOMFUa2mKfbiYLuClRezpnuwuSk2/+bpAacXB28rwpwciv8vs65QbMjEhISsHHjRnz22WdWAbcJZb8ZY4wxxhhj7I5myA261RIH3ZXL58H2/9ZwEDBhRe71rxoA2gzb963dA3h0Xe71WS2BjPj89/s4uUiLFxYWJtrrN2nSpEiPY4wxxhhjjLE7hl5rnelGxcGN1O5yFHATKi9njDHGGGOMsbu9vFwPFYxhToXA5eWFeTey4PJyS2+EFXDfPMc3Xj6D0tCwYUMRcF+4cAH33HNPqTwnY4wxxhhjjP2XYru8h1lrj2KHvg3iUQXd5YqT6+aguzBFGWNdVvctQNWqVTF48GD88MMPePHFF/ON605KSuJx3YwxxhhjjLE7WlKtwfhLnxvLVKRMN5eXVwBz5syBXq9Hp06d8PfffyM0NFRkvmfPno2uXbuW9+IxxhhjjDHGWIG0euvMdsXJc3Omu0KoW7cujh8/LjqYv/baa4iKikJAQADat2+PuXPnlvfiMcYYY4wxxliBPG7swCjVMYxV70Q2nPCT4VVUFFxeXkEEBQXhf//7nzgxxhhjjDHG2N2k3q4XMds51Xz9pwpUX87l5YwxxhhjjDHGypUk6/Ncz+1mfrfjoJsxxhhjjDHGWPkyWAfZKlgH4XczDroZY4wxxhhjjN1hmW4DKgoOuhljjDHGGGOMlR9ZhipP0K3i8nLGGGOMMcYYY6z0S8sJl5dXYAZDxSljuBvx+meMMcYYY6ySMeQPuiVDxRnTzVOGGTk7O0OlUiEyMlLMcU3XJUkq361TiciyjJycHMTFxYntQOufMcYYY4wxVgmoNDjS4gOsOXETf+l7IQvOaOSCCoODbiMK9OrWrYuoqCgReLPy4e7ujlq1aontwRhjjDHGGKsE1E64GDIWvx89a77JUIEaqXHQbYGyqxTw6XQ66PUVp5zhbqFWq6HRaLjCgDHGGGOMsYpmy0dIvH4GZ3v+gJ6Ng/L9WadXgmyVRAE3IKPiVB1z0J0HlZQ7OTmJE2OMMcYYY4yxEpJlYN8s+AKY/+tCtP7wDXi7WsRb2iwExh1AZykSI5yOwFdOwlLDuAqz2jnoZowxxhhjjDFWdjITzRd9kYr4tBzroDs9FsNPPoP+zk5IkHwQjDislYdVmC3CA2cZY4wxxhhjjJWdtBjzxepSIhIzcmx2L9dBDb0xL8zzdJeD6dOno2PHjvDy8kJgYCDuueceXLp0qTwWhTHGGGOMMcZYMYLuTDgjKV/QrfTT0kMFg6QWl1WoOI3U7ppM965du/Dcc8/h4MGD2LJli2h2NmjQIKSnp5f3ojHGGGOMMcYYs6dWN7wQsBADs7/Eb/rBSEzX2s90S0qmWy1XnMbWd82Y7o0bN1pdX7hwoch4Hzt2DL169Sq35WKMMcYYY4wxVgCNM/YneCFeVibfLqi83JzplpXbKoK7JujOKzk5WZxXrVrV7n2ys7PFySQlJUWca7VacWIVl2n78nZmlQF/3lllw595VtnwZ57d7WRZRlJmbvwVn5pltZ8uZWeKwJSCbtkYdEuQ7/h9eUeXT5JpDRRizZo1RV6AgQMHws3NDWWBFnn06NFITEzEnj177N7v448/xtSpU/PdvmTJEri7u5fJsjHGGGOMMcYYy1UjdgcOXY/HM+p/kQo3vOk9E8MaeJj/7psehl6XP8ENQwAy1N5oIl/Bq3gNvdu2vqNXY0ZGBsaPHy8Swt7e3iULulUqVZHnug4NDUW9evVQFmhs97p167B3716EhIQUKdNds2ZN3L59u8CVwu5+dNSJxv7TwR+ec51VdPx5Z5UNf+ZZZcOfeXa3M/w6Gi639pmvv1NrCT55ZFDuHVKjsHH5XOy7lYOUeiOxOzQO7i7O2PnWnb0vT/Glv79/oUG3w+Xl0dHRYgy1I6jDeFl54YUXROZ99+7dBQbcxMXFRZzyog13J288Vnp4W7PKhD/vrLLhzzyrbPgzz+5W2vRYq+tZWenW8VjVWtjhNw7/XI/AcFdvpCEdTrJ8x3/mHV02h4LuSZMmFalU/OGHHy71TDIl5CngXrlyJXbu3Im6deuW6vMzxhhjjDHGGCsDOanWVzPyz0ClNygF2C4apcq64kwY5mDQTZ3Ci2Lu3Lkoi5JyGou9evVqkUmnzDupUqVKmY0dZ4wxxhhjjDFWMpI20+p6TlaeoDsjATXTz6KhlIEBiXvQWXMBq+R+FWa13zXdy02BfJ8+ffIdEJg8eXI5LRVjjDHGGGOMsYKodErQnQJ3eCMD2uw8QfeNg3j95vMY4FQfVdK8UVdzAof1LStn0E0DxKm8mzqGh4eHi25tAQEBaNu2LQYPHoxu3bqV2YI60O+NMcYYY4wxxth/ISsZKw5cRLzKH0/3rm//fgY9VAZlXu5UyRvecgac9FnQ6Q3QqFVW83RroYYkqSpc/OdQW/KoqChMmTIFQUFB+OSTT5Ceno42bdqgf//+opnZjh07RJfoZs2aYfny5WW/1IwxxhhjjDHGyo3ul+EYtXM41m9ch0vRqQXcMct8McK5Hi4ZQpANJ+ToLUZtG4NuPQXdxpmzaJ7uSpXpbt26NSZOnIjDhw+jRYsWNu+TmZmJVatW4ZtvvsHNmzfx+uuvl/ayMsYYY4wxxhgrbwY9NLFnoJGAH52/xaaw4Whc3c4MVhpX/N1tNX7ecR5163fE+nNKJ/MsrQHuzrnPR3SyCjBmulHZgu5z586JMvKCUDOzhx56SJzi4uJKa/kYY4wxxhhjjN1JVGos6r4NE/YOQrCUgPMXLwA97JSYq9SIdgrBBTkdLd2c4aSWoNXLyNYpgbbdTLdsqFzl5YUF3CW9P2OMMcYYY4yxu8fROBVi4SMu37xxrcAx2OnZSlDt7qyBi0ZtznSbWYzpVlXWTLcJrUhqoFazZk1oNBrk5OSIxmrZ2dkYNmwY/P39y25JGWOMMcYYY4yVv+w0nI9IQpzsgxpSPLx18UjJ0qGKm1P++ybfQpdrPyBdLaHfbU9MlFZjsbovsnU9c+9j0IqzSj2mm1y6dEl0KKfx2vXq1cPmzZsxduxYXLx4UQTj7u7u2L9/Pxo2bFi2S8wYY4wxxhhjrNzIG9/GhrRlcFEpwXKglITE9BzbQXfSTfSK+Q011EFINfRHPdxCoJSIbMtMd3Bb/OH+MA4k+SKz7Tjcs+a0mF7sswrSwdyh8nLy1ltviYZqJ0+exIgRI8SJOpcnJiaKU/fu3UVnc8YYY4wxxhhjd4msFITPexC3prXE0l2nHHqI/nYYXCQtUmQ3cT1ASkJ8ujItWD7aDHGWDWdIzu7ishuykaW1GNMd3BZLXB/EWkNXSO5VEQcfcX+9oWIE3Q5nuimLTdntli1bYtq0afjuu+8wb948ODk5mYPyBx98sCyXlTHGGGOMMcZYadrwJupEbRAXd2/6Gx2b1kODQDudyE2Sb4mzr+RHoPOpj60xXmhmN+jOFGeZcIazMeh2hRbZOutGaaYA2zTm2/K2SpPpTktLQ9WqVcVlDw8PcaJ5u00o6x0TE1M2S8kYY4wxxhhjrHDRZ5G581tci012aG0ZwvebLzeSbiEsNr3Qx0iZieL8smtrRPm0E5lpKi8vaJ7uTNkZGhdjplvKk+lOjUFN7VUEIhG1b/yNjzS/ooN0EfrKVl4eHByMGzdumK9/+eWXCAwMNF+nacJ8fX1LfwkZY4wxxhhjjBUuPR74sTvcdn6ML2Z9g7DYtILvr9dCSlGy1qSOKhrh8YUE3bocqLXK86o9qqKqhzLZdmHl5ZlwgdrVQ1x2Q451pvv4b5if/hJe1vyFgKhdeFSzCU1UN6GvILOGORx0DxgwQDRNM3nmmWfg5ZVbdkCl5+3atSv9JWSMMcYYY4yxSu7nPVfx2p+noCsoEt39pflic1U4zkYUku1OugFJVjLOXbO+xyvaZxF+u5CgOytJnBlkCT7uzhiU/i+eVq9BYkbB5eXZoEy3EnS7Its66LaYp1tl0b3cIFeyMd0//vhjgX8fN24cJk2aVBrLxBhjjDHGGGPMKD0+Ars3LEOE7I8DbYPRs2GA7XUTfcZ8sZYUi/B4Jctsl3cwFjf/CbtPXECaSzUgW4drhQXdGQniLBkeCHSTMSTsawzUSHgr9emCM92yM9TuvojXBCJe721dXm4MunUi6FbGdKsgQ1fZxnQXpm7dulZjvBljjDHGGGOMFSwlS4vkDGXqLXsS1n+K35y/wL3qPYhOVsZI26Lt9ir2GZqLy/WkSNxIKCTodnLD7sz62GzoiH5NlaHD1wsL1NVOuOjbB7sMreDi5afcJMlIT7OTVW/7CB7ADHyvHwN9/QGYWv9PvKx93mamW2cxT7cKBhg46GaMMcYYY4wxVlz6dW/g3JcDcd+365CerQSe+VzejKCrK8TFhlJEgUFxdEA3fKidjGxZAx00uJFQeFO0iCSl/HuKtArfO82GX+pFZOZYZKHz8quPBcGfiMDZ28sLBkkpns5OtxN0e/jjpLYObsjV4OqkhotGCaqzdXqb5eWmoFui6xWkvLzUMt2MMcYYY4wxxpSx0ukJkcjJMy1WvoZkR35CV8MJTM/+DOvORNm+36X10MhKUNpAiiiw0VlUchauykFomr0IY3I+KTzTfWoZeqf8i2DcRp3EgxipPigy5LGp9rPpJMHYNK2qpwsMzp7isjZD6Wiel94gI8c4Dp2CbjqRLG3+TLeWgm7JFHRzppsxxhhjjDHGWF7pt2GY0w1Js3vhyUUH7K+f1EjzxY6qy9hw+Lzt+92+bL5YW4pB5O0kO68bD5xfheZSOBpVryJuiknJMzVXHvLRhXhTNw+tVFeh8lLGiftJKeag2ia9FvFp2eIidS6XXbyV58pKtXl37dnVeEa9Bi2lq3DLiMDToVPwp/NUO5lulTno5jHdjDHGGGOMMcbyC9sGVU4qaiAOF8KuIsleV+/kCKurt27dsDmGWTbOiW0aO21IuArZVtl11Al0OvIKvnKah2ZB3vByUcq+byXaz3Yb0m+L80TZC87e1RwLund8hr9uj8LrmuXwdXcGjEG3JifV5nJJ5/7BW07L0E4VChe1hBrp59FSuoZsy0x3/X74yTAKRwyNkdLzAwyVZ2O5vm+F6V7O5eWMMcYYY4yxIjNc3IjfVq7FyhO3bAeBlZT+yg5x/qNuJGJQFSdu2MlMpyiZbhp/PSj7C1w3BIimannJWSlW112yk5Boq/FailKeHiNXxX2pf2CF+l2MUu1HfFoBAXRGvDjLdKoCjZfSSM0PyQUH3RkJ0ECPHNkJ3m4aqFyVoNsTGci0kVU3GKcM06lcoHJ2F5fdpBxkay3GsDcZjq/0D2GfoSVUXtURqaqOFHhUvnm6HXoylQr9+vXDsWPHSvNpGWOMMcYYY2UtNQa6nwbg2M8v4PgN2+NzzRLDIS17ENuOnMYry0/h++1hFW/7yDJux0bh4zXn8M3mS44dWJBlGK7sFBf3GlqI82PX7azLkA74LeB1vKh9HpflmsiBE+JtBbvGoDtJVsZOV5HSbAfFqUrQHS37IhBxaGIIQ00p1naATgx6qExzbrv7AZ5Kebl/YZluY+Y9EZ7wcnWCavA0PJDzoXi/aTaawck5SqZdr3YVnc9NdFrr1zBND6ZWSeIklou7l+f3yy+/oHfv3njxxRftbyTGGGOMMcbsiT6L5IhQ6/Ge7D+hX/YwNJFH0P7Wb/jw7+MF3/nWUUiQ8ah6o7i6/MjNor1Y3GVE/TgGX/7vf/aD0nJmOPgj1HM749rB1Zi9PQyHrinzUxcoIwFO6Urwe9TQCGrocSzczuOq1sVyfR9sMnQy35Qv2DUYIOUoY6V/cpqA6c7P45yhDpIzbQTSqdHiLAa+kCiIppeQUu2Xt2cmim1InDz8AA+LMd32HkOLZJynmw4CeLpoIIW0xwWnFkiCF9KybHRgz8m0CLpdzDfrtNm5z5kchZqIEdlyt7D1eMnwB7qqzvE83bZMnjwZH330Efbt22d3IzHGGGOMMWZT9FnI83oj+6cBeHzBfl5JJaXXicyrQxKuQh1x2HxVFXuuwAZciDwhzq7LgeZpp2wGgnZsuqXG9cgovBz3Ib77Y6XNDGlxJKbn4PcD4QiNsd3Uy2EGA/S7voKvnIxaUoy46Ze91wp/XIoyTjte9sJO1zdw2WUiMhKtx25bou7i7aTLeNVlNfqpjucvBc9JNQfG+zwHY6/nEEQgACm21nVmgnl8tsrDX1z2paDb3nYxlpYnyp7w9XI3B91VkYKEAkrSDZlKdpzKvynoJp6uynl6to3PjE7JdBs0boAmN+jW5+R2SJc3vo3dLq9gjHovnK9vxyR5FdpLl3lMN2OMMcbYXSEzEbrZHXFqel98tfFCeS8NK4A2LhSSrEOglATn6zvN8wcXRn9hLaauOYOWH2/C99tCeR2T26HImtkCodO7Ys72S4WvE48AbGj1nflqG1UYLhcQuMqRJ8U5bauvnX7ECNUBXIp2PNBdcjwWelkFZ0mPezNX4I+D10u23QwGxB5djb4zd+KD1ecwZs7+kmXQY8/DKSse6bILjvmNhC9ScOri5YIPRFiM046U/eCilkXjM01atM3SdN2FdWiRcwqD1EfxorQcA1XH8me6nb2wceAWDMmeAS9PT1RxU8qzbR7gyFLmyU6WPeDkZcx0IxWJ9rLWxiZqCbKX6EKO4Lb4p/8u9M+ZWWB5ualLuVbjqZSBR53CBGkjeqpOIzU7/3JJxjHdMgXdKjUMkjJlmF6XbdURXZxZTBlG3ctpurFKN6Z7y5YtIpO9fft2cX337t0YOnSoGMe9cOHCslpGxhi7u4Rtxc2lL2Pe5hPQVpQOIJWMTm/AkfCEImVtBIMeOdlZ3FDoDmNw9sbtpGS0zj6Ok7tXIyal4Plnrcgy9DEXcTM2scLs/P2XbiZkFNg5Oa8zVfrgb31PcXmk+gB2XIwt/EGRJ6BePgFuh75HapZOjC2OS7XYma+M9Dpof78frhlRaJhzARe2Ly503mW4eGGHoS2+1d4nrrZRXcGFKOsGXmYGA+SoU+KiRgLuV+9GL9VpXIy2c/88kjO02HclEd/qlNfqqzqJnRdyp88qlkvrEbh2Inpl7RJX07K1+HTlseJ/H1/bLc6OGJpgVvWNOOH6NCar1uNiYQcW6vXGp7UW4G3tFGjdlW7gvoYEpNrI5EtrX8FS58/ghUzzWOp8AbJKhUg5ABflWqjjlIhuhuNoJV2x2XAN5gy0O1y8A3Iz3el2fsuqt8SvjefgPd3j8PN0Fllod9/qMEBVYHm5lK1sZ9nZS7nhynY8n/WTaNpmK9Mt6YxBt5ObOM92DRTjznUWjdRkY9BN83SrVEpQ7qSqOPtQDgfdixcvxrBhw7B27VqMHj0aixYtEuchISGoV68enn76afz1119lu7SM3SkMBhiu7cWlm1G8E8byz83552TUvLQQTfa8iJmbLvIaupvotcja8TVGzN6DsT8ewAtLlfJJh1w/AN1nNaD6PAhTP3kH+8OUDAIrf+ej07Ahp424PFm9CStP2C/1tJKRgKy5vcWYztDv78Gn/54r2wWtYK7Hp2PwrN3o+/VObD6njDUtDGVKl+r6iss9VGccC7ovbRBnXVTKHMc5eoMoL67UIk/AKTl3HTyuWos1JwsPasNi03BaricuN5Wu43yknSA6LUZMiaWTVdjvrmyvJqobuBDlQKY7+RbSFk/AcOxBekA76N38UEXKgObmAduBpIOyditZ+qaq61gzxg0rXT7Gfbfn4mgxs926q0rwvs/QHNXrNheXacqrsxFKNtkuJzccyaiOc3JdwCtI3FRdSkBsSv4DQVK28ly3NCHi3F9Kttlp3BSId83Zj+ej3sEUzTpx4CIvbc838b72UVww1IZbFaXs37egTLerN05IzXDQ0Ax+lOmm8dwUfNsaW24hqXpX7NM3h87VV7nBRQm+vaRMcbAjr8Nd52Bc9ge47VJLXD84eje6ZP8gqgFMZNM83bIKkkoJUQfV0KNViDLfeKUJumfOnClO1Jl81apVePbZZ/Hhhx9i/vz5+PnnnzF9+nTMmjWrbJeWsTuBQQ/9309A9etwZM0fhteXHy36c9w6ho1novDZuvM2vzTZXczFC0edO4qLvdWncWrv2iJleVg5u7oTrrs+xaDbv4mruy/H4drtdIceKu+bBY0+E1pocCHLH7O2colrqTMYzCWIRXHyZpKY75V0U53DhtMOBt03D8Mp9qy42E99EomHl9oPQpi1Kzsw6+8dyMjRw6DX4f0Vh5CjKyRrpdeJoPuSrOyYB0gpuB5ZeLBuCN0izrcY2mNevf2Y4zQLey45FuQ74q6cCuuq0j37mKEhcqCMtd1w+laBD5FP/4kGsZtwVQ7ComY/Y3LOWwiNTbN951QlgI+DD7K864jLwVI8rsbZub+l6/tRI3ITJms2o3ODQKgbDxU3d5NOY3+YMsa4yG6HwTXqMHJkNc7VGo9WfjLaSqEYoj6MFYeLdwDGEKEcdL3i2gLeDXuIy62lKzgfUXgQH5GoZHY1PjXEeaCUmL/6QpcNlU6pPohzrinO/cVUXXnuF30WnUK/xVj1TqjclSC3CtJtVmIlhfTDYv1ARMIf7j7VRPl3OtyQVMC+pqmzuQ/Ntw2gwamvMdvpe7il2/+8HO/0LSZo34PWTcmmw0UJjL2QgTQbme5YjyY4JDeF5KJ0X3dxUkJQy1J9U6ZbZ1Fe7nA/gooUdIeGhmLkyJHicv/+/aHT6cS5yfDhw3HxImd0WCVw4V+oz/0tLrZWXYX/2V+w63Kc448//Sfwcz8cXzYV8/dcw8SFhwsfH8T+W9lpyDjwM9YePIvbaUUrUdSrnPFY6tNYqe9u3sHff6WYOxHsvxe2VZwFS7cRIsWik3QBfx51oCNvcgQQullcHJPzidi5OByeYL80kxXduZVIn9EISZ/WxVvz/oKtXj02Xd6MKifnQQUDtJITPKRspMVcc6hKKblmf3TS/4w1+q7i+sPqzVi4z4FGSpUdleSveh5fREwUO+9HXZ7BQ9rVhU9Btfk9vHJyKO5R70WmkxJcuKTdKLiLeUYCJGNDr+OuXTHw9m8Ypj4MOfo00kvYmCt73xzEfNYclz9uhS+/nlbmHbZ14Qdw8Ydx6Pnldjyy4FCB46kLo41WDhb9o++JY+PP4p6caTgXnV7g9EuGbdPwJb5DdSkRtVv3FvNL2x2KYdDjtkcjXDaEQO1b21waHZ9USBaYRJ8RZ2cMddEs2BuoqXTubiVdFQfIiuWy0kH9kKEpurVuAdTtBZ2Tlzhwk3B5f9EPnGQkwDlDaZ7mHNwcqFoPepUTXCUtom9eLfCh2n0/YFzWn6L5mrufksGujkTE5d2fsJh7O8W9prlreL4pw2LOoWfcUlG67eRhDLol20G36TYvVw3UAQ1w+MFTGJozw36mO3Qruif8gybSDfNYce/wjRilPgDv7Bi7Q+RoGIfpdQTTPN2U6bbRvTzLuJ/rZgy2XTRK+Xi25YE4g/H/uSo36DY1kKtUQbeTkxNycnI3mIuLCzw9laMVxNnZGZmZjjW7YOyulhguMlkpsru4OkJ90LGdciLLkHfPFBdrS0rJ3KmbSQ6VfLH/iC4bOYsfQNbmT/HqqjD0n7mr8FIyC5SloQ6sx1StzEH3QQ667xqmjBlNt7LX5WXMd56JQ1ccKBMP3QxJNuCIoRGatemKIc2ri5s3nFGmjWEllBYL/cpn4ZFDebVUTIn9DNtuOjjW78yfGBn9A/qoTiLTu764qY7hujkTVZDdoXGI17pgWZUnxPX2UihOXgytMPPGlpnEa1Cn3oIMCRHebeArpWGg+hj2hhb8f0mOu4gqhmRkwwn6gGZi3mJXORu3CtpWMefEjvkNQwACQ+pBVUc54NkBF4ofwImpoubBZcs7qKa9hcbSDbyW+jU+/mWlY5nc4og4Dvw6Cm4xx8U4+D2htzHpl8OFj8O243zXWeiV/S0OuvVCpwbV4aJRIUtrwE17lVd6LVQpyr5Mmkct1PRV9nFi7Y2Nr9kJsxr+gknat+Fb1R8GJ+X+UkpkoQGuHKMcELgg10JzCrprtBPXa0mxOHureNss56IyxGCH3BYDm1UTc0FLjQaJ29pnHcSloh7A0OfgQNV7sE7fCfVDqotAUF9FObiAhCsFv8ejC/Cm05+orU6AS1Ul013NVqbbOC46RXaD5FZVXHaXspGclmHzfqlwh7OnnznTna8UX5cD6eK/YphFFWMw7OOuBNJ25+k+swJPps1FL9Upc9CtcvMR595Shu0O6bKMNONrm4NuU3k5MvIf7NJlo2HYL3hEvRnuGmW91d/zCv52/gg1c67kLy+XNIAp010Zg+4GDRpYZbIjIiJQt25d8/UrV66I8d2MVXTXm05Bu6wf8aD2Q3G9hXQN569cd+woatRJSLcvIlt2wk/OE/HawEaiG+byw5w5uWMcng/nm/twW+8BZ2jFUeNvtlx27LGxF3H74BJURzzSgruZS9FOX7lVrPJEQ+g2hC+YjLXfv4RfNh8pcdaGFSIlEqqEK2KM4hbP0ZBVTmKcYWrM1cKDLGOm7bChCTrU9sHIoEQ8pN6Gw9e4yqFU7P0Wal0G4mRv0Um4qeomMmMuOtTozmDssHxergOnIGVcZmPpFkJjC9kJl2WcuKEEAA0bNoEhqA1Ukoy2WQdxuggH4goly0iNvIwzYddLNJ61pCgTtS/sNs5FJpe8nDp8rzg7JdeHvrFSJdlCFY4jl28U+DA5VumwHWaoAc2j/+JF3zk4JjfGjfgChujEKfuml+UQNKruBdRWvnvbqy6LZojFkhwBefP74uJv8jBcr30/fvB5HWeyq+HVP0+Vfrm5QY+Mf16ARs4Rpd3P96qFev4eSElOxA+blaxwUYXGpeOGXA2BgUGiu3TDapQok+03AUuLFQcOtbIaKq9qqHF9Fd7WLEFg9nVk5tiuNIhOVg4IVPdxg+StxAABclz+TG3etxulBN2hUh00DPQCApri0tjd6JkzC2ciU4q+frVZ0Nw8JC7eqNoDAV7KlFRqY9DdSXVRDBUqEq/qmKGague0L6NZkFI6rfZXDtoF6SMLfI+qVOVga5ZbNUj+jXDVqz0uyTXzH0DJyp12y8kjd9xydnqyzY7klOxx9lKCcx8pLf/3X3os6m9/Gr85TTcH0L7GknGap9vmejUuQzI84e2qPEYyBt1KYG9jvyPyBB7e2h4bnN+Cl4vyGLgomW4vicrL8zwmOxWdw2bhU6dFcNEo93eLP4f2qlC463Pfa0b94VikG4RIqTrQ7Xlon9iFqwHKNqxUQfe7774LX1/jYHk6+uHtDUmSzNePHj2KBx54oPSXkLE7zN6w2+Joo1ftNjD4NRRTQTTMPGV/3JOlc6vM4876t22EJ1Ln4pDL83COOIgrZXX0nBVtvP7BeeLiQv0QfHhfF9DX3PaLsY5lN87+jV6n38LrTitQp34TGLyCoZEM8Em9hBgbDVQKdPx3qP64F3VursSI+EXovfcRvPDThlKbx7QiorHX83ZdwbLDhZSj2mMsx7wiB6NmSC0gsKm4Xk93BdcTCh6XL0ceF+enDfXRNtgNw/Y/hOlOCxB/81LxliWvzEREHl6J3VtX48iVmMrVwFGWkXNJqUB4S/cM1K3Gist9VSewt7Dxn3otpAQlk3JdUxeuNVogS3KDi5RT+Hf2mhcw+eQ4DFYdRttavlAZx532UJ3FzksONPdygD50G5JnNIXXTx3RcnEr7PlsBL5dtbd0PjOOykzC6WN70eOL7Zjw8yEMn70XExccKnrnfkvh+8ylvu2aNYbeTZkvWBdzScwMYBONb01TgpVkt5pwdVKjtp+7uSGbXXFKoB5KQTcFcEGtxXUqly3u8A7DnplQG3JwyNAEqb0/Qe1HF2Dc46/DzUktsudbL5TO9jc7txLu8WeRLLtjR5OP8fqwVpjX9jp2urwCl5O/moPboqCGaKRBoCcQcQw/pr+M5c6f4rLdoFsppY6HN/y83OB6ZjGe1qxFE+mm3Wx7ZJJye3AVN0g+xqAbSQUvb1oc1BmxMMgSdP5N4axRAWoN6jRuASe1SnzuCqxssCX2HFSyFrdlb9SopxxYE2p2NidHTl1T3p+jKEC9auznIdYhBd1+xkoZKVrMrW1TToY4QCiewzNQVARsaj8fn+sm5M90G4PpVNkNXu5uiBizEkOzpyMm29n2/agjuZfyf8kbGUjJWzJumi4MHqhiDLYDd7yGVc7vo6l8xfb+g8UUY95uxqy1Kei2U8JOQbRapom9DOa5uWEcq+2B7Pyvo1XWR5bsBBdn5f6SRlk+yaJHR0KrJ/CxbjKuqOoA3sFAtebIdlKWpVIF3WPGjEGvXr3s/v3tt9/Gp59+WlrLxdidyWDA8evKUcHOdatCNWQGPgr4FtsNbXHwqgMZrZuHxdkuQyv0auQPNzkTzpIOvVWnsOtSEY/CstJ3ZTvUKTeQKHviStBwjA1JxG9VF+F9ze/YcNaBpjyx583j1JoHV4Fq2Fd4ze1TXJBrF21sXsI1GNa+Ki7u0rdCksYf9VVReDx2OqauVgJDZiHqNI6t/kF0SZ6+4SLe/ucM7vvfbnFkv0iMJY80LUvTIG9Ixp13ytAV2DzLYEC2XhIZ8kvq+mhYIwAIaS/+1EY+j9O3SpYV1R3+BTlfNkbw+snotXcibi56DKN/2Gveqa7wJAn/a/Irxue8C1WDfnDt/gyWNJiJ6brxhffTSIkU2TuqLnLxCYbU9TnM774b3+rGIjSm4PVnuHUEtfQ3RFOfNjV9gFYPYGv7OXhP+3ipjO2VMxKQvWwSqmRHQS8rSYzh6oMYeXwK3v99m/3gtDTpcpCxcAxqrXkA1dIviexYW/VVvH3jSUz9bV2xM7r6G0rW8YihMVrV9IGqWjNxvZ58HTftBVSpyncsbStXY9fl2n4e4rygg15y3Z74CwNE9+XGlOmu1kJ5rBSLm9HF+F3NSoF84g9xcZ5qHB7roXTyrubtisnd68AFOfhle/Gyz7bfgIzMPd+Liwv1Q/HYYCVQbOitFeORx0g78fcxB4ewmRyci8FnXsEA1TElYFQ7IyQrDA2lW7ho77coXVlXt+UqCPB0gWTsuk1l0TZLzJdNwP8Sn0Z31RlUr+IK3Dsf9/utxGpDD0QWNLd63AVxdl0OREhgbudqGuMrth8dg4gs4nemdw3MdXsK83Qj0LFe7nPCtw5yXP3hIumgvXmsSE+ZdOsi9FnKd4Tp4A/q9sZG91E4aGgqhgDYlKEMociWNXAzZq/9jd3A8wXd/o3xb+13MFc3Gt5uTnCu113sLyTnyNbVVabyctkdbt5KppuqbgyZybanC5M9zJluze2LaKO6Khq52WqmZshMNE8xZnoMXJXl9rYbdOeWu5vLyz2rY3u77/Fozpv5q/KMc3RnwgWuzspYbpqaTLwPfe7vtOlgskZdpBmt7xoV810xZoF2GhLTcwrvmuqIJQ/g2YsT0Vm6gHa1fYGGA+DdsDt01K24sCPqei1kU7MXQ0O0rekLNFCaEYqgu6ilT6z0XVgjzv7Vd8XIDg3EPJQ90zdjtHofDjgw/ZOcoDRXCZerow79SDcdgcyQ7qJzaJGC7vgwpMMVB/TNsKnN9/B5ehP0aldRInflxA7HDvBUFhS4LB6Hlsc/QGN9GFoEe+FJt+34LuEZfL5SOcjlsBhlOqiLhlpoFuQNVG9lzpidjypgJ1Clwqbuy9AiewH8guqJHQYppGNuhqUE40rlo4ugWf8KnOVs3DQEIFXyxF/SYJyNSMED8w6IHgLliXY8P1p9FoO/3Y1Vnz2I337+DqEOztNbFFsuJmC/oQWGtwkBqrdArU4jkQMn7A6NLzgwTFLKmSNkP9So6iF29OpRNlRURhQQdGenQbqtDCu57tJY2emuWg/V240QO5qU7SzpuO5/Q7MwMuNDESisHX4YeHIXMt2C0EAVifuvfYDvtijBSVnSbpsG91ilSqNtbT8ceqcffq+1Ds1V13HvrS8d71diKScdqiSlW3SyT1N4umggGYPuRlTWb++70FiSGyP7oHoVN5HBfub8I1jr/K79AIeCxJpD8HrWY9gtt0b9AE/Aww96j+oiKHFLvFj0RqVO7vix2odYqBuMWm0Hwc0UJAB4xnMXDro8j7ZRfxY9MLQn6iTcYk+KIC2ywXjU8VcONKD5vaJxFw2luHBCqRxw2NVdaJuxXzSEbEhBt4/SDb6qlIbY23b2NdJic4NuKs82B90JNpupGeIuoR5uifmcgyjo9vCHn49SYhxtr/maqemk8Xeyrum9klvH8FnOV/hYs6jIBxTTnf3xZVJvzNePQMc6SlAqSBJUTYaJ6sLYdAOikh3PoHssGYXzro+hj1eEqLoQGg/B1rqvY5uhvf0hD+nKvkICvOHv5SouVxVTcclIznsguEoN7PQYijWGbvBxczYHsPSVlp6jy19eDnd4uHvgdo9P8Ib2ScTnXc3m++UG3aayb8qMm5qfWZKNgXsaPbcxCw1Xi/JyO5lu8RjZTfz/FpxcRed0aiJqL9OdCWe4GhuoSWrjEABZa/4ulVNjRKUEHSShOdJVe75GQMppVKqge8iQIdi/f3+h90tNTcUXX3yBH374oTSWjbHik2VUjz+Ik4vfQbcZ29H20y1o8dEmvLXiVPFL5mhu7puHUN8QjjS4iXJD0qiasgNX6M5vzFlIukwkyR5Q+zdEFWpuUa+vaDRDP6pXr4aWehdzypSErZqOoz8+ic3z3sRfa1bhXETxA4AKX1p+cb24uNnQAYObVwNCOsKgcRPZhpQbpwou+aRfyQRlbP4NBKJmVeXIuBivRuMNixB0X/bugk4Zs/CG/hk807cR4N8A6gd+xXdNl+C43Aiztjo4xryM6DMSEXk9VJTcl/c4c/2Gd+CSHokI2R+Nm7XGmifb4TXPzaIyoOr533GisG7JFnJajcf3+nux19BC6ajrp2S4aksxhU4bFn47A1lwQX1jGSKqtxRnzVTXHZu31pa4S5DXvSYuztWPwqVxe+D17hXMeuNJMW8pzaH6xG9Hip7RLw1xl3Fi2afo/80u/HrgOi7FpGBwzjZMvPUhrsy5D38eKL3PKL1P00HNHg2U6Wna1vQRQ3toXGWBpajJStB4Sw5AiK+buFzDeB5VUAls3CWRIY+VfeAfVMs8nK5JdS9RYkw7r2ElGBKUmqXFJ/+exxW5BjJ6f4TRnRoBwW3g9vhaaDUeaCBFYMvu3UX6/BZZ3CWoD/xPXPzC6Tm8Mel+uDpr4PnAj6KRUQ/1Oezd/E/RD1jHXRSNzWj8fVCQEuyZhmo0lm4WMAWVMeiGr5I5dXKDb1ooGkk3EVXAtIumMl8qcTYFyKqgFmKarOpIcHjKP5MMPTD7Rj1M1U3C/R2UjtIm3h6eoincferdWHao4PHpjtKfXC7Otxg6YGgX5UCf4F4V+vrKeNaGibtwNc7x9yEbq64uGWopmW7XKtAbp3RCop3lTjcG3agCf08XwNsi021reJRxeyVIfuYAL6iKA/+32jyEx4NX4i3tk+ZKBkGbjtapu0QSoqhB9zkxDhyo7u0qKhIsae75Ht/4TcVJuQFOGns0FConHc6Zyvpw8s/tX0VqGX/b7ZaXZygHxRNkLyXYlmX0Wt0VYS6PQJWR/4CHaZ+U1qHLpTV40WkVGki3rAJkQ1ZuplsE5l2exgp9H8Rlq60rYkxjxOX8WWsaa22rZ4RkfIzexQcqlXHYsLmRmv3ycrE8cDePAycexgA8zV6mW3YxH8AwlZdT75wc43uo8+9YHHF9Fq2kK2LKQfXuGahW2YLusWPHivHaTZs2xVtvvYUVK1Zg3759Ys7urVu3Yvbs2eLvQUFBOHHiBEaNGlX2S86YPXotdCufQ+cbc1A3fDlikpUvRmd9GsaeeQJv/+93xBdxGighPgyq7BRkys7I8Wti/kLrmLgOH2p+Q0xMVMEZF9+6+LfhZ/hS9yDa1DYeifXwA4KUH9k2hvMlyojlRTtrQ7/bg5zjS9EhejkGRc3D/ccnQTuvHz6c89tdMYZcjr+CG/98hMvfDMGlz7pg/RcT8Oryk1h3OsruNBbFFnEM6ozb4scqJ6QbAukItcYFUm1lqqDWYvsUkNlIjRYHVajEWO8ZovywaDPRN2cHXtb8hcuFlLJa+vdUJDLhiiaNm5mDdzrCPmFobzipJRy8moBj14vZIKi4MhIQu+o9xH3WFOov6yB1wT3oN3MXWny8CaP/txdLDl4v/W1SmJtHoD6zTFz81PllfDi2K1SunnDp/6647XHNBvy6Wxnv6Yir3h0xU3s/wl0aKZmbqvXMHXVvFTSm1GLMqXkn0hh003jIi5FJxZtyad0bUMk6bNe3QU6vDzCgeZDIJtBn89dHO+H+KhfxYeqn+OzfUix1dUTUaWTNG4DWF2ailf48utX3w/wJbRDT/HFR9TNEdRj+66dgRWk0iEyJhMvcDpimWYBGgR7mBknOcafxmetiPKzeUvDsAuZMtz9CjN2Ym+x9GRud34J76lX7n9l4ZY71MEOwkj010sScxhdV/sJ49TYcL26JeexFbNi8UUxHSJm+Z/sq40QF/wZwmvgPvmn0h6i4mLbuQpnNEZ2z8QOooMcWfXv0Gv1Y7k66bx3I7SaLi/dl/YPVJx2c09zEty4W13gfX+vGiWEaQnA7hFftgRNyQ/sBlYsXQj3a46ShgQieqFyVOEt6ZKfYyc5mJSPl+inRMbmGjxLwEWnMPDwSuBLrDF2KPO0WdQ2naYzoII3orG2p2Sjo1S7ioN7Vs/tLpbfCFdTABUMtbHXqi54NlfG6Js7Nhonz3qqT2FVI53czbZb5cx/tUsv8fwY+Sudt7+wo2wdLW47FDN+p+E03UHmMcf1XQxJi8o7pzsmAKkfZjjqPQOWgVPwVPBQ5XfxfjSqovJyqiRKAWPiirr/x940ENDF/396ILUI1l16LrCO/iYqkljXybC+jNjWVwPOso9UJxgPoCbInAgKV9WDSwCMbzaRwRCUkFZjpjpe94Udl5ZRtl/Wivwv15rAScx51kg4iRIoT//+kYwvxqvpPNJfCrQLX1EGzxFjvbYa2IrA1l3TTy1k2uTOWl9OYbipXtyoVpzHgeQNoXQ5Uukyr+wkdHsOHjf/Fm9qnbDd3NJe7u+WO6aagOWqD6FAu5X2fpjHdcIarccowyUn5XDpJOnPQTdtSUKnN3csr3ZRhjz/+OK5evYoPPvhAdDB/6qmn0LNnT3Ts2BGDBw/G/PnzUatWLRGEL1u2DDVrWh8ZZHcZWYY29jKuHfwXpzf8jH1rF+HgjrU4fim83LNahTLokbNiCtwu/CmCn+XSYHxxb3Nc+GQINnU4hg6qy3gv7TO8t2R30UsDo0+bx3s2C8ktX6p28n94TLMRtbRXEVnQ0V03H/yr74Il+v65OyL0hVJbmd6ESocPXyuFQCr+CjacjsS4eQdFRuEf1SDsDJiAy379kCO5oI3qCj6OeRG7Zj+Jn3deLrMduhJJjsDtP56A4fsOqHV6FhqlHEBj7QVIaTH450QEnltyHMO+21O6WaDgdviixvd4W/sEejRWjvATKViZyoR+BE8XNJVJovIjHSn7Idg/98er9ZG38LLmH9yOiXBoXctxl7D2lDKF3MjWucthyiLc1y5EzP05Z7sSFJQ5gwGZe35AxtctEXjyfwjQKssWi6qirIze0tlbCaiz7kHM/eo9HPkPu3Vrd8wQ5yt0vTB6xOjcI+4t74fWozoCpGRkXdiUfxydHaZMUr0AT2UnskpNxLV/BW9rpyAysYCDJutexzOhUzBQddScBYFfQ8hqF3hJmciKu1r0bGFOOm6lyWJ86zyPp/G0ZWBGcY0mGzPwPQaqjyPo9Jz/bnhK0g1kLboHrrpknJbroX+P7vjjic4Y2DIEdR6YAfWkVdCqXNBPfRLR/35SpOn2bArfC4+062iluoqu9XMDEinuEh6UN2CUej/OFPQaXZ7B677f4Wf9MHOm2yXpMpqobiIEcfYbPhlLy6mTtGXQTUMQRqWtEENOCnzdAui3fIwHjj+MJ9Tr8GL/Bub5as1qdcELIzqLjDqNHXeon0RRRZ6E85VNYiz5iqpTMKSFdWCh6fasOO+lOo2tB44U7bndq+KPjC5Yru+LpkFKpQ8dXL7YbwG+1d1vP+huMACf+s3AZ7qHlUy3xhkGd6WywSUzxnalUfg+9N0+GoudPzdXMAge/qhXXalGK2zsvpXQrVDvmCYCOJp2yrJhsLIgXpAaKQ31embvxqFS+L77PaevmEfZrfmw/GNZGwwQZzQm99SlMMeeMPGaCFToAHLVgGDze1BXrSPOa0pxiLAVFPvUwmZdG5yW6ytBNyUFREl6CuLyZrotxi27mrpuazPQOPpfDFEfsT/NmLFLfqSxzNsq0+0RILKtVMFiiAtzfB8t7hJ6nf8Ifzp/gpY1LAJHC02re6EaEhAW5eA+g6n5IpXAWy4jgIE7RmC9y7uQjMPJ8r/YSLwXNB9TdRPhJ8rKczPHmpxk6wM1xxbi/cT3ME69QznoZZzrmn43qBrGJMXJX4z1znKqIprNudy+gP6a0whEonVW2aLLee782abx2ZTpzrMPL6lwot9iTMl5FWo3iwMWLl5w8vIHtUqzmek2Zd7hnlteTr+dxz8XHcppfu98B4JgCrqN1Siu3mI5qdrT/Psom+bptpgy7E7cRy3rMd00D/f48eOxevVqJCQkIDExEZGRkcjKysKZM2fw9ddfo3HjxmW7tKxMUYnKxb+nIfnTOnCa0xF1Nz6MVodeQ/ejL6HLrglot7Q1XvnkM4z4fg/m7rxSpLEx/wlZhu7f1+B8caWY8uIlwyvoN+UrjO1UT5Sc1Rj+NnKq1EWIdBvdbszDX8dvFavJ0nlDbWW8p5FprBqVzdntCmpkyi6bOmEKxulNRNBd3OlNTOIui0z21RXviiOHg5pVw/Nvfo4+z81BoxdWwvnVM0hrMlaMdXtMvQ5ZW6fhzb9Ol85491KiO7sambM7wz90heiMuU9uhdXBr+JQx9nw6f8anu5dX5RspcVex5H5L2LNySJuRzv0khp/RFTDekMX9LDMNhgrEZpTM62Cxu0bj4wr47mNP9JObkAVpaMrzfVa0I6IuWRrbjcsTnscAZoM9G9aLd9d3s+ZjZ3Or4r5pAts7lUastOQungC3La9C3dDmsjG/BL8Mc5OPINuH+/G2amDcejd/vi5/Q10U5/Hi1lzEP3LBCzeU/ZjURF9Bk5Xt4qgYUPVhzGyVXDu39ROcGqtdLgeodqPTeccCFoSrkJ3aTOCEI/6pnGGaie4DXofqww9cDtTttoJshJ1Eg11l+EEXe62V2vMJbUN5PAiV5ZoNe4Yl/IyumfPxph+3fMHZi5e0Iz4Wlx8QbMS8/9ahwzLMYBl1XTrj0fgmh2Pc4ba2NLuRzwzvItVYCLV7QnNmDni8nOqlZi7ZEXJKiCu7RZnBwzNrIJuOaiNOG8hhePsrQJ2pF2rYHdaiCjjNgVlUhUlMUDjXe02fLqtHNS6KgfnDhkQD2prPgh3rqDXtSfhKtShG0Tn5nMeXaw/txYo6JzSs644kLN43bZS/4427J4pzmks6ZDePfMHl371kVOrl/itaBbzb5HKfWlZw4zTsVkeYK5jzGranSdaTEGlbA8RdNO2Mpc4J9gucTYOH6BKBstMNxFjmYs4tEc+tRQDbv+Oker9GGDj+5eoWt4nzkeqD2D9qSJWAeR9PVnG1gtKgDKQhjTl5VUdWX5KN26XW/vg0MfA/NmtjgbG4U2CcVw3Bd237GwD0wFKUV7u7m8Oum/nnR7LlM0Vnc6N5dzGzLi/lIL4lHT7FTyLx2KaegGCXbJzg1JCGeFAJY6opb9pDswdTYicl2ujZYjtTtf3HxqLQ67PQxfpYKmyuT9Ltdwx9qa34KX8n3VKj7J9YMDFE2e0weI7x89DyeZK7kqipgrSrH9HjMGruRzcNO0WKOi2yHQbL3uZDixvehcLNDPQVXUOaZaBdOMhWOjzAtYauuYPupGe/zdMrcEN73ZiaIOXu3VZvukgts0x3b61cUzVQsz0YZl1l52M60qb5/uiVld87v8FpmkfFgcTxTq572d00P0iyuRN32+ScZ5umq6z0ma6balSpQqqV68OJ6fcWn52d5INBlGu23fmTvxwLBNVDEmirX8oauKMUyuEOTdFnErpJHrJECIa+Hyx8SKe/fJnfLdo8R1Tpiwf/gmaEwvFzsy70gto3rS1dXDr5gPne5QOoRPUW7Fiw9ai7aRGKyWcFyjTbVlyFqD8SNSVogr8cdcfmo/aCftF51OrzEmtbkir2Rf/6HuKkuFi76DmpEO79GE45SShq3QGI1sGYu7D7eFjnDZC8KoGzwd/hjz2VyR4NsIv+mFYcewWnv3j2B0ReFO55db1K+CmT8VJQz18V2cuGr+xFaOf/Aidh09C1z5D8fbQJtjxam/87jMPT6rXIO6v17HlfNGmArGFsth0FNjbVYNWlkfLjc20qAHQpYgCDorU74dfanyC+frhVkfvJeMUI3VV0YWPyQvfK350cmQNGtepZXUE2cTTT2kQ9Lh6PRbtL9v53SM3fwevq+uRI6sxUzMF2Y/vxGNPvoIW9WqJeV8JjZ/re+/TyOj7qTgqTjuiLbaMx1erDpZpFYVu1zfifL2hM8YO7JU7Fs2khbJj3F91HFtPOrCeLvyLUWdfxNtOS1EvIHf70Tbwpf4LNC7Yzthh2XjA5bpcDbVMXW5p2/f7AJ9W/Vw0ACvq1EU0m4FoRuQZgDHtati+U4v7oGs4BE6SHo9lLsSCPWX7edBveg/ucSdFX4oldabjtVFKs7i8pJb3I7vpfeJz+lTq/7D0UHjxX/OqEnQflJuhSz2LBkl+9cXnzV3KRnzktQKzaqaDXabyctOBsGAp3nbGj4JSn9ois3RJDkF9i88D/BvBoHGFp5SFrOjLRf/ePLZInO02tELnTl0K7NL7vOF3zHf+BuPTfy96iXdBDAbcyHQSv/PLXe7HCDuBv3P7h8X5cNVBrHH09WUZCTv+h07yafi60jrPDYRp/XsjDTkZqbYPYOl15sqDIHPQrSxbdSnRdnMui+EDwZZBt8GA4VemYo3ze4iJUapzCmUwQB+2Q1zcj7ZoT81SbWk4CDqNB2pI8Yg6t6tEXeZvHPgHqckJIhDpZnFQyZJLhwlYohqBS9rqCHfk+EG8khG/JgdZ7wMFNEaEUx0x1jjCxneZ9uhv6J+zUwSGItPt3xBHBv6Fe7I/RUJ6tt0San9T4OzuB1lSAip9qp0p1dLj4HF9Gx5Sb4e/r0++gz2SscS8geqWwwd6dLeUBrVnDXXQ3E55uZOP8jnyTw+1XS6dhxx/1fydblUCT9lqX+X7OEBOsDtXd3yacntVY9dylZvyWfJBunUHcYvGZyJ4NQbdnpJ10zOf/Z/jOfUqVHM2vp6LlzkjnpZt8XzBbbFKM0R08vc0zZ/t5otMtaeYhSElM/8+rymTbTk2GylRGBz+BT7V/GLzMej4BB6TPxLVLJZBN5yNU6sZy8nNPPxwTN1KDC8xlZeLu9N0ccYDdYIx6JZEebnps8FBNysrtJMauhXYoxyFLlNZKchY/gQWffe+KNe9mZCJw67d8UfTuQifcgH1PzyDlu/tQYN3DyLgw1Dg9TD8+fZ4TL+3JTrVqYq31H/gpfDncG72WMxeubN85w+OPAl5ozKOc7p+AkaPfxY1rA9OKur2hKHxcFG+9GDO3/jziOOdWQ1RZ2xmuuHXQHlqKRrh9sZ9ZiZBveF1/OL0BbydZfMOheDhB/dHV2Kp0xhk5BhEQ5Bi2f4ZnBIuic6vX/l+jK8eaGcOjPKSmt+Dqq8exMxJ/eCiUYk5R1/843C5Bt60s0XdmF9KuA9fyhMRO3YNXpo8XjninkcVD2fUG/KCedzu9uWzHZtH257wfdCsfxW9VKfEjo/VjrBvHRhcfXFDroaUuAj7zdS8g7AmpwP2GFopnctNqhqDbinK/ufD5OpOcbbP0MI6226p05OQJZVocnTx5AHRmb8s0EGIIcfa4QfdKHzq9yUefeVztKltMRWLJbUG7r1fhGryGmQ6+YhSyIHHn8G3/x4ts8D7kL4Rbsn+WO05DoOaW5fGCkFtoPOqATcpB6qbewtvoGjKDhmCRHm5WWoMRnleQBspzHYH5exUSMZSy2TXGrnjYknDAdDX6SNK8IpUlXDjELYfUjpK39OmRv4st4kkQTPkcxgkjSjnPrlrpcOl9EUWthXqIz+Jix+rX8Br4wbmP9BhwWX4DGS4+IsDDnO2XnBoRzefpJtQJ18XQ4VSAztaH0BUaZDmomx3v6xw2/8PDHpk//sGHlevg4+z3nzwJDfotp/pvtH+HTGG8qiqtWjQZabWQDIeiGssXynaeGG9DvrjylRUSwz9MTZPk668nNs+JEovR6gPYvOO7SXulm4iSxKeT3sUHbPnoke3nuYd33waD0Vktb6YqxuF7eeVplmFSotF9X0f4HenGWhe3d0qqPJc9ShOuz4pDszZOoBl+KETdsiPo4V0NbcZlsW0VTaHAhgz3TSsx6q8XKWCf/xRtFJdg0uig01KY89DkxWPDNkFTnU65XaszsvJFapmI8XFntl7ij8s7HYYam9+DIdcnkO/+l52X4+mudtb/1Wck+vgWpr9/3Nm2kxRxkvfZQ2rWXyXtZuIn1stwQ/6e2yuf9XWDzHLeQ5CNMni4DNVajnX6ogIBJiDyNw7qxHj3hBhcg1jZ25lncseynAAt+zbttd54nVxFoWqqF7VRoBsDLobShEOB91ZN5Wg+7pzQ6UXiw1OwUqPjabS9UIrEok2LsxcXm7uq2Kk8laC7iAp3vZn8uhCPJCxBPWlCPgbM90U+JrmvU6y+C2SLRqfKUG3V/5Mty4HwWfn4g2nP+HtIlk3RwMF3dbrOdW4L24Ohjs+ju86bMP7usfzfw8nXEXtK3+gj+qEddCty0STWytwr3qPzd9O+l037fObs++0blyUHW9nQ2a+/cks4+fBxeJzTj1qiGlMd26mO7e8XKqM5eV3ijlz5qBu3bpwdXVF+/btsWfPHlQoNI7sj/sgb5+GyGsXy+51Io4h8/uucL+wAiOTfoe3kw4vD2iIHW8PwoRx49EkJCD/DpVnAKpVccNDnWrhzyfao2GTljBAEmPqHj/5AH7+4lVsOF06pb5FdSyzOv7Q9cNafRdUG/SqmEPbHlUvpRvwKNV+rNx9zLFGKLocJAZ2RqihBhI8G8DPMhA0Bt31qLGKvUym8chztOyL6gHGpiOWy6SS0Mm4zIeLM0Ys5hwMh34UF9/RPYVPxvexv8NgflE1+jYJxM+TOuARp+14/spTeOv3Hf99MyyDHgl7fsaD8/aJ9edXxRv3Pjcdg1oWvEOqavMg9D1eF5c/xjx8+etfxS+vvbgOLaP/EQ2g8gW7kgTptYsYq5mFm4aqBY4PzNdMy+LzUUeKLrSLrnxFCbr3GlqiRwM7QTeVCDZVmlU+hI1YVoQDRw4x6BGZmIFHFx5BSpYBO0OewdtPTc7dsSqAVKcH3J5Yj2xj4N3j6HP4cXvpl5rTj/nr1zuhV/Ys9O87wPbBJQpI2z2C1ZqhiDJULXTHWLbIDllmunF6GaamfIhHNRttzy+cGG5uuOPnp+xwWjKNab3g6DRaVH658mlMC39IdPG9t50SINrlVx9Sx8fFxdfxO77fWja/G3HnlM8mTaM08B4HPg+egXB69Sz+9H0C0RnAjzuVMZJFEq78vp+R66FN/fzfB+muSkBWX4rEVVvTf6VGo8rpBXhbswzVfYzj9ImxvJwylRFJtsd0mzoT01RheX8LpWCltL2l6lrRxqxf3wd15m3xWcmpOyBfOXQ+1ZpD10T5vz48ZRm2X7STPSwi+r9AFWs5Gk+M76w017LJ1RuuE//ESrkXzkan2y1JthKrTLt3jUqbg5UKOTNP5To1jcp3AIt2rFMi4SelQu/snbsj79cAUc51kCK72Zy2Sk6yX16uClQCOAp+HKrIu7bLPLd45wbW/TTyUrV7BNv9H8af+j7FH3N/eYN5CtHeLYxd3u1oZ5wt5Vpq4UG3vs+7aK1bhHn6kWgQYFFeTp954zq6lfdgk14LdZYyXEI2NUYzT3UFkdG1OoDaoD++qPMzXtE+Z7U/JHkpB8JoTmibBwCTruduL8uDJCYBjUVPCHp1h7aZwQCX28rQv5wAZX52m4yNLZuqbuCCA0F3RMhwLNX1RZxnk/wHPY1BN3XGtzXMUn/8d7ykWoEGUqQ5020a011FSrMKYk3zbCvzXVN5ubK9PC0z2MamZaZx0IL5fhnW5eU3DqFRxgn4INUqA+3tprFdKh55Ar3DvsTTmrXKjDomxinDPKRspGfmf4+ZWr1539myIk/lohzk8ZCykGnZ4C3iGAakr0V76ZJ5yjA6OPGjPA0PqHfklpdbjenmTHe5Wr58OV5++WW89957oks6NXMbOnQobtwonWkb7ghUqly/n5iqZMOCj/HHIeULqlRd+Be6BcPgln5LzPv6lc8HWPVif7w8oBHcTXP0FUbjDL/x86F6cieS/duJ/5gvG36F71/3Y9riTfbHPpaB2JQsPL3sLD7QTsbmJtPweE+l47BdNdrDENJZlGR2TduCvQ7Mv0zvd23DTzEw5yvUq5Enq2YMqoIRj4g4O2P84pQOylcMwdblXhb6BOvQR3Wy6EfNZRmGta+J7pjr9Z3QsPsYNDROY+aInjVd8L7HarRQheOxa6/ivaV7S6Ujq0P0WqQvfRRVt72Gx1PmiCZUy5/qancd5aXu9x6y6w6Ai6TFsymz8O0mZZqUojJcUUoK9xla5useSyQnVzQuZGq4zD3/Q8csZfiAZYkxBUWkXmFBd0YCpNtKwHTWuaV1NUXe5enyjDgfo96LNftPl6i8MS/dxncQ9uODyEhPEcuw8NFO5mlAHFKtOVwe+xfZGk80k65j3dZt2HjWwSyZg6jUlqak8fNyw5i2dkqvSd93cKTFe6JMeF8h/89lY6b7GizG5Ft0/LUZKBDTNHGitDxPeY0so0vGLrylWYqbEZGOZf1vHYU68arIVKUEdrAeymKH1Odt6Jy9xQ5lxNF/S33IDx2Im3x9MMbnvIsTDV/EsJY2KgtscHJxw9tDlMBn0f5wJFuWVToifK84O2hoim4N8ldZpFkE3TazYsbS4yi5KoKrWnwnGjPdNWAn063X4nq88nzmxniWgnKD7qI0U5PPrRTnm/QdcX8n62mI7HHqrRxYHKk6gFXbSyHJcHUXNm3bIi7SAZ3CDp7Q3zsYZ9vYdsGBoD/GOFWVXDO3iVqeMcXi/1LeA1hZybldlI2Bm9DteSxsswwL9MNtTkMlm8d0B+QLuiXj0C/KmjrSTE02HuTZb2huPZTBljo9IA34COflOmJ4U3GqELLPrxPnNOdzvyZ5DlDk0THYRYzf1aXGFvo9Qt9TlISUNC75AltTCX6+DG260oiRqkqcvXL/rwVeWYG3NUsRor9l3SXbOJUfER268wXdSbZ7mFhN4Wfj/1bd3lg/8iie1r7iWKY74Qqc9JliVhnvEKWHhk3VlIC8qXQDl6IK/z97xG803tFNgWQ8cGPFOOQhSEqw/ZlMU35rUlQ+8DDN8R7YFGed2yBS9rea4lE2lpdnqT2VihNTIzXLObWN90mXXeDhZjzAYTH226q8fOPb+FH/sWga7GUqL7fIRufLdGfmZtpFdYOJRSdzQ95O5PS9tKAfjrs8KXoRuVvMY28Kut2RZT3P+OVNeCX7R4xW78+d9z7hCjrLp8T3tynTHVn7Hvyp641stQfQejx0kzYgtNoIVBR3Vab7m2++EZ3Un3jiCTF92axZs0Sn9Llz56IiiWyqZC2om+GMlYdLL/NIwdm+2ZCXPwKNIUtMQ7Og5e+Y9uIT1uWURRHcBlWe2w7tsG+Ro3JDF9UFvBQ6CS9/u+g/mdJIe34dnl98RBxRbVTNE9Pvb5u/IYwNqu4vYkvws/hb3xMrjjqWKTxnnGoi3xQiNI7JpYoYv+iefsP2AQdjJ1xqOmEzoEyNxsP7hmCB01c4ey2iaD/gF9ZAdfOAKImb5/o4XujfEEXi6g2Xx9Yix8UXLVXhePDyK5j616Gy72quzUL67+PhEbpaNL674tkOy5/qkq+Uq0AqFVzunQOtk7coI8ShH4veLTk1Gqo4ZUfxRpUO1llqC6b52C8bGwRZyUiA27b38JPztwjwcLIei20sLxdzPRcUDN1SOgRfMQShbs2aBZbuomZnGKq3hqukRZ/0jdh2sZQ6V59eAc3heeiVvQt9XMMw75H2NseVFyqoFVzGL8HvzefjrFwPryw/VWpN3wwH5uLK1gVQQ4/HutcttKLDNE5y/5UCgu7MJKiMJeJZ3vWsn9MiULCZ6TN2raexf1bDCogkoeaJr/CM5l/UyA5DjK1GUHmdWirONhg6Ynh75YBeodyrQjP8a3xX7TNs07fBlxtLN9v90+6rYtjLede2eP/eDg59x5pQB+j7/G/gQ8NcLD5YtLHdqS7VxP+HQ3JzdKyTPwhKdVV2fmtIt3HFVpWRRUBmObaYgm6dkxeS4IEIW9v01FI8uLkzPtf8bPv7yNhMjV73bEEzGliSZWSHKZnUbapudpt05RPUShxYpCFR3aL/KNnvqsEA7b+v4sOIp8Q47cd7KN2sC3NvnSw8qt6AA2cKnzFBNma6Lxko6M7zW1nQ/6VUJVtM/QL8fKybYZlKzfON6dZmQWUMFjPcgnJ35k1M44Mp6Lb1vW3JYIDh+kFx8aSqGZoH2+6CbalrfT8RVNFynS7q705GApwiDouLUdX72BxGZanFyalY6vwZhmIvrt0uuOLAFKzSfl3eKqDeO8fitMsT+efqTos1N0bz98r9v+Jy6nc8rfkXDaVb+aZaNZWcWzVD86xmHrscl3eaMVJAZYKg1qB+oLLuHQq6o06Ze+00Di7gQAn1YpA0ooN3fEThVTfXjFVrVgdgTcx9BmwH3VKmUq1ocPPL/a7s+AT+V/MbMQbaMtMtGRupGYxBNBoOxpLmP2Ka7uHcoNuY6aZx3+Zx2uYx3dZjv2XT/NmW3cuTIzD42NP43enz/OOzjQF9smwxxRhRqaF3Vl5DNmbjrWTEo6qUBo2zq/XvgbOyvjyQbV15aBzjnWkxZRjUxukfoTNnus+1+QBv6p5CpsYXqFIDckhHZLgUfECqUgTdOTk5uHXrlsgyW57KCr0eTUk2aNAgq9vp+v79+1GRfHK+Oi4baohGLRR4rz8TVToB99ZPoNrygegESPMwhvb7CR/d30VMP1AikgSnTo/B+fn9SA1sjxuqmtiVHIgH5h3E/7aHll3W9MxfcPpzPF6Megs+LhLmPdLB8Yxc0xEIGv424uCLzedjCs/ApMbggnGu3XxBN5UfT1qDwer5uCyHINzWD6IxixZGnXAtS1dNvKpD9q0jdqwa5ZzHpSKME8zJSkcivDFfPwwTBnUvXpAU2ATOj/6LHKcqaKcKw/CzL2H6mhNlF3jnZCD917HwCN8spkT62P1dPPvca2JKrCLzqganIdPExQmqrfjgnxNF+8yFbRVn1LitbRPraZnMbofixWvPYK3zu7YzJsbAi8bTV/fP88PvWxtxo5dgcM4MUbJqd9luHjaXGtpt4GPZ5dWY7R6l3offDpRCRUz0WehWPS8u/qAbjQceerRoB0DyqtcbU+4bIcrkqRTt6V8P5dtpK7KMBPE99nbWtxjgcgETuhRckkm61vZCO+kypNjz9sc7x18xb7+gwDwl4sZMdzUpCdHxNnY+1C6I1gSLKhZbWVGVqfu9FF54MzUa82vMhq7S98So1rYbXNnUehyG3TsJKknCpnMxOHS1FKZu02Yh6c/n8efWA+LqRyOb2R0zaY+kzcCMrE/xoGYnLu1d6djYWqPNgY+jf85MJAX1tBo3aBJdpR2W99yMR7Vv4orNTLfy/+JW3h187xq4+dQljMz5XEzzmO97LuEanORsaKFGbVv/BwIa49bEg+iR/R0uxKQ5dmBckvBtg0WYlPMWPJv0LXz4jwWXvm+I8/vUu7F06yEU29UdcEoME3Pr6ur1s+5sXYAxoe/iI6ff4XZzZ6Fj83VRStB9GTXNByptdc+mHjJWUpVmZzGyb+54biMxZzf9LW+AY9AhtMWrWKQbBC/fAPvjg1URuFxYpjs1CnqdTmRMNSFt7I9zt+Aq6fF8SBje0CzDZkdmSLAUtlVUp10w1ETrlsp3REHUtTqL8/bSZRy/WcCBnltH0XbdMEzVLLR5gN9FmywCT3V6lPVvkfHgxW25Su683sQ4RpvK/q2ahv31GL5PeFKMBbYabjdoGp6rtwFz9aMKzHRT0G11IMwCNZqlOC4xQ1vob4Zcvz+exbuYrbs3f2WFJY0zcqo2Ehed4s4VvG+TGo2ciLOiai1v53LBvyHOhDyEP/T9zd32zXTZUBs7d0ue1lVzPsbybcv9zfAO72GadgKyXY339Q5CckAnMZY8b6ab/t/mdiQ3NlwTmW6LoNs8lZfF/NmShMC4/SIplpKZZ2y+aUw5PKz7kYjXUA5+qbOT860vyXggQDYG5madpuBNp3ex1tAZ6ZZjzbXKesqCS255udo5X9CtM34m7fUjutsVee88NDQUjz32WL5AlzYIHe3Q6x3/QS2K27dvi+euVs366DBdj462/WWXnZ0tTiYpKcqHRKvVitOd6r3hjXHgr4fQKOprMY7w6V1jMbRZQJGyC3npdHocPBeOvtRoTDcBTce8jYdaBUGnK8XmZ1414fr4egQn3cawbbFYczoKszafx/Xzh/HiQ6PMP5ylIuYcpJXPiQ/wSbkBZtzfGiFVnM3bNe+5LY0C3NC4micuxaRh1YmbGN/J/hhi9cKhWBZ/Cw9L76JRYI/8zxvQHF7+6UB6EkJjktGkWp5ul3GXlDFKcjAe8nW1uVzqml0hJYaLcp0DYXFo4O9YALokozO+yJqFGt4uWNMysPifbb8mUD38F3J+H4POuovIOfoCZjn9gOcHKtOVlJrsVGT//gA8Y46IcqlPPD/AK1Meg6+ruvjL3vIhpKekYtyuEMREpGPZoXA80KGQsbBG6ksbxdHHXYY26FG/qu1l0HggIPk0/CQJ16Pi8t1HigsVn0XKdtas6pbv717NBiJGrUz7c/12CmraKKuT6vXDsgNXsD2nPh6u4VX4umg8Eil9kjB2czWkXU9CnyoFf94LlJkEwx8PwcWQhd36lsjp+RZ61PMtle/Jb8e2xCdzfsGrmbMx7ddP8fnj9xTYsbkg0oEfodFniGaG9TqPgJu68PfsffBL/OPyHZbr+mDP5eEY2Sr/WE0p9qLYflcNwaiTd/s5eUGtcYdKlwF90g1xANjqu7j947h/VyPcSsvEkiou+ZZHFdgS6gv/ogWN/72ViB717R9Qka7vgyYzQWT7smp0g69b0f5P1KnqinEdQrDxyHnMXrMfvzwzuOCKiUJIO6bD5/zv+EW9FdPqLcTw5sX4fpGcgbYPA0fmYax2Nf46OlYsoyP2himBAPXoyPu6dF2ndkNgdXqu2yKTmfc+6oTr4v92BPxRzzv394H4uys7fhk5etxOyTTvECuPuyoed0MORGcb25QEhtSHlytVNulwPiKxwOEgpn2kdedu45ahNb5vFlS09RjUHtqgzrgeEYWLYWHi9UzTYRWFvH8uaDf3L30vjOvexOFl0DQaABw4j17SCew4H21/eIFBD5VxiEy6T2Mx5aNWa3FAwiMYTsZO5DHxSVavLyVFiP+DFHQHejnl/k2bgb7bR+GESyTuT5pvvcwqF+wKnIBpuksYXMXG76pPPfF6VAYcERVd8Pt1D8Rb9Vbi2JkzGFk7wLF1k5mMp6I+gkqjw8TTQ6Dtb+egrQ3yubViW2wztEO/hn6Fv15wB/Fe2qlC8e+1ONxvZ0YDKeos/NLDUFdyQ7xf/t8iFWVpU24gUI5HdFI6Ao0BtpQcKdY/Bd1V3TXmx6ldfcX/hapIQWxSBrRBxu7Ut8NQR46ACjKquKhyX0ftBh9Puk8iouj+eX8rM5LE61DQXc3TYjtbcDrxKza7fo9/crrgYlQH0bjXnqgMDdZntRBB2v/s7FuZX7vlWCzYdgShuqq4fjvVbk8F1Ykl+ODmVLRy6gZ33wX5n9O9GkLbvovfws6gU1Km9d9TosV2ouo9Zw8fq795udB3jiw6wZtuDw0ahZ/1tdDKNfc3391J+c6mAJluk9ITxTqjcd9uTpJyv+rtsCPkBSy56or6xvuJ1W8MhnM0HuL/o9agB9TuYploSGV2ZprVMqkyEqE2ZrprmZ7b9DcqMU+5KaYaS0zLyg34ZRka05RgLnn2VQJa4LhrMm6mpiMlI/d9qrMzxOeIDmqpJfpe0EIlacRrO0OLjOwcaHNyYEiPhwcyxedKe+MI5Gt7EJhMyzwQdzKHv0uL+sSTJ0+GRqPB2rVrERQUVKJAsDjyvp4p2Ldl+vTpmDp1ar7bN2/eDHf3EmRw/gNugU2QFeuFGvp41I/ZjG+XpqKJT/GyjnoZWByqwvH4+9BF1QzNGjSGdOsE1hunWSgL/dwBr/oSat/4E5PjNmDWrL3Q1h2MFn4l/7y45dxG14ufwsuQhV36VjgbeB9qXj2K9coMD1a2bFHGrtlCDRselg8hyOkQvtj+InxuK93J81LrszA84So8JBmxqgCc3r8TZ2y8DXUGfaWosPnASagt1i11YxyRcFUE3dcMQTh/eDcu24g5aiW5g4oWKeiefvA8/BKU5iAFoYOD351QIxOuaOOnx5ZNG1FSvnVfQeewL9FTfRYr9y7Fa+Hd0b9GKWW8ZRntLk5HzayLYgzRW+o30LNuLRzapWSbS6YmulWXsDIcmLH+HDSRp2E5RMkWSdZh8OVtoN0O6joefPkI1tuqPJNlDFZ7wlWfBteUq1j573qI30+jRtGbQaPJrhuqIfv2Taxfn7/qp6qTGtE6CX9u2IWmNv4vp+QAH6SNFZUog84fxnplREIhgtHQV4UT8cCeaBVqFPB5t0s2oGPYNwhOuyF6PMzxeBbjMsOwfr3SWKzEZFnMdBCiisHzMR/h1R+BIXWLfgBOo89E37M/iB+t+YaR6ODgMgYma9AVQBfVeby965TV/00Tt5wsnHF6Ghdz3FEl5hrW5/ky6aPxRRVdBvx1MVixZgM8LRIClOSMTKQPg4SwkwdgHKlg8frZ4vWbS9fx64nLqJVuv/S7xa0/UN+4Ix6sSsL69etRVEOTDuEdl4XYHN8Bn/4uo2NA8f7vemfcQK9LyhSL3xgeRB+vWGzYoDR+Kiq37EYYAEl8p8zbtB4eMSEo7FiAW1Ys9p6jLJsTVLdpW9subb55/qjYlaFxrKvXrofFbDToeuUEAo07+Krzx5H3v6WnkxppWgl/rtuCEIuEVq/w06BDIzflQASeOYJMO9Wo1Z1VSM1SYenGfeharYD1LBtwM03CrSQnOKlkZF49hvVFLE5x9p+IuUneOJuoxkfL9uLhBkUbduaRHYN+V5Xv2fWagZhwydHvGMAv1RM9aL2oTuPZ7ccBOyOyPLOi0F+fLYY6pcse+T+/soxhkjOc5Bxkx4dj3bp0c6+khtE70Uw0G62K2zcstrcsY3hyONylHMgpkVi7br3VZ2dvuPK7m50YbfP/S38nP8RmOyE7KRKr/l2PvBXolnZfUiNergZDTCjWO7hyOns0QfW0s2iavAcL//ZENQeOldO+x6DLynf1UXVbhBzZjdDCdo1kAwZL7vBABqIvHsL69banQWsWsRk0wOyqHITUW5exfr3ST8akfToQYpz3/O8N21DbeOymYfQesf7j4IOY67nvv2l0Mig/7CelYOfBY8i+pnzOB8TfgodxyrAje3fgtMV6TYyiN6PG8fNXsD7H+v9tjNfzmJWlg0qlwr4dW3J7ZVmoH3MELeRwNFdVx6rth3C7uv3/W+cSldcKcDFg2+bC9n/q42enRojSSvhj7U60qGr7eVvd2AXquBAuV4fHmSOw9TMTLmJbDa5GJVh97qpkhKMPFcvQaOvk2+a/+WRcxUuXv8Y4Z2+8G/ol1svKl8qROGX5s1OV73s6qOx3Yz8eV+uxO2KouK1W/F6xb0j7TBHXcv9vbMruhJ0GNbJDr2G94Yr4XI3S5WaUzcslyxgBtRiSpUvLXSbS4dpF0OGbFLjj3IkjyLB4r90y9KBvYAq6V2/YjKrGgga1PhsjZOX7JylTn+//nTZT+T3cvf8QEi4q67jdjSugtBb1Ktm7czvcNUC92DBQeztnSYdDR45BG5aN0aeewGhX4N74+bi0aTtaRCxFiG+3Avfl7wQZGRllE3SfPHlSlHk3aWKjuUAZ8vf3h1qtzpfVjo2NzZf9NnnnnXfw6quvWmW6aQw4laR7exfenKa8qXyvIe7gH4jLqYLLST54+cEuRctayDLkE4vxxuWmOB6fAI1KwsRx4zGw2X8zPmK4bEDWsmVwvqrHm+ol2H3tDA56TcOzw7tYTRlQJOm3of9lGFz1iaIEf22DT/Ddg93zrRc66kT/SQcOHGh/LnnZANXsd6FWR2FF5mk07/KKzVJC6dZhSKdl0Xm8Rs26GD7cxty0iddQM242DiQm42LVlzFsmEW5mEGPE4HLsXjddrj61cSoET1tL09CE2DuArSWriA2S4WhQwcXeFBLijiK/afOIyknEAGeLvj4kZ7FX695n/t6BxzYsxH/XOoB3JBQs149vNSvfokPsu0OvY3Pj43EJ6oofOU/DZ9NHms9x2MJDdAZcOL7fWiUtBvXXO7Da4ML+Z5Kuo70S/5IpzHZdTvhnpGd7N5VnfAjTaoqmvLUb/sgWoXkjvlT/7ue5kARme4BXdpgWJ5sqhR5Aqprv2FLtjcC6z2OYTbKorecjwWOnRQlgfeN6u7wew5snogJPx/C1bgUtBo/DCF+RcuASbu+gCbttJiz92P3t/HDs/dYj+0qDX06IXNeX9TPjML98XOR2H0RRrcpoAGaDaodn0FtSBNjfAM6P4AHhtAuogOye8IwcxZqq2KRk52BYcPutXm36bP24lpqBhb1bo/u9a2bdqlTfwfCIsRY1Cbtu1tt++vxGTAc2ivGqT04emj+/yNp7YHvZopmMdnQYNiwAXYXVZqjHCTepO+AN8f0Fp2zi0qKqA7Noh9wv3o3/o0egTcfmpR/rGthDHrofx4odtI26Dui+4hJGNvesey0PVrdLrhcXoN79Bvh3egn9LI3JZ7J7DbYKcXhYc0HePr+J/I1+TR9x4+rEYOa55ZjSU4vNOnwAhpXzy13VM+dam7a9NrwAVZjT1UH/4dWzovwi6EP6rV4EwOa5v4uqi+8ZG6ON3P0YNul4LEX0Cj8A4RmZuOA3xcYNsz+51G6uhNpf7+ADHUvhDWegjEjlUZsRVUtIhn3/ngIJ+LV+Gpi78K7n1suw6b3RPZoh7417hk2AMMdrDYQ9AOhmzkbftpUuGZGYOjQJ2z/Fuiy8VWaJ0KvhKFPuyYY1jt/U1MZD+G3o5FI12vQre8A+BqngZPOpOHEhjCc09ZB/27t0d+isZjqeg3xGxuAJHTt/WBuOXN8GE7GncYhaNCjbWsM65q/E7s8eCDu/WIvknK0aNShi92KBBqXHX9gtwjon7xvoMPDtFTHooGNb2Kw+gj2B7yKYb0ca5A3I80NWRc2o27Hfhg+3LHvMn38L8DN3ailDUO3Po9ZVWeYqP9cAsQaq+oG9bSeMoyWd9sh4OBBkf0PatEeg5oZ95+TW2Hab81wOE6DJzu3w+Dmyu2qQ+FAzFpUlVIRUr+J8v5kGerTU8Tf0zU+GDNyWO4LpESi5d/vYqBTIlb5fIRhw9rl2wfIOHkcjQM8MXx4N5vvUwpzApYvE2PxDwXWwbBhdhqkpUYheOU3uKqqCreGw633u+zYln5GVGF61Wxs8/NJ9IuUmWBuojo+vWeIzcqsiKhIbD2/FvF6fwwdOsb8/0G6ugO4RLNZeKNVo7oYNkRp5oe4i3C69DGqSoCXXzUMG9ZWVJi5bN+E41eSUSukM4YNaw2kRMDp+6cxSKPGIY8HMWxYVyCrB6Yu7Yjd11IwqU0LDOuoVGXGHriO9TcvoWpgsPLeqeHZSeXlXL38MWwYhf9GF6sAWQlwMWRi6NAHzcurXvILkKRkusf17WU9JCGtPfr/7zDCDRpM6dIjt3w/NQo4TQk9CYFBIRg2rH3uYxKu4ta1P3EgwwlNW4037wvJSxeL18mEC0YOHSz2VVVHo4GIJXCCDi1atcGQpl6AMkQfAYFBaNqkOZUpiW+NAvfl7wCmSurCFHlvt1mzZqLU+7/m7OwspgijH9kxY8aYb6fro0ePtvkYFxcXccqLNtydvPHMerwEdfvncfrrPUiNSsWG83G4p6BOvXmnnVn/BtRH5mOEvj3Wq1/DD+Pb2Z7Ptgw5PfIHtEcWio6KvXAGTc8+gi+vv4KJE6c4PJ7MLPE6MheOhlvKNUTIfpgd/AVmTuhlfw5bR7Z1y/uAA//DCPUBbDr/MJ7ra6NxkTF1RSWtLWr42H4+XTra3fodNdXeeDzhqTz3ccIZNMRKgxaDqnnbX57ARpA9q8MlLRo1Mi4gIqUP6toaT2Qk7/gUva7vw7PqB+DT6x14updi+X6DPujaoA/e2BGGrzZdwu87TyM7JQ5v3tvDobFu+ZY1OxW/Hr2NT9ddgN7QFnLD3zFrYo8ijWt0BK3aFVW+Q0DGTnxwKBXR3T4teFxyQAM87/czziVexXNNggr+rAQ2FUF3I9UtXI3PRPu6/vnGjlLQPTDQxjaOOSWm5nNVt8e+xAn5/x5xDKnnT8Ebnmhfu2aRvp+6uEdgp8d7iNa64qe9zTH9/qLt0C9PrI9+sje+kh/G65MegJ/3/9u7D7Cmzu8P4N97Mwl7IyACIu69t9ZWrVo7bK3a2traaacd/rr3slNrd221y+q/raNad9W6994LFEGWskfm/T/ve5MQIAkBwQHn8zyYQZCQ3CT33HPec+qgCiggEqrxc2H8fggGKfbgq8Vv4HjUdI+aFXH552HaJjfM/Ey6C28MbOH5Y6QKgjmiPXB+N5oU7EZ28chKvQPY7PWz1m7KzRv5V/6/uz+MGZntsDErGr0LDOhs+/6FU4iZNQhzVI3wRtAH/HOqksBomL3DoSjKgFfOURilG1xOiVjc8zesWvgr0kN6ISHCw8emotjuMLe5A4qDf+AJw0zM2twPT93gpquvE+aNX0GVtZ9nVv6JfgYzusdeelVbv6eA439jpLgJz2zcg0GtHHbUnc3yLWDjJxXwiWwFf2/XwaUy+zAGYieOiJE4k6NHm8Zlpahnb12ISV8twnllNML9y8+MZs2J4szJ/GBIRoGh7Dln6yeto5MMPo3h6+p9ValEy5x1aCxq8WNantvtUTr6NwIM53njtebtImu8/9EpNgSD4r3R5MyfmLMhGC/dJq/zrZK+AMa98nzwv1Qj8HGXGKiq896rUsHcdCBwdDHal+7Ciey7nL92VSqszGuME5ZAjI0OdP533vI5ZhxazfsrZBSYEOZv/YzrdBceWhaOLLMedwT5lP9ZNqIpJ4lnZ7OLzYgItH5v8zS8fm4utIoxiAnu7fz3qVQ88NyRnIOkC6VoH1O5Cz5rNBX0w2B8pIzHT2HPI9CnGr1FWo3kQTfrhTLj0FGoBsnrht1h0ybmn1Ygx3w95rSN8nh7EJt040F3R/EEDqYXYmDzygkUU/ZJXsabjEj+HqKq+Hkd0NjeBCyr0Fj2u0PisNLUEWelYkQE6Mqu95WDb1Zevr/EJF/PmnWZrUs3vUPL33+FiPi0JYgSlZhZqK/0t6Wxki5Wlxbk7frvjpCXtMULaUjKKnB9u/S96JoyC17KWGyIdPK56kSHUCBDOIKkjBCXtzdbe7SU+jWBl9Z5g7uYdU9hqWYtnjU8ggLDLWUd3OP7YmrTn7DucBpu9vNyeBzltfH+KEJBifX9JvUYhu59DAmqSHzv1Ve+zlt+/1ILZhgNJfJ1qmAcE2JxSroAf51Gvs5Yinj9EfQSD6PIaH0OCuXPMFbCrdM5/G7e1C2AB90+UhEMkggf62eQ5fo38NBXf+OAJQ4v+pb/GfbZJelOwlxUhGKTVPY9s9xboRBe8LPdH5vUbXgs92M0V3RElnmc/XsGewZezTuw8/dijRfM1tZiZgjlqpRElQoKhco+p/tqj9s8fg17GsHbvqZOnYopU6Zg3bp1uHDhQrnveRrp1xTLWs+cORM//vgjjhw5gsmTJ/PmbY888gjqJaUGQb46PDpQXif00fKjnjWhsVhgXvw0D7gtkoC1Uid8dVeXyx5wOzZZUz2yHoX+zREq5OOtwjdx/Ivb8eWSzR7PVWbLCJbsOAZTXjpvivNJ+FR8cN+NbgNuj7QZxU8GiXuwYo+T+nQmfT8/OSTFuh7fY+1Qzf6+rKysSk0nbJ11m7pbh8casjWRj/yyEnO387rPbObrPw2SAqvV12Fc96obStUEOwjx7k3NeFfu+w5NwNtfzXQ6K9UliwWl/32Gwg/b4vvF//HGLbd1isLn9/at9YDbJqT9UH76tDgP05fIHcFdYV1E2Yg21ohugJMdmHJC5cCFdXE9UaFpk3ThtEMHaycHSqxjw+KE8zjtbGzYtu9w57HJmKhcZp/H6inBJwJRSEc38Rgy9izF2QuelTkxyw+m43+7/HCd/lP0uvXxyt2Ga1NkRyhuns7PThIXYM7sL5DtYWM1w7/vQmkuxU5LIloOHFNlp9+KFPH9+ClrJFNpJJ/FjNy1M9ANB/naRKe9J5rdgNNRI5EihdvnN3M5SVDrcxAi5Lnses+I1rnOLYUzbpup/XOsGH9beqF/Gzezkz2guOENmJQ6PjamYP1XOOrpjHAmbS/w71v87Ke4Gy+MHlA7y8iiu6C0UVe+M9ni7Fwcd9cskmWLWBNxqSm6Nq/ivS1EDnJY8FxxVNrZUh3vnh8UGFT5b7CNDRMu8GZqFeeuZ0l+CAkOdvt7LUov3vBUn3HcdTM1sxGWI4v52RXoWeVoqKp8ZnwLr6l+gWrPbI9fP5aLycg1a3mVSNMeI2v03qtoJq+p7K/Yh3XHnE9LYPsntvc3d+8ltgZaKQ4dzNnjZ/t7IvwrvAb9GtkDxXKjrhyacjmd+WxlG6HpsoP52S3wLj6H5mIKusS6ec6d8WsEY4SczY3MWFt5FJcT29hnTrGRj2Nzt165ItbJmekonMSeM07Gk5qNEHPl7Vfv39T5AXJ75+2ccp232T6LrdFkufdXnXxwOUTIt48IYxWHtuDO27fCwRdr93KNYIK+oMI+zKm16L3lITysWOyyiRrn3xhmpRd/ryjNdLOEyLpvdtASixbumqiV/ZEYv+MWzNO8jZI0F+NFjSXQFsvNi5UhrqdHiP5RzjuYq7xw2BjJx1SWG8dna0omSNAX55drkMZKu+0Vf2ofSHwxYllTNMbWLM1+u+JsDNp0N2arpqLI1txQ64997V7Fh6Y7K1UQCtbGa6yJnuOs7qLgNlhp7oLzCHZa4WZrrubYcR2CiBT/zthlSaxcqejQvbzIocFbTo8X8aDhGT4ZwP5e3HE8JjVdhUnGp+VGamz9uZWCz+mWt18BtTcS9UrzKOgOCAhAYGAg/2Ip/q1bt2LQoEEICwuzX2+7TV268847+Ziwt956Cx06dMD69ev5WoImTS5tB+Vqd3/PGDznvRyTi6dh6rIj7m9sMsA4/2Eods/mAfeLlkdww91T+NiWKyqsBXweX4/izo/wI1t9hX34bmMK+n24Ft/8dwo5jl0xHeWn4WDKBUyYtQOPrzFigmEKvoj9Eu89eFvNunRXFNkR5oBY6AQ9YrLXO53BLKUfsGe6K3Uut9H6QbJ+2IQYziHLcWdo7++IS5qDaCETCVWNZus6EYubvcPHSmw77Xo0jPTfR/z0T3N/3NS3Gl3ba+Cu1lq08yvmO6dvXJiC5Z9OxF+bDlY51kxK24OcL6+Ddu0b8DXnYJRyA14a1gKf3NG+RtlyTwldH4A+MJF3W211/CvsSHbxOJbkYPXe03w+JBs357SrvCOHma8VA4akoT/hUcNTyNQ0gb+Tkj8EN7OPDTubWXlnSTpX1rm8U1WdyytiO6Rd5DGDzyrmYtpKuXuwW0UXcPLAVkyeJ9eijerVyvMqmksgdhgLfRf5IOlL+ul49fs/nY/Yq+BnwyD++putuw8P9HNeEuhWrLykg63rZju85VxMQvim1/Gj6iO+c+4qwGxsCxQcuy5bZ3Szgy1Ou1xbCYPfxv+if8Wv5uuxN8X5aCF2AHLdcXlsz5BLPUDqHw3FELmj/7PiXHwyd5nHoydzVnwAhWTCcnNXdLj5KeezdGtI2/dJZKiicNrSCLM2yY+dM+aj8hrBteYOVQapkvW1xUpRK44Yso2lchqQ+csZv0ghG6mOs7pFJU6FDMR6S3v3VTIKJQTrwZQ20jEcPe8ioEtaD0VpDrIlP6ibOu/CXh2+veTX+gTxH/y0vor9AavVF0PRp+RjPCM8h/v61OD1wyQM4iethDPYesT5pJrCv1/AncJqNPIyIdzP9YGxRD8zIpFdbmxYZk4+i4mgUggIspac2/nagu6ccmPDLNY57JW60zu6cApPJT+Gf9Qvuu5gbp0Hv8PSour53E6o2ozkp0PEHVh1uIou5ik7ELpoLG4SN/MS7uo0lZSiumCB3z14yvg4dp11EnTnJEOUTLxBqX+4i/3igBhc8G6GJEtEuWCxdMfPGGpehwAUIMzxuWvcFav7zMV9hufLupdbg242XqzcuDBGqYZFKz+GyuKM8h3SMw8jPm8rn2/v9rUlivaDaUHFSeXmWjsype61J0TaeFI1JQiwWA+ee+cd5xVOlVgPurEqH1bi7BKrvuBN+i5UOtBin1/u+NiotLAorAeTbHOv7V3JdWXvC6IISe1TbvwXDi3Azflz0EY47TAyTN4XZQcm9KXW15FXAPZH3oFZ5hvLzejmf7pXIG/ExjqFO04gsAXTbJ+s0sG4pPV4vPhrjFGsKR90hyTgp2Zf4H7jFPhW3Pe03nedUMqbVNrkB7fDKksX5Kkc3s8FAWpr4kwOuuUgnZWtiwoF/76sjsfXXkYevdrXrl2LNWvW2L8qXna8rq5NmjQJycnJvCs5W1ver5+cwajPtLknMMnyG1+jJ237Fov3OW+gwd4I9bNugurg//GN9nnLYxg2/lkMvMQj67VGpYXupqkQH/4PSV1fh19QKLILDfhg2RFsmzocyz+5H6t+mYqdC2fgwNzXcGr6cJg+bYMvvv4c/x3P4m8Kw4bfivfudbHGriYEAQpWYg7gJsWWyo+toZiPUmJOKhPcdowVghPs2cwkx5mx27/F/Xlf8pFBbjPdTGwfBHYby5uZrD+R5Xy8VMp2CKfXwCSJ+EVxK8b39GzWao0FxED3xCYUJN7Gj9LeiyW4buUQ/Pn+BCxZsRypFx12ZEpykL9nAVK/HAHpu4EIvLCH7wB8rHoU/SZ+iIf6Xfq68CopVNCMmMrP3qNYiVkLljs/QLDpcwxf2Q/3K5bx9cVV3q+wltD7ROOEFIUT58sHTseFOCyzdEd4iIssiX80/yBl3UNVecnljgCz161wUc6Un1K3RLybJQWuWHo9hVJRh9biGYQe/AEbTriZ220ogv63MYj862Z0Me9B32YheGV49cqPL4XmxndR3LgfNgsdsSrDBw/+vNNt4M1e++/uVWO44V2MuvX2mlW3xHSHRVAgRsxC8qkKjcwy5YwHG/fXLMLFQTVjKX+sRonrecOuijtobO2v2/XXoc0RHcf6CwjY72yuc9ZxGL/qh7sscgbI5cG9ahA63wdD497wEgx4+uK7+HKFdbGcG+xvG35uPGaYbsG65q/ilk6Xto67khYjcGbsesy39MP83allmbOK77mn1/Gze726V/lYSCFy0B0jZCI50+GxPbMFzXa/w+dRO82qWTPdkcIFpFqXFnDhrfFtxJt41vio0xFwjoQY1iIP6CYcdXlwTzr4Fz9lBzGGtL30A1tCu9Eo0UUiVMhD4ZbZzmfHO/5+ScLna05ADzX69OqDgIoBraf8o5F56/+hs/4bbD5XWnl0WEEGQg58h7eVP6J5uL/r99MjizH15E2YoZ5RdgDLZEDkl3HYpXkYCb7Gyr1rrNlZVl5ur7QymyDky5/XuaqwyuOObLwCEZ63j783nkuXD2pVZE7awE+3WFqhW1w1M91Mi5v4CXtOVh9ysX9mZTnwBxILtmOgYi9ubOMmqHNG64/zEdfjgBSPvWdzK+8f6PORoY3DUSkGCRXHtdlEdsTmwX/jedMj5YJF9ZrX8Zn6a8RrCsovf9H6QxHdGekILje+K8unOY5boisH3Wwb9ZMPGoYgt/zIL+uMbnaQxHYQ0xUFW85ln7Hu/GCJhVXlsA7mXs3LjzlzQ92oDT9thrPO54BbP4uTpXDEhbrJnjvO6nas/jvwJ0bm/sID5HKZbvZatGa7hVLrCC5nmW6HGdxac5F8YODAn5ho+A0dxFNlySaHjLhtjjdj+yy1jwuzGb8AI31+xwpL17JZ3Wxt/s4f+JKfYGcPX+ZRDCpcjL7i/nLZcfn3VMi8V8p0l6LIoZK11DrFwKvCvjs7yMaw5Aer1GBMUPAeVLaguz4ND/Mo6O7fv7/HX6QOhLeGeP0b/Oxryl9w8M938NfOs+VKmHlp0Mw7oEndinzJC08pXsK4B55D/0QnsyuvMKFRO7Qb8SjWPDsAH9/RHreGpmOouB1DC/7CDafeQ5e9r6Dt0elomrMRSpjRXXEUN3eIxNIn+2Bin7hLGoHjVGu5sdIAcS/W7DtRvjTcbMC+uAewxNwDETGJ7o9KW0uI48XzSLKVELNmdtlyF9CTUlTV2VS2fDQ+iO9AsAMSznbkpDXv8lM29mVI3x6udzZqk8YXvuNmwTz2D+R4xyNQKMRo40KM2HInbv9wPrq8sxrXfbwOB6deD79FExCVtYE37Vki9cbPnf/Eo8+9gy412ZmpqabXQZ8wFErBgjEXv8L83Wx9qAOTHqbdv0ErlSIDgZ7NQ/YJQ+mkvXjQ+BxS8w3lAsXkC/LzHeOqxJgtHbDOjE2sWJ5+Ti6BZ40BE5pE1Wz71gXjSPRYfvZZ5f9h9tx5zpcBlOZDP/sWaNK2wyQJUAZE4YuxnWo8wqtGFEroxs9D+P2/QaPWYOvpi7jz643ls438vubh1Ob5eOy33TwDNq57rNM1jB7R+KJk6Ge4Vf8mtmery5flZsrZQrYD2cxVnwlDEfptexifqL9BxsXcSkH3GSnM9XNv1a6xvMO1z9mM3cOL4J97CH3Eg7ixTUTtHJgSRahHfQO9JhhrLR0wff05LNhT4XVgI0nIKSjBfbN3IK0IWBb6AF69w/NmftW5T13jgtEmyg96kwU/bZYfv3JOrOTdcdmOeVzrblU/Fr6NYFHq+AEtY3ZS2QG2lG3onD4P1yt2Oc/WW4Nu9l6Wk1P+fda2hKDKRnbW5UBdxGOVly0wxhJYDi3iZ5eiD4a0qYUlXgoVtP0n87OPiX9h2mI3S2hK87F/6Xc4nJoLnVqBiTXNcluFtR+CiNBgHuxtOlGht895OQBiDbzio9y8Tq2PO5/VbTtgUHAegmThM4d9/Jx8TgTG4YIuHulScFmgyH/GLI9mCnAzSUcXBLNO3g9S5Z6qvEQv/zwUF0/yysALIZ0rBUoeCUlAyt2bcKPhA2w6nVtuDnM5FjNMB+bzs2sVfdCzQsNGTzTSAd4aBYoM5sqVeVGd8UTg1xhleMNtVV0ja/n++XzbQQ89r8ZgzL6Vt1HbY2I/SNa4K75uPptnOsvN6LYSfOTnPww55Wd12ysTQquuoIlog1RVDG+8dcJZhUJBOtQlWTzBpI5iPbA9I4TLTeuaCylOKxtZhv071d2Ya77ObU8d+Mr7DKwh3XmHzy52kO1B81y0F08jrOJSJZ1cxeZtyZeDUNtMbZ7pLgteBTaqi+1yCCUoZMGtNajOd7ydKMJizSqj1Pp35KUiMGMbYoXzlStBBQF+1p+1B9DGYjTa9Ao+V3+JIC8nrx+2Dty6Dr1i0G0rd6/0e6xBN890O8zp1h37C7eIGxGsdPicP78fE8+9gteVP5XLdLOgm8/ptpaXs6bH9YVHe1r79++HxeL5H33o0KHanf9MgF5PwNL9EYiChBcVvyHx75vw1yeTsGDBPHy4/CgGf7Yej6cPx1FLY7wY+An+9/jj6FzdMtXLTKUQcXvnaHz26G3IuO4zHIwei8M+PbFX2w1bdQOwKPwxLOvzJwY/OxvTx3SsfuM1T4W3hiWkBfYhESU56TiQ6pDF9ArA94rReNz4ZNVHwK3ruuOE9LKgOz8NgqGIZ6X1PjEelRaqijPxfugq/E/5O5YekNcW2SVvgpC0jq/lnq28Hff38axTam1RNB+MwGd2oGTUL0gOGYBsMQSZYjAPYthavh2mpjhticBfmlvwa7eF6P7cAjw6sl+dlr+7ohn2PsyCCv0UB7Dhn1+Q6RiE7vudl76dl4KA5sPdl7o5YKXjtrmm9sD5xCpEHZ6JVqySwd1BlTBr0C2ew3HHD/sUh9Lyaq7ndnQ2qB+MiSN4udknpvfwzPdLygfeF06h+LuhPOBmH94v6t7A+w+Pdl4OX9fUOrSLCcHvD/VAqI8aky++hR3TxmLB0qVIP38OF3fNR9703ohdcT8GGDfwOc2vjvCwW7kL3t3vRUl4J5igxA7HAMma6T4mNUaiq+yQLggWlbyNSLnneCMkft6aFeGZ7iq2oa45/2Cm6iMEXdxbaS2u5fBCfrrU0h1Dq5v9cicgBpqndiKv5wuQIOK5P/bj9+0VSoP1BSicMwEbP5+Ak5kFfE37jxO61tlrlgVHj/VpjDsVa3F406LyZYtsp3/3r/x0kbkXbvUk0y6IEKzZ7mhzCtLySsrt4LP1vk4z3Vo/WKwlmqqitLJS05JcpFj7IlT5vtC4G882xYkZOJ10slIvDxxbCoWxkB9ACGjeF36XWFpuI3SeAH1AM77OtvPxz7D5lPPmtsZVb6D9jin4VvUZP2Bdo4CyggGJckBVaV136m5+ckCKQ8cYeWfdqQC57DlMyEWm7QCWNWPNZnSHBzh5zJsPxdpBi/G2aXxZebl1PTd7D28U6L6CTAyXs6YthDOVs5vW0vJDUhO0ia/5UsXGCW3QIsIXJouEv/e7yHaf2cwDxTxJB++WN/B9oOrSmgvwVMAWPKD4B7srlJizA05H0liAJrjuP+OwZj4jr1Q+SFUgl8TrJRV0vpWnCsSc/AUvKOdALMywb+MXiuT3MGeZbvhE2DP/mQVln0Fmh9dk46AqmtX1egKzO8zjpdJO+z+c328/yJPYuBrLJ8PlJm1s/f5RJ0G3IaApphYNxxzzIMS7+zx3yHQ7VgyYC+XXBRulFmJrrmYlRnfBZktrlEga5JYYHDLd3uX2DQVrptsPxTy4tdiC84oZcbV8O9FYID8vx5dj7JFJeEE512kyxrZmu0Bvfc8tkV9//MCVl5PXkDX45+vArZltbtt3eP/ULXhN+XPlfVrrgYCKme6YbW9gmvorRIgO+9clOWidvwG9xEPy8ieVDsfCbsRSc3coWQa82RCYxv2FY43Kmmdf6zz6VO3YsSMf1RUa6lnWtGfPnny0WHz8pR1VJQ4EAeLQD2AJjId55Wtoi2S0LUzGd7vy8JXJupGr22DDoPmY3ifh8mauLpUuCOH97udfV+yxffg//PLnESTtS8PCPWloFy3vNLAj+ltPyc1AusZVsdbLWl7OxgotsQXdWUfKGmyFexhQFV/EsMzvYFKIGLr7RhQObWE/mmiQBCSLTbHdEIvhA3vU2k5ctSiU8Go7ErFtR/IM2R69iZelsnIjH1VPhIb6YtSVuF8VBcUDPR/D2a1/YXdJBB6fswc/T+wGrTEPhtXvgn0czjTdiPv7W0d6eIgFZnkFBTiRUSAHyQf/wk0Zv+OoOBrNwpyPo+Ksa8lYw6c9DjsR0tktvHxqt9QMN1/KgTKW6bn5C5T+mo6T6aXYnKXBkGnrMamdgEGZP6NJ2lLoYMIFyRcve7+JNx+5C+HOmoZdRux19s8oL4TNk3fYsX0tIB+D4FIRjKAmrfDBhK61sqSkW1wQ39Fi67pvbCsHt+a0fVBYy8sfj/JzXanAgoWsI2iETL4WkpVHSjnJ/Lljo2XcNXJidGf/w/WKPTgoxWHTyeyykWkXTkHMOMh3fPbqemGqNSNea3RBePHGQB7cLt15HAlLRmHe5n6Ia9kJIcWnEHL4F/gZMzFUUqCXzxC8OfGOyo2satmQvHm4UfU9Tloi8eW/Q/DSiLJRP4sjn8LZY97Y4nMDJnl4EEoITURJ+jEECIW8aSXPojk02eof5HznWQhrhWNn06CFge84s2Z4lu8GYFVpGsYKL6NJsOvxbpzWH1JUZ+w5lw+x5CIOn88v19WbHcxdKA7BMX0QRnaU15DXCqUamlumwTJ7BMYq1+KZ339Ey8lPI9AxADq5GopdP/Kzi7UjMXWA66ZQ1THe9AduU8/HR0cnQZLa2jPMptP/8R3KnZbmeNrdZ6VXICwqH4jGQki5KTxYEPL5XCDezMlpI0MW3Fivtwc41lLlVCm0yteeENmRr09tL5zmB9XbRDms/022lZa35lVml+KOLo0xfcl2rNx+AON7VA7gTfvm8cdoubkbhnaoWfNTrSEHD+VNQ4HSC2+cmYi7HX5PyoUCFOhNfCleudFPFUSufhz7NcvwnPFhXCwehBBr0J0hBSDMyWvff9/3eER5Biv1XXgQyAKtC4XWdcsVAkvHjue+QjEy8ytnuvM1ER4lIGyVR07LwDPkXjvs/dSj9dw21rL1SOEizqaxgyPll1ax6gu238cqQ2wH2N0F3ax3THZuWXm3pUBewqDXBFVaCiXc/AWe2L8KFwwG/n7cyBZ0Szr4eTmEY0Pfx8OzN2FbaTjfr5KswXGB5FXuYChvjlaYBp1UzNdPe9vXiHtVbop2eBFeuvA5FivikV8iH3hwLG/383LyPGrLMt3lDo6WXISfJQ8aGCv/HlumG3oUOwTqokl+3Qoqh9eqUn581TDKmW7fcCxLfAvTzp7A3SzT7R8FSReG/CNOms/W56CbvSm++uqr0Ok8ywYZDC6aYpFLDw57PAyx7Sjk7piL1GO74C20wrjQGHSIDsDQthFXJgirD1Ra3NYxiq/p/nNXCp4bkgid4SJOb54PQ1Ew/L38q64ciB+AbbduwJjfz6CpLei2rgdnnSztMw6rEt4KUpM+UJ7ZiDtNSzB3e0880Fc+gPXlyRBML34LUd4Clveu47XcnuAlSyrPRz9dZor+z8PU8iHkzjyIlOSLmPLNn3jZ9CXCSzJ5J9/CthOqVxFyeBG+TX8Sm1RNsS3jC36VlHWUB15s+cDNFWailtNuNP4pbYcnV+ahl+0IO1u/em4XP7sLrfDmJWS6ObUPtBMWIiIjC60XpeJgaj5WbDuGhzR/82+vN7fF2hav4aNRAy+5oVNtCWvZB6b7VuD80g8RnLkZOqkEaVIQNnsNhKLfM3ijR+taW1Jyq3oHmisXY/HRscDI1kBhJhS5Sby0NCeondv1rkJADA+6eVnsxWI09rYgN6QLClOPQBEYU3XWKr4/cGg+P6r/p2PQfXC+fT1przbNan/5DK9EFDB1VDuMzp+NLmePo2vucWBL2fdTLKH4MfR/+OSesZXGqdUFsccjMGz5Ggn6NKi2folDnT7k7yEs2/zRDiNSTaPxRl+HLrdVGTENzxQ9jGWHM9Eis5AvqzJnHecHU05bIl1mrISJK/Dox+t4lQ5b3tDETwEh9wx0ggWFmgjnWbyKf8t9y/DVb/tx9EgGVh/OLPdeuL04As8U38uzU5Ob1/JSr9g+MHV/HHt2bsLywqY4NXsHfrqvK9+GpZQd0M+dAC0k/GoehDHj767+rHYXYgynoBCT0aN0A/akjJYPPBpLIKbu5N8/49fZ/cE89pwGxvAKk1BzBl9GFWoNutOkYJcHfCL85R10e6Y7oi2WhD6ItakKJDjLjjuKkucItxdP4ZezuRjbrSzgLfVpjGRLY/76m1rVgfUq3CmswljNK5iXOQB7zvZGR8f38+KLwIE/+Nn/dNdjRrOabQ/5XtEwqnzhayxA/ml2sNI6I9lYgqhvmmOhuhHeC/nA7fuRaDHw7KUtSxtSYK00QKDTQFP0DuFjMYOtHcx9V0/BB2kr8KHIRmV1q/wL+j6HV3Jvwq+7MjHZFnSX5EKhl4M8gT3/HmCj3kRYkJRReelGSbcnMXyZP196NDe6GvsfWn/ovaOgKUqFdJ7tn8kNAjlJQuHehYgXSqALTnT//uMViLRWEzF7XzGy8ovL1kgXyQcwTN7Ol5KwyjLWkC6XLUFoezum7Rew2dIMNzl+9kR3wSldEXILC+XeCdZMt17pW+55tXUk94Wc9PAulYPzPHjDv+Ja64IMtCzeiVOiAkm2ANp2e8nb+TJFW3m5UCHotjZ4K4AXEir+Hq8g7Og0Fd9tzUCpLdNtsUBhkbcDwVoxxllHgqlZp3sWdFsTXYySNdOrhzwKulmzsmPHjnn8n7JMt5dX3X9wN1jeIQgY8DgCBgDW41WkFrAdtY6Bevjn7cGfu5rjHmE5mm3+H2aqW2B+6++r3qnW+CAqJgESUnDmQhEvQVVmyEH3YUtMtcYxCb2fAs5sxH2K5bhnVU8MjhmBwwVemLHmBC8de2Fkx6smaLqqaXwQ39gH392j5Q27+mT8hnDlPt734DO/KXhnZPVmWrMjv96mXD427BeWrbZYIGXKQfcpRDsfF2bjE4bYFhpYVm7E3hS5CY5C5YVlff7Aun+XICi6ee3sGGt8ER3jiwWTYrHsYDp2HdJiRfodyIi6Hp373ojXr8IDJMomPdD40fl8p8Vo1CNMocbtdVCt0+7cb+io3IE9eQlIyh6GuKxt/HqW5W7epIpMJOvZcEKuVEi6UIReCSFY1OZzvJl0GIM9qWKJ628f9/Pqcbb2uB3fobTs+omv81po7o17a7txmQO2A9nl7neRtakZcvYuAYqykI1AJAX1QaM+9+K1tjF13+TQRusP9dB3gUWT8LTiD7w1uzHuG9YH35/w48Eva4o0xiEw8uh1zpYGHM6Us2IsCMw9w79V6BvntlSeZUlZ0J2WWwr4ZPC1xSzz5B/mQXNFRqnG4FbhWH0kA6uOpOOp6+VSd2autZR/WJtGdTIiUT34DQS2z4d65k7eK+DNTz/D/b470DJnDbQwY4clESXXvYteTSuXDNeUos2twJFFGCZuw4+7z8lBd8o2iBYjL/WOiqt6r0RkVSOZhxEtZPPMYmieNdMtBSPGRdAdu2QM9ml2Y5z+FRTpB8E7vBV+UtyGHZYcfF5FptsWdLcQzuLIWRYUlVVWrA8fj4cMrfiBmTDfS6vw8IloBgh6jFasw+trdqPjhLKAzrJzFh97yKYwtOw2RF6zWhOCCCmmF3BqBeILdyHl4l3yMoj0A1Ca2ZSRbMRHVlFube+8LY+7amPPdAc6P2BiHRsWxLK6hQY0uZiEaEsq77nj9MCU1g/hQSxgy0RqrjUgLcxEqSoQ+QYJoUGerWVvvfctHND8hpeKJiK78Lpyo8yOZBTyKQjsOrcZaWd6P4mX/zmGPaXB/CCCfdlFfhrab3oMK9QKPBuyzP3/IQgwDnob3+1eB22+JFdslObyfhSMaG0mV5EtuGVBt9R6AL4oLYFJkhBY4YCvrbqxsMQI0SAHuRa1X6VJLR+ntsRxqTEPziOsGfF8yRsxFYNoW6k4WKm40Umm28l7pPVneFBf7FCxYP25Qsmr8n6oSosL8bdg1eZd6GxtngbrjG5GUDu8VhW2TLdJbqRmscBiMvARYfz1wWbOn1qL8Dx2UGgYGkzQzWZyE1LfiReO48/SB2FRSbh7ZSju9P0GGmvX2RHtPVtnGenvBY1S5EftUnJKEJt+kAdkh6UmuKk6M5ATB8PS6hYoDy/EHOFVYNar+Ms4ChbpNozpGoMR7Wpx3WcDwBrW/PNEbxhmf4SNpgE4nPgo3hs2qPqVIdZmaE2ETJxNv8BLWEVTCV9jj6C4KkehtYjw4x+mrESPzU5mWbGVmf5YaB6IJ2pxx5hhB4lYgzi5SZzD0fyrmSBApa670mZFXF8gdQf6iAfw75EMPNBzCF4Onobk8xkY5m4dqsNzzw64rLZWKtjKHt2VctoFxcES1gaqzIPoVrQWW073QW/zToj5KciVvHEq7Aa0q07GpibUOoQOnMS/GDaUR24FdgV0GAfD8X+hPvIX3jZ8BCz8CIPN7bEQT+HDUdVfTtDc2nmelXgj+wQESLgo+SAoLKrK92w2koZ3pdccs68TbVqNHiLXtQxDmJgHKS0ZR863R8tQDYoWTkbSAdaHIA7je9bRWFOFEomRQZjzQA88Nmc3uuZuRVuTPEVmpaUr0gZOw4MDa3kyQbPBfAZ8E1MmMvavROnwVlBnnYAFIjZZ2mCwJz0JWNUIb6aWyR/3TrbycikIPV2M/lIaC+AvFPMO5izb3TTUx9513uW4MBu/KJgCm2JldjDOZ2XzJpi2YGHNUbkcuE9CLbz/xg9AaUhbeGcfQNuTX2P32U72Ph1bTQnwtcTid2E4pvS6tCo1RXw/HnSzqpmNJ7PlzH3aHv69/ZZ4tLYuj6uqmR1bCnee9UAolHvHZEqBzruAs0w3O+4IOdMtFWXx/ZqL8HNZ1RBtXbN9zjYZIDQRH7Vfijkbj+Duqg6SWKlVaqgFPdqJSXzqw3Utyg4mHLL23mkb5Vftg4WaXo9g06a1yLxQzP/fAbYGndblgMlSBBIiqq56sP3trCkaC6IDizL4ZfZ+HujnZH9v+/f4OfstLFZ2QX5JO74UgPUAYAIce6uk7cGtxn+gE/1RXJQIwWIsl9m2a38nFq8Mw5kS6+xtazDMMt2Vyr7tc7pZUzRrBtpe3u4i0+0wW9xQUlZCX1Wwzhr9MfYpLcayoFuh1rkuL0/bjee3D8IYdSh+FRfzJrOKZc8hzpcdJHsJ9UH9zN8TUhOhzSEkXMe74M6TpkCTn8TXOJ2OusXjD2TxxHL8rJuG8YqV2HM2B6m3LcQd+tewX2jJdxKqQ7xpOkrjh9gvNxEzMLF7I7x1c5vLl5GqR5qE+KDZc6vR54VFeOi2oTVbiuETBotPBG9oGFl0EDlJcsfe01IkEsKrXourOLMB33t/hYcUi7EzOYd/0Pxr3enrW8NyQ1INzW/kJ0PEnVi68xgyii34PS0Mmyxtq36Nh7WyN8LjDXiMpfZmeqwM0hNip/H8dJxiDeZsPY1C31isRVfMNQ/E2F5VlDPWN2xG6+3foqjDRBig4mva85TBeHdUpxqNuRx44n38q34WUtoBGLNO8utOSNHug+f0A5iSfD/+Vr+CE5kFgG3ShIUF3Z6/X4ekrcNm9RP4WPU1flh/HNj6NbwP/oZvlJ+gd1xA+TXEdYA1zVr2VF807TYU/zW6H3+1+w6JTy7ChIHyeKRapfaG2GEcP3uHcTHm7UjBluBb0bN0Bn5QjEa/RA8+K5v0xKbAkbypFAvK9OEdscXcii/RiXf1OWntFs1mdbMGYKVHlsMr/xSvFnHbZZoRBCif2Impfi8hS/LHFmufFvOZbVh/WF4bPrhVLXSWFwRoh8nTRcYrVuOvubN5VpE18Xxysw4jDe8gtPc9NR/bZmWJlUfldhePYNtRuZrCfHqDPejuXVVXdNbvxDpmjwfFXSbiRa9X8Ye5v/NsvzXoDhHy+AgwC888AtlCkPNMt9mEPodexxzVO7hwsaw0nB1gKYHW48albLwZ01Y8jb0pDg24ji1H2y1PY4S4pcavLVvpP6s6s7MuBzwuRaGtB/+v1lSAXrpzaCKkyzPPC87bKwacHrwQRPha8vnjyBqplRxdi7bCafiqzOUPMp5YhXtzv+R/X75JxPYhC3Gn/lUotJVfG7b9GLkMvaxcvNL+jbNMty0zDu9KmXZOpcXx0evRofRbpJeW/X/mEoff4yRYj8jciJvFjVDorY+tUa52KJVU0Kgdbq9Ql2W6HbqXGyt0L2eZ7/ri8rcUJuQqJo6YhtLZt0Cbcxx6SYn3FY/ilVHdPd8ZvpiE7oatKBT1WJ2cA5MlADukFmjfyL/KLGglXgHQjp8HS85ZpOSb0Dc4GrdVt4yK1H5fhdg+wME/+Q5P9qFzYB/dey1NeZOuKuWdQ8/idQhQxGDOwR3IP/g0+hnaYodvf3S5yqcN1AvRXWEObg6vC8fQNXsRJs8LBEs0sMeeNdFyK6wlUvt+iMdXl+B0egGkn0fiq/PH8IT4GBJC+3j2+9uNhvnft9DSeBYZhzag05FWMJgnIyFYi+Wd6660/KqlUMH7lk+BYe+g1GjETV5+NS679SlKhq94Hi3MJ3EkaAy+b/wX9p08iwfcdSDWBiC48AT8BAVOpl0ENGXjHbtU5yBpdFcIKi1aGlPwwKEJMB05z3euPjPdjinDLs8iMLbT3n3kw5fld4k9HoW08wcMUuzBR8s+wOtGNqc6EIPbx1RqHuVU61ux43xrbDh/AmFZRTja8wGMXdGSByouR2D6yRl0lunOzMmHZulY/KuxYLDwjWdd2UUR17UIw6xNyVh1OAOD47UQfhqOlWYFRmg/v+Qmanbx/VHa9m5oD/yKN4rexj8fbsKbeAg5xUa0iPDHowPLlh/UWGgL6P3ioM1PgnhyBQoKO8DrlFzhcFDXDU9XdRAiMM4+aeV0ZiHM/i3wV0FrGCSL86oB+wGPi0jJz4OiVA6kzb4uRlwqlAhOWYlQRT6EvHPyUipR4NV/TOOqxoVVCLrbCMn45my2tTYHkE6sRIf8tegsqhEfW7PnbVDAeWgUa3A0WWf/f00pO/jrdr+lKSa6aqrpaNM0zLF8htmKwUjPH4FWTXvi3bifsPloKm52WjEgH1hna+P3FhkQuu4uLNYYcIv62/K3s3Yv9xVKcEZvwfmgRGyTitFLW2E7L8hAJ/EECgWznL22B9FOMtDlOpFbg+4Ww/DZzhJsTJUw1sWBIK+IZsjFOWgdmqJZinN5v4x8wRs+jjPdrZpsfRXT1Sm419C4XKa7BJryBxfsQbdR7l5uDbrNUMjLOe373RWmQlzDKNNNiCP/KGgfWYviUb9gz6j1+ODF59HM1RghZ2K685PO4nHsTMrGf8fl8RE1npfOgrygJmgS29T5kVNy+bGgm2UZhKNQpsnNg1hZpUczVxOHwqLQoKV4FuPPvY6QtLW4RbGJz2auiwZapAJBgKL7g/zsi6rfMe7sa4gX0jDKk4BX44Pgfg9gH5qhoEQPS/phhCAXuYIfmoZVsZNrowuCYsSnMIha7JIS+To2L5UC79/R8dqaOFHb1Dpovf1rvs6VPbWN5P4MrEM1qyJZd9bMR7m1d1dq6x/NZ92y6iYh5zTM6Yf41SelSDSvzvs+e15vnsHPthBToIQJS83dENZ3ItrXdjf6q0FIAsy95VnhLZHET1nG89nBnk+CaGVdbnUoLQ+ns+WKEbcjF60l6SxQZJ3qy9bee772/4aW4egkHMeRIwdg3jmbr0NPkULRtXWLGo3vckV786fIi7uRb1e3WFahdeluxId445u7O9fO2n5WJdLuNpghooklBbvWL4HSVIRMKQAhiR4kCQJj7QFYdvZ5pOWW8EyjWiE67wRvLUePFLJRmC1n1gslLXz8XX/mCQFywBWBTD66UvrrQbyc8wrP7Dod4edMcDOYtEHQCXqYU3bYRzWajq/mp1vRBl1ja3aw+obDL+ED1Uxozm22j0GzpMif58nalp6t77c+LlHCBTnTrdLikCECh6RY5z9vnV/OyvSLCvN4QztOV+FxtI4yZHPrWYM09sUvV+xNsfsnvJn1NB5ULOGBtKHbJHxoHM17lFQuLy9bn51vmyMfEIM1Ulfskpoj0MXoUFvGnJXQ28YqlgYk4JClCYpUwc73W6wdzAXWKJb/0kZYkDgVLxgfgFbl8DrzjcAfQ3ciQf+LnOk2y/fLBBEqhUOmu+IoxmsYZboJqUjjA13bkehRk0cmoh0klQ4BxiL8X/7d+COnP7ZjOPrXdudacuXE9uUnncVjuCH3I4QgD5naOM920nVBENveAez9FYmivI7xN2koXul5FXSibyi6TITx9Aaoji7iTdH6t4nDHR5mmdkOMytl9c/eA4WxgM/b9YlqDZ2To/0utR8DRWBTTM2IwKnsQtzULrLOy48bhCa9gM2f43rFLgxefRQFegvfkXT72LJRcGyE0LkdaIYU7PEbBH2GAud0raueI1xRm9tgVvngxPp52G9qDGW3+/Bsp5qNhboWKK97GSVQoeA0cIt/JB7sF1+tOeBtw1U8ACvO1OHEWfmxdrsEK1wulW8pnMX6NPngyAkpCgnVODjSPWUm5mvex05TIorWZYGFNj+Yh+G+2p4EotTA/57fUXxqE/YfOYYJEV3Rq32r6r1PVEHo8Qh+MQ/BZ2uzMGfLu2Cpx9XmjrjFk21OrYM+uhc2nClB0cVMmP59F8NEESeDrnN+4Cu2N1b1/h0v/JuL2y/Y5qMHI8JN13jBPwbIOMSb5bES9vBTa9EHWfhEHOV5ebkoQmw6gE996GTagz0puejqmwNV/hneR6UksneNH1Nl0/7A7tPoYNonj/rzKYK6OB1mSYAQJWfYq2Qt048V0rGPNWJkj4t1pF2Yn+tMNysv1+fJy8qKJQ103r5OM90+QgnU+afRLGsThosKaLSjnN7OTyhBcokRuW1uxldmH7CnsFIG2ro+m2Wb9aVlI7hyiuXA39WSB99Dv+Bt1T/4w9SfZ9NDfRU42edT3Lp/s8teCoI16GaN/XiVg9YP+337YoUlGY86HnRiyz40XpAgWhupmet9ppuCbkJqk0IFod2dwK5ZCBQKeffxuZrb3WdbyLUluCnMzYZgdnIozuuDcQYRGNcuxvNMdf8p0CdvhSb3JGabBqP34Ns9a8RFaocoQjV6Nszn90OnisVrof6eLx/RF+Kp4G0YWfAev7jB0g69mlXRKdgJRUxXjK6/8diV0XQQzJoAhOtzsQ9jMEXxIIoTxlWZPbcF3axM8/FzDyHd2BvDWzaq0fp6RfPBaMG+0AAolPC64SWMBfhXdUUsnYjFmrU4J4Ugek82gpU3Qgh9v8qgm1Wm7MyWZzQft0RXq1eKovkQ4L/30UU8zvbs+ai8nKY3183IS0GALqEPeiR4uPSkunzCcGvfQHy/ZwM+zhuN14RfsKnRvRjnScUVW657/1I8/vpyNJVOIe7QF3hb5YuXwm52fmOvQHjHd8OFf7chqyAb53XNcbQgEI1cdJrnguQSdnZgMyPtDBTFWXw0oykosVrZfjFhEA+6+4v7sOJoJrr6/8uv32Vpjs6JNX8TVbBgfvcs9BYPYuXRTLQO28+vPybFoEszD5f6BDXlJzFCBp+Rbt7xI27O3YR/hG6IDb7OZdDtI5RCKJAPul+Eb+X11NZRXYEoRGDeMXQ//yUkZUss145xmxHnzdRYoKxVVd4fUXvj+ENJGPz5FgTaqjqOLEbP4u1YB9eZbvH4Mt6fgPUKYGPDWMWlLfPubD03/xmN/JrUoRQlRjPP0LNMOaOtsPxErZAv85Fh1vJylumWK7/kv4E1xawvalRP88svv6B3796IjIzEmTPyWI5p06Zh0aJFtX3/CLn2DHgRFrV8BPIdy714a0y/hl06Wh9LlMfNQ8e73uYjL9gouJeHVaNDcGATaJ7cAcsTezF8yi88Q0QuM1GEIqoDmoQFVC+4MhThpuSywGCdpT36NqvdrvOkhpRqKNrcYr/Idvbts9DdaS6PorldsR7Z+XKZc7carhMlnhPC5bXuLBPKsKalbkvxfSNgCm+P1ZbOaKY/bG+UV60DlpEdYR78PopFb5yRwvCG/9v4ZEzXa/ZpYzOfWcl6aUQXfJPwHV4cV9Z4tSosKIsL8UGicM7+WLLLrtjWYS8paIa3o77GE8YnEeFuHnuEPJatlXgGBUm77FMB4qoaZ1ZR00HI9W+Bn02DsXx/KozbZvKr/7F0xy0d5bXmNRLXDxIEXnF25OBuFMcNxuOmZ/CN6aaybuZV8Y+GWVRDI5iQl54E07aZeFr5J5opM50/NhpfvryMCSw6zU9zJB/+PDoLzoOEfAjW2eYFks7eFdzONqdbKEZRcTFvptdKSIaf1slBDUFAgK/8HLLgmWWgLes+wEfC53y5m9NGavx3yK9JPxTZ14LbTv0qzui2EjVypttbKEUx62Ceexatslegq3C0fHk5a2i36wV8oZoOlSEPsHZpZ5lutUN5eX3KdFc7Evj666/xzDPPYNiwYcjNzYXZLJcDBAQE8MCbkAbPNxziI+uRPm4NHnnuPfShnfL6RxDQNTYIG/83EAsm9XI7B9hl2VxwHELd7bSQq49vOB/nx7AsmbbNCPtIIHIVGPAi8treh3/DJqDVHW/ghlYe7OAnXM+XBLH1rY0FuQcHa7hF6lib28pd3Knrg07uxvaxUtRH1+ND3bN83jZzWEjg78PVoeg1CbqXzyD8xQP49uk7L7mT+JXWNtofS5/qi2/u6YJoTxuUWSWGeWOIQl7HfMLCOse7XlMfeWYRXlP9gqaWJOyzdhJ3m+lu1J6ftBaSIZ6Xx5kdlGJ5p/1q8WsExaMbsUY7CB1yV0GVexr5khfORI+suvmlO7og6OPkUZodMxfg07UpWGLqgt3+g9z3FnAkKmAOkJcmqHJOQJkjT00oCWjmvPKNzfaO7okN5jbw0ssHm3Iklul2FXQXQizN4efz4VU5MLaVoaMEQmEGmi8fiwXq1+Hn5XybDrL+PGsemlvMRr/JXfxz4Osya21bC+4vFPFgHTlncP3inlim/l/lGd22P1NdlukuMpiBs9swPu1tPKX8C17q8gcEIlKWYoRiG+9JAJ9w7NH1wg5LCzlR1bg7THf8giON7kCDDbpnzJiB77//Hi+//DIU1rIApkuXLjhwQC75IaTBC4pHRGJnNOIzYEl9FeanrZ3GOOSaIdw+G4UPbgGe3IN3xvajBnhXE98I+I+ahkGTpqNve7kjsUdLgvo9D6OgRhNVLr6/p4vna05JzUV1hiVGnhK/xNwdN/bt5VHVyfjAg7w8N9kSDr9mvat/wJNRqKDVaht2BZq+AB8l3Yqhih38Imu+1S7adZm94tCfuF+xjM8GT82Vu1HHuQtOQxJhUWpxUfJF88Jt/KqDllheGVZdLLh7tH9TrLe0Q5IlHD+YR2DSYDmovxTaXo/w037ifizauJufH9O1cbWqn1Shcjf6XtJeKMx6PhZLG+q6ek0YvwDjjS8h1+Llprw8EMnXf4tR+tchOszSZvsbzsrLWabbYhvjxWZ0uwiGlZs/w1zt+7hO3M1Hvwklchd6kybI9TIca6m7P8t081ngudAacxEkFDid0c1Z13R7Qy/P6raODCth3csrlJdbrB3MYTYAjbtheuib+MA0Vl7T7dcIUuKNuOjj4Xv5NaDa71ZJSUno2LFykwGNRoOiorLF+YQQQki9o2bN01qBVuHXI32fgarXk5hpX0tILgfx9h9RvG0W4DcS93XxrJnZgNsexlPfSig1FOPmjg1wzF5t0fhCpVIB1gbaZ0L6oUWEX5Udz19V/YpbFBvxhPgKEsPcNLFTKIFnT2DM1NVYLk3iVx0WmuHJGva3mdgnjq8LfvXwNIzs6eG0kKo0HYSipiMQd2oZuonHcCjwOjzQt3rLvYT2Y/BDSjhy8/Ltzf2ahLp+HNnoWNZwcFtxS0wT78EBQyhurBh0s6ZpbW/GriVrcJdxDW+SlyUFoIWPxuXsbaHEmhFnHf1dZa0zDqMHDmC10A45ubkQzXrn3dNdjRpjQbfjjG4XwT06jMN7BwOwTh+BPizTbR0ZVgo1dBVK5CXRIehm08WsHep59/J6qNpBd1xcHPbu3YsmTZqUu37ZsmVo1apVbd43QgghhJC6p1BSZ9nLza8RdDe8hBHV+JHYMH+89NRT2JuSi8GeLB8gLgk3vIXcFe/hdfMDeHmsvGzGpcQbgR0z7fO6E2KbVFnlI3r5oU9jDbYlt0K0kImI1v0qr1/2EDsY9tT1zQD2VVtEEd53/4q8/UtwT4k/4lp1q37lWquROHQgBo32f2mvGGDj4dwJ89XgYFEcDhbLAf5DTkaoBXvLAXYo5GA6XQqsPDbWJxxpbSfhm91FEPVlme5AbxePsbfcf4RlqQtzM/h5npnXuTmErC3LdKdaM9223+OyJL1xN2z20eN4Xj6KDCbAZA26JTVCKlamKKz/h8kadJvk9ds8052XCuHUOoTlnQIg995ocEH3888/j8ceewylpaV8tt327dvx+++/4/3338fMmfILkhBCCCGEkNoW7qfFkNYR9MBeqg7jENBhHKZ7ctv4Afazp6VItIryLGP99KjrcPvXWuTk5+G33ldh01BBgH/7m9D9Ev6L65t6o9mhTfz8MUtjPOBuROzmGfgjfyr+VPbCm6Z7+VXNnIy9U6dsxKNeq9FXOsgvpyOo8hgyjQ9K+r2Mn3f8hwml63izb9aYjb0+nNJZg27kozg3q6y83Rrguy0vF1gjNRNgLXdnmW6XGXX2q6wjy4r1ZZnuEmgqzRqXrI3leKZ712zMSX8GK1SdoRR/ATIOQvn3JLTQse3mRTTIoPu+++6DyWTClClTUFxcjHHjxiEqKgrTp0/HmDEV2tkTQgghhBBCrl2sXHzoBzCsfheL/B/F5B7lq11diQzw4o3e0nJLq99E7RoxuKkX/s/SHF6CHslRIyuvvXbkHQZfSwHuU67Ab+ZByPGKdT7ffueP+J+0AD+ZbsAKS1ccF5vC10n/AluX9CBzJo/o+Px0V7/fO9ie6S7Il4PuXMkXAe6qD+IH4OcuC/DxxizcWFxWXp4Pb4T4uGhCmJ+GXqbtkAQTigztHNZ0q6Gr0EiNTZ1gRBZ0m9VQwswHhamUbE63vNRHkOSS8wY7p/vBBx/kX9nZ2bBYLAgLkzt9pqam8gCcEEIIIYQQUk/0eBTqHo/ivWr+GOsQf613iXdHWXoR18ep8Unpm3hxdH/3N47pYT+7WjMFY8OWOL+dtYN5AXTYbGmDaH8vpw3evPVZ6Kc9iU7mE/wyC7q7uuoqrysLunciDr/GfYDVxy6ijbuGv6xDenA88lEidy+3lZdL3kisuMbcJmk9ns56DZ2UbXFaf5vDmm5NpUw3rI3UJIsBsMjTsMwQoRJZ0G37exvwyDBHISEhPOBOT0/HE088gYSEhNq7Z4QQQgghhBBytWrUDmET52HqY3cjror13AiIKXdREpVug+5g5NvXgTu18mX8jNdghBIfGUdjs6W160y3Q3n5Wb031khdsM7SAVFO1pQ7spWR8/nc3qE4hlikSGEIdpXptnYv9xFK+MgwS7txeMbwCFaau1SaNpB350K0Lv0BG0ytIFnndJugsDZSk4NuoSEG3Wwm91133YXQ0FBERkbi888/51nu1157DfHx8di6dSt+/PHHur23hBBCCCGEEHKtYdlbP7kieKulJYa1beS26dlY5VoMFbdXbqJm4yP3NjgmNcaX5luwV0pwU14u/58GqHCxyIA06+g3tgTAnXanZ+J95fcw5afD1PVhDNW/hx/NNyLEVabb2vHcFyUoNphQHNYe8y39cEiKhbd1rbeN2jsIRfCCWRJhMZv4dSZJIU+RsJaXQ5IaXnn5Sy+9hPXr1+Pee+/F8uXLMXnyZH7KGqqxzuX9+1dRUkEIIYQQQgghDdU9f0O/7mPkRD6Icd3KZ77tGpe1dntbNQtf+o92fjtfOegOF3LsI8lcrtEOTsD2sQcxetZ+PJizArl6CzLRElEBbtagA4g+PQ9jlan4p2AQLhYbeAzMjh1Umi9eacxYEYr0ZnlWN8vyCoBWVT7Xy+6vjdlkZNPReHm5mgXdZrHhZrr/+ecfzJo1Cx9//DH+/vtv3rk8MTERa9asoYCbEEIIIYQQQtwJSYDm9m9wY6/OckbXmfDWKO3+JD97JGw47u8d5zbovlWxCU2FVJ7ldrb2mxMViImQy9bHl/yKjzAdccJ5NHK3ppsFvX7y7/A2XEDKRTk7HqRTQ+FqZJzD/HAWcJuPr8J14m5Eakoq3Tf17pn4UPktugtHYDbo7Zl4JSsvt2W6G2LQnZaWZp/DzcrJtVotHnjggbq8b4QQQgghhBDSoGiHvgU8vAH9HvoMMcE6t0E3M1/9uuvScqtwPw1CvAREChf45XxtZKV11hUprEF3I+EC2vzSFqvVzyHG2+TmjstBt5dggL60GMHrX8WP6o/RVpVW+f9O+g+jlf+hqZgGg19jbEFbnJQi5Tndoc1huuVbHIoc0/DKy9n6bZWqrGRBoVDA27uKhgGEEEIIIYQQQjzHssKN2rm/TWRH6P1ioclPxgEpDrd1cj9BSji8CMvE56GULCiVVFAHuFhT7vgz1nXjrYQz0JgK0EQohs5HDqyd0pSNhjOX5EEwyM3gTKrK88htI8PUMCK/+Wg8sCwCRWYzJrJMt08YpNajkHlmKRpc0M3KySdMmACNRl44z9ZyP/LII5UC7/nz59f+vSSEEEIIIYQQItP4QvPULpzdsQQtm3REn0Yu1ojblOYilM30BnBOCkV8uAez063Z9LZiEj/Ngj+Cfd2UpIsKHO38Jr7dko5MvRIKY6H1vvpVvq1CYw+6DWYLjGa5lJxnuushj4Nu1kDN0d13310X94cQQgghhBBCSFUUSsT0uMWzxylMXibM/GYehEf6x1f9Mz7h/KSleJafZkqBrseFWRW1uwcLNm1BfLEFCrO8VhtaP5eZbhXMMJgsMFos8tUs0118EcKp/xCWdwDAMDSooJs1USOEEEIIIYQQco2J7oqigW/jk10WhHe+Ca0j3ZSJ2wQ0LncxUwpA83AnpeIO/L3kYNpUkld2pcZZpttaXi4YEbr2eexT/42PTKOhVtwAXDwN5V8T0E7Nmr+9iAYVdBNCCCGEEEIIuQYJArz7P4nXqjPluXF3LOs1F8YN0zFSsQWnpUj0bxzg9kdCCo9hkLgLJQYNqx1HoaSFt1btprzcBKlUDz+hGCIka3m50HBHhhFCCCGEEEIIaSDU3mjdoScGiHv5xZXmzkisItPtt+VD/KD+BMPEbfxyAXTOu6TbG6mZYDHJZehGKK0jw6zjxdhg8HqCMt2EEEIIIYQQQiqJ8bHgN3MvtBdPYY+U4HpGt5XoJWfCC6HDDyFTcDCtAGHOgu4+k3H3oU7Ydd6I4SZ5GbMBSqhE0T6nmzLdhBBCCCGEEELqN10Qmtz7He6QpuKDUe2rvr21aZoRCvxh6osFlr7QqZ0E3Vp/FKuCUQItJGum2ySoIIoOme56VF5OmW5CCCGEEEIIIU71aRaCw28NgWAPht3Qyg3a/FCE09lF/Ly/l/OQU620rnQ2G+QTwbr225bppvJyQgghhBBCCCENgUcBt0PQ3UE8hd6mXTgrhKFRQOfKtzu7FRPzv0acIsQedFtElfWX2dqOUaabEEIIIYQQQggpowvmJ+3F05il/ghzTAMR6X9n5Uco8whuKFwESeyMdE08zucbUKi0jjHzbQTTsM9w8NAxeFDQfk2g8nJCCCGEEEIIIZcuKL7cRda9vFGA1kkUWjYybG6j/2FeagrCNBr7OnKp43iknl9ab4JuGhlGCCGEEEIIIeTShbbAf02esF88K0Qh2NvZnG7byDAjigwmfl6e0V0/XRN/WXJyMiZOnIi4uDh4eXmhadOmeP3112EwyPX/hBBCCCGEEEKuMK8AXGj3MC5KPvxilnei8/XgtqBbMKHYYObnVWxGN2MognBqDULzD6K+uCbKy48ePQqLxYJvv/0WCQkJOHjwIB588EEUFRXh448/vtJ3jxBCCCGEEEIIgH4RBgQJhTBJItLUcc4fE2t5uQomvJ0yAe9qivEi3pO/V5AO5dzR6Cp6AZhSLx7TayLoHjp0KP+yiY+Px7Fjx/D1119T0E0IIYQQQgghV4mQc//yU6VgwZFsF5XJCpW9vDzQcgE6oQSirbzcNjIMUr3pX35NlJc7k5eXh6CgoCt9NwghhBBCCCGE2GQe4ieHLU3wyvCWzh8XJctiA1oYoJSM/LxgLTmHvRzdUm8e02si013RqVOnMGPGDHzyySdub6fX6/mXTX5+Pj81Go38i9RftueXnmfSEND2Thoa2uZJQ0PbPLmmDHgVgn8sTEGDMCYh0vn+eGgrzG7/f/huWxZ2aB/jV1kUavm2Zgt4Hly6+vflPb1/giRJVyxr/8Ybb+DNN990e5sdO3agS5cu9stpaWno378//5o5c2aN/v85c+ZAp9Ndwj0nhBBCCCGEEFJTK88JWJViwXHtvfzyzarvcH8bLbSGCxhyaDLMggpLOvxwVT/AxcXFGDduHK/C9vPzuzqD7uzsbP7lTmxsLLRarT3gHjhwILp3747Zs2dDFMVqZ7obN27Mf6e7B4Vc+9hRp1WrVuGGG26ASiWvGSGkvqLtnTQ0tM2Thoa2eVIfzdyYjC9W7MMh7UR+eULk3/j+vl5A/nmoZrSFRVCg9PlzV/W+PIsvQ0JCqgy6r2h5ObuD7MsTqampPODu3LkzZs2aVWXAzWg0Gv5VEXviruYnj9Qeeq5JQ0LbO2loaJsnDQ1t86TeMOnR6+zXCFcds1+lUGnlGE1tXdstSVf9Nu/pfbsm1nSzDPeAAQMQExPDu5VnZWXZvxcREXFF7xshhBBCCCGEkGoQRLRP+gHtFUCyJRxF0EKhUMjf0/jCPPg9HDp8BC7asF1zromge+XKlTh58iT/io6OLve9K1gdTwghhBBCCCGkuhQqWAQlRMmEMYZXkI5gDFdaK5nV3rB0fQhJWUvrTdB9TYwMmzBhAg+unX0RQgghhBBCCLm2mJRyY2udIPfgUom2UWH1zzURdBNCCCGEEEIIqT8s1lndXrAG3QpraGo2QTi7BcGFR/m67vqAgm5CCCGEEEIIIZeVpJIz3f9oXsYc1TvQqKyhqaEQyl9uQp8T7wEWU714VijoJoQQQgghhBByeankoJuJEC7C38vaCVxwKDOXLPXiWaGgmxBCCCGEEELIZSWqy4JuA1QOQbdjiErl5YQQQgghhBBCSLUZb/oCbxvvsgbdSgR4qSsH3ZTpJoQQQgghhBBCqk8X0RznECYH4FDCz5bpBpWXE0IIIYQQQgghl0QUBfip5PJxg+SivJy6lxNCCCGEEEIIITVwcjWeEX4rKy/XUdBNCCGEEEIIIYTUjlNr0UjK4mdTpZCyTLeogHnAyzjc6A5AaV3nfY2j7uWEEEIIIYQQQq7IyLCfTTfgJdMD5YJuS+/JOBFxE6DU1otnhYJuQgghhBBCCCGXl1oOunWCXj5VK+rtM0BBNyGEEEIIIYSQy0vlzU+8UMpPBcGha3n6fvgXJwFmY714VijoJoQQQgghhBByeRVf4CfDFdtZm/Jy31L+eD0GHHsdKLlYL54VCroJIYQQQgghhFxeXgEOFxyy3I6XJQvqA+WVvgOEEEIIIYQQQhqYTvdg2Yat+CmnTeXvsVndkpnmdBNCCCGEEEIIITWi9kaze7/EhZBumHZnh8pBN1e+7PxaRZluQgghhBBCCCGXXUKYD1Y907/yN2xBdz0pL6c13YQQQgghhBBCrh4CBd2EEEIIIYQQQkgdBd2CfEqZbkIIIYQQQgghpHZZejyOYxE3Axr/evHQUnk5IYQQQgghhJCrhqXvczjaaBSgC0J9QEE3IYQQQgghhBBSRyjoJoQQQgghhBBy9bh4Gj6lqYBJj/qAgm5CCCGEEEIIIVcN5U/DMOjIizz4rg8o6CaEEEIIIYQQcvUQaGQYIYQQQgghhBBStyPDINWLR5gy3YQQQgghhBBCriKCfEJzugkhhBBCCCGEkNqOuUUKugkhhBBCCCGEkLoMugXKdBNCCCGEEEIIIbXL0mMSjjS6HZJvo3rx0NKabkIIIYQQQgghVw1L14dwPGIkQEE3IYQQQgghhBBC3KFMNyGEEEIIIYQQUkco6CaEEEIIIYQQQuoIBd2EEEIIIYQQQkgdoaCbEEIIIYQQQgipIxR0E0IIIYQQQgghdYSCbkIIIYQQQgghpI4o0YBIksRP8/Pzr/RdIXXMaDSiuLiYP9cqlYoeb1Kv0fZOGhra5klDQ9s8aWiM18i+vC2utMWZrjSooLugoICfNm7c+ErfFUIIIYQQQggh9STO9Pf3d/l9QaoqLK9HLBYL0tLS4OvrC0EQrvTdIXV81IkdXElJSYGfnx891qReo+2dNDS0zZOGhrZ50tDkXyP78iyUZgF3ZGQkRNH1yu0GlelmD0R0dPSVvhvkMmIv0qv5hUpIbaLtnTQ0tM2Thoa2edLQ+F0D+/LuMtw21EiNEEIIIYQQQgipIxR0E0IIIYQQQgghdYSCblIvaTQavP766/yUkPqOtnfS0NA2Txoa2uZJQ6OpZ/vyDaqRGiGEEEIIIYQQcjlRppsQQgghhBBCCKkjFHQTQgghhBBCCCF1hIJuQgghhBBCCCGkjlDQTeqFnJwcjB8/ns/JY1/sfG5urtufKSwsxOOPP85nt3t5eaFly5b4+uuvL9t9JuRyb/PMkSNHMHLkSP4zvr6+6NGjB86ePUtPBqm327zNww8/DEEQMG3atDq9n4RcqW3eaDTif//7H9q2bQtvb29ERkbinnvuQVpaGj0p5Kr01VdfIS4uDlqtFp07d8aGDRvc3v6///7jt2O3j4+PxzfffINrBQXdpF4YN24c9u7di+XLl/Mvdp59OLkzefJkfttff/2VByLs8hNPPIFFixZdtvtNyOXc5k+dOoU+ffqgRYsWWLduHfbt24dXX32Vf3gRUh+3eZuFCxdi27ZtPAghpL5u88XFxdi9ezd/X2en8+fPx/Hjx/mBVkKuNvPmzcPTTz+Nl19+GXv27EHfvn1x4403ukwEJCUlYdiwYfx27PYvvfQSnnzySfz111+4JrDu5YRcyw4fPsw68Etbt261X7dlyxZ+3dGjR13+XOvWraW33nqr3HWdOnWSXnnllTq9v4RcqW3+zjvvlO6++256AkiD2eaZc+fOSVFRUdLBgwelJk2aSJ999tlluMeEXJpL2eYdbd++nf/MmTNn6CkhV5Vu3bpJjzzySLnrWrRoIb3wwgtObz9lyhT+fUcPP/yw1KNHD+laQJlucs3bsmULL7vq3r27/TpWMsuu27x5s8ufYxm/v//+G6mpqezgE9auXcuPCA8ZMuQy3XNCLt82b7FY8M8//yAxMZFv42FhYfznWQaQkPr6Ps+2e5YZfP7559G6devLdG8JuXLbfEV5eXl8WUVAQAA9LeSqYTAYsGvXLgwePLjc9eyyq+2bvSYq3p7tz+zcuZMvrbjaUdBNrnnp6ek8gKiIXce+58rnn3+OVq1a8TXdarUaQ4cO5WtLWDBOSH3b5jMzM3kfgw8++IBv6ytXrsStt96K2267ja+RIqQ+vs9PnToVSqWSlyAS0hC2eUelpaV44YUXeJm6n59fHdxLQmomOzsbZrMZ4eHh5a5nl11t3+x6Z7c3mUz8/7vaUdBNrlpvvPEGPzrr7osd3WLY+YpY9trZ9Y5B99atW3m2mx1t++STTzBp0iSsXr26Tv8uQq7ENs8yfszNN9/M+xd06NCB74yNGDHimmpEQuqXutzm2fv69OnTMXv2bLefBYTUp30bG5b5GzNmDH/vZwkFQq5GQoVtuart29ntnV1/NVJe6TtAiCusszj7wHAnNjYW+/fvR0ZGRqXvZWVlVToiZlNSUsIbMCxYsADDhw/n17Vr1443Kfn4449x/fXX0xND6tU2HxISwjN+rLrDEevav3Hjxku854Rcfds864LLKjxiYmLs17HMyrPPPss7mCcnJ9PTRurVNu8YcI8ePZo3nlqzZg1luclVJyQkBAqFolJWm71nu9q+IyIinN6e7dsEBwfjakdBN7mqX5Dsqyo9e/bka5a2b9+Obt268etYl1p2Xa9evVx+ILEvUSxf7MHeAGwZQULq0zbPllB07doVx44dK3c962PQpEmTWvoLCLl6tnm2lrviAVS2/o9df99999FTRerdNu8YcJ84cYL3qrkWghHS8KjVaj76a9WqVXypmw27zCryXL0mFi9eXO46tlSuS5cuUKlUuOpd6U5uhNSGoUOHSu3ateOdPdlX27ZtpREjRpS7TfPmzaX58+fbL/fv3593MF+7dq10+vRpadasWZJWq5W++uorelJIvdzm2XmVSiV999130okTJ6QZM2ZICoVC2rBhwxX4Cwip+22+IupeTurzNm80GqWRI0dK0dHR0t69e6Xz58/bv/R6/RX6Kwhxbu7cuXyf5IcffuDd+p9++mnJ29tbSk5O5t9nXczHjx9vvz3bV9fpdNLkyZP57dnPsZ//888/r4mHmIJuUi9cuHBBuuuuuyRfX1/+xc7n5OSUuw07xsQCaxv2ITRhwgQpMjKSB9vsg+uTTz6RLBbLFfgLCKn7bZ5hH1IJCQl8m2/fvr20cOFCeuhJvd7mHVHQTerzNp+UlMQvO/tiCQZCrjZffvklf19Wq9V8bO9///1n/969997LE2SO1q1bJ3Xs2JHfPjY2Vvr666+la4XA/rnS2XZCCCGEEEIIIaQ+ou7lhBBCCCGEEEJIHaGgmxBCCCGEEEIIqSMUdBNCCCGEEEIIIXWEgm5CCCGEEEIIIaSOUNBNCCGEEEIIIYTUEQq6CSGEEEIIIYSQOkJBNyGEEEIIIYQQUkco6CaEEEIIIYQQQuoIBd2EEEIIIYQQQkgdoaCbEEIIIYQQQgipIxR0E0IIIQQXLlxAWFgYkpOTPXo0br/9dnz66af0yBFCCCFVECRJkqq6ESGEEELqj6effpoH1wsXLrRf99xzzyEnJwc//PCDR//H/v37MXDgQCQlJcHPz68O7y0hhBBybaNMNyGEENLA7NixA926dbNfLikp4cH2Aw884PH/0a5dO8TGxuK3336ro3tJCCGE1A8UdBNCCCENhNFohFqtxubNm/Hyyy9DEAR0794dy5Ytg1KpRM+ePe23tVgseO+999CsWTNotVqEh4dj/Pjx5f6/kSNH4vfff78CfwkhhBBy7aCgmxBCCGkgFAoFNm7cyM/v3bsX58+fx4oVK7B+/Xp06dKl3G3ff/99zJkzB9999x2OHTuG+fPnY8CAAeVuw7Ll27dvh16vv6x/ByGEEHItUV7pO0AIIYSQy0MURaSlpSE4OBjt27e3X8/Wd0dGRpa7LQvGhw8fztdtM02aNEHv3r3L3SYqKooH3Onp6fz7hBBCCKmMMt2EEEJIA7Jnz55yAbdtTTcrIa9YOv7xxx9j8ODB+Oabb3Dx4sVK/5eXlxc/LS4uruN7TQghhFy7KOgmhBBCGhBWVl4x6A4JCeGdyx2xbuZHjhzB9ddfjxkzZiAhIYF3KndkC8RDQ0Mvwz0nhBBCrk0UdBNCCCENyIEDB3jncUcdO3bE4cOHK902MTERU6ZMwe7du3k2u+JtDh48iOjoaB60E0IIIcQ5WtNNCCGENCCsKzmbsc3Wdnt7e8Pf3x9DhgzBiy++yLPdgYGB+PDDD3m38q5du/LmazNnzuTX9+rVq9z/tWHDBl5+TgghhBDXKNNNCCGENCDvvPMO5s2bx5ugvfXWW/y6tm3b8u7l//d//8cvl5aW8nFhnTt3Rp8+fXDixAmsWbOGB9427DYLFizAgw8+eMX+FkIIIeRaIEiSJF3pO0EIIYSQK2vp0qV8HTcrGWddzqvy5ZdfYtGiRVi5cuVluX+EEELItYrKywkhhBCCYcOG8Yx2amoqGjduXOUjolKpeIM1QgghhLhHmW5CCCGEEEIIIaSO0JpuQgghhBBCCCGkjlDQTQghhBBCCCGE1BEKugkhhBBCCCGEkDpCQTchhBBCCCGEEFJHKOgmhBBCCCGEEELqCAXdhBBCCCGEEEJIHaGgmxBCCCGEEEIIqSMUdBNCCCGEEEIIIXWEgm5CCCGEEEIIIaSOUNBNCCGEEEIIIYSgbvw/GwXw+rmR1K8AAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "different epoch, -0.884765625 vs -0.8857421875", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 34\u001b[39m\n\u001b[32m 30\u001b[39m \n\u001b[32m 31\u001b[39m \u001b[38;5;66;03m### Plotting difference ##\u001b[39;00m\n\u001b[32m 32\u001b[39m \n\u001b[32m 33\u001b[39m eps = \u001b[32m1e-30\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m34\u001b[39m abs_diff = np.abs(inspiral_modes_py[plot_mode]-inspiral_modes_c[plot_mode])\n\u001b[32m 35\u001b[39m rel_diff = abs_diff / np.maximum(np.abs(inspiral_modes_c[plot_mode]), eps)\n\u001b[32m 36\u001b[39m \n\u001b[32m 37\u001b[39m plt.figure(figsize=(\u001b[32m10\u001b[39m, \u001b[32m4\u001b[39m))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:257\u001b[39m, in \u001b[36mArray._returntype..returntype\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mreturntype\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m ary = \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;66;03m# pylint:disable=not-callable\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ary \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:68\u001b[39m, in \u001b[36m_convert..convert\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 65\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mconvert\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m 67\u001b[39m _convert_to_scheme(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:274\u001b[39m, in \u001b[36mArray._checkother..checkother\u001b[39m\u001b[34m(self, *args)\u001b[39m\n\u001b[32m 272\u001b[39m nargs = ()\n\u001b[32m 273\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m other \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[32m--> \u001b[39m\u001b[32m274\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_typecheck\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mother\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 275\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(other) \u001b[38;5;129;01min\u001b[39;00m _ALLOWED_SCALARS:\n\u001b[32m 276\u001b[39m other = force_precision_to_match(other, \u001b[38;5;28mself\u001b[39m.precision)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/timeseries.py:118\u001b[39m, in \u001b[36mTimeSeries._typecheck\u001b[39m\u001b[34m(self, other)\u001b[39m\n\u001b[32m 115\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mdifferent delta_t, \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m 116\u001b[39m \u001b[38;5;28mself\u001b[39m.delta_t, other.delta_t))\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.epoch_close(other):\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mdifferent epoch, \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m 119\u001b[39m \u001b[38;5;28mself\u001b[39m.start_time, other.start_time))\n", + "\u001b[31mValueError\u001b[39m: different epoch, -0.884765625 vs -0.8857421875" + ] } ], "source": [ - "hp22_py_real_inspiral = inspiral_modes_py[(2, 2)].real()\n", - "hp22_c_real_inspiral = inspiral_modes_c[(2, 2)].real()\n", + "plot_mode = (3,3)\n", + "\n", + "hlm_py_real_inspiral = inspiral_modes_py[plot_mode].copy().real()\n", + "hlm_c_real_inspiral = inspiral_modes_c[plot_mode].copy().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", @@ -184,17 +244,12 @@ " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\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", - "hp22_py_real_inspiral.start_time=0\n", - "hp22_c_real_inspiral.start_time=0\n", - "\n", - "plt.plot(hp22_py_real_inspiral.sample_times,hp22_py_real_inspiral,label='Python')\n", - "plt.plot(hp22_c_real_inspiral.sample_times,hp22_c_real_inspiral,label='C',ls='--')\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(r\"Re [$h_{22}$]\")\n", + "plt.ylabel(f\"Re [h {plot_mode}]\")\n", "plt.legend()\n", "plt.grid()\n", "# plt.xlim(0.7,0.74)\n", @@ -202,134 +257,89 @@ "plt.tight_layout()\n", "plt.show()\n", "\n", - "# plt.figure(figsize=(10, 4))\n", - "# plt.plot(hp22_py_real_inspiral.sample_times,hp22_py_real_inspiral,label='Python')\n", - "# plt.plot(hp22_c_real_inspiral.sample_times,hp22_c_real_inspiral,label='C',ls='--')\n", - "\n", - "# plt.xlabel(r\"$t (s)$\")\n", - "# plt.ylabel(r\"Re [$h_{22}$]\")\n", - "# plt.legend()\n", - "# plt.grid()\n", - "# # plt.xlim(left=1.2, right=1.245)\n", - "# # plt.ylim(-1e-21,1e-21)\n", - "# plt.tight_layout()\n", - "# plt.show()\n", + "### Plotting difference ##\n", "\n", - "# plt.figure(figsize=(10, 4))\n", - "# plt.semilogy(hp22_py_real_inspiral.sample_times,np.abs(hp22_py_real_inspiral-hp22_c_real_inspiral)/np.abs(hp22_c_real_inspiral),label='Python')\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.xlabel(r\"$t (s)$\")\n", - "# plt.ylabel(r\"Re [$h_{22}$]\")\n", - "# plt.legend()\n", - "# plt.grid()\n", - "# # plt.xlim(8.05,9.25)\n", - "# # plt.ylim(-1e-21,1e-21)\n", - "# plt.tight_layout()\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "67b21752-4eac-4fc7-805e-032fd58be48b", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true, - "source_hidden": true - } - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "lengths do not match (4652 vs 4653)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 2\u001b[39m hp22_c_real_inspiral = inspiral_modes_c[(\u001b[32m2\u001b[39m, \u001b[32m2\u001b[39m)].real()\n\u001b[32m 3\u001b[39m hp22_py_real_inspiral.start_time=\u001b[32m0\u001b[39m\n\u001b[32m 4\u001b[39m hp22_c_real_inspiral.start_time=\u001b[32m0\u001b[39m\n\u001b[32m 5\u001b[39m plt.figure(figsize=(\u001b[32m10\u001b[39m, \u001b[32m4\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m plt.semilogy(hp22_py_real_inspiral.sample_times,np.abs(hp22_py_real_inspiral-hp22_c_real_inspiral),label=\u001b[33m'Python'\u001b[39m)\n\u001b[32m 7\u001b[39m \n\u001b[32m 8\u001b[39m plt.xlabel(\u001b[33mr\"$t (s)$\"\u001b[39m)\n\u001b[32m 9\u001b[39m plt.ylabel(\u001b[33mr\"Re [$h_{22}$]\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:257\u001b[39m, in \u001b[36mArray._returntype..returntype\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mreturntype\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m ary = \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;66;03m# pylint:disable=not-callable\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ary \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:68\u001b[39m, in \u001b[36m_convert..convert\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 65\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mconvert\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m 67\u001b[39m _convert_to_scheme(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:279\u001b[39m, in \u001b[36mArray._checkother..checkother\u001b[39m\u001b[34m(self, *args)\u001b[39m\n\u001b[32m 277\u001b[39m nargs +=(other,)\n\u001b[32m 278\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(other) \u001b[38;5;129;01mis\u001b[39;00m Array:\n\u001b[32m--> \u001b[39m\u001b[32m279\u001b[39m \u001b[30;43mcheck_same_len_precision\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mother\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 280\u001b[39m _convert_to_scheme(other)\n\u001b[32m 281\u001b[39m nargs += (other._data,)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:135\u001b[39m, in \u001b[36mcheck_same_len_precision\u001b[39m\u001b[34m(a, b)\u001b[39m\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(a) != \u001b[38;5;28mlen\u001b[39m(b):\n\u001b[32m 133\u001b[39m msg = \u001b[33m'\u001b[39m\u001b[33mlengths do not match (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m'\u001b[39m.format(\n\u001b[32m 134\u001b[39m \u001b[38;5;28mlen\u001b[39m(a), \u001b[38;5;28mlen\u001b[39m(b))\n\u001b[32m--> \u001b[39m\u001b[32m135\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m a.precision != b.precision:\n\u001b[32m 137\u001b[39m msg = \u001b[33m'\u001b[39m\u001b[33mprecisions do not match (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m'\u001b[39m.format(\n\u001b[32m 138\u001b[39m a.precision, b.precision)\n", - "\u001b[31mValueError\u001b[39m: lengths do not match (4652 vs 4653)" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hp22_py_real_inspiral = inspiral_modes_py[(2, 2)].real()\n", - "hp22_c_real_inspiral = inspiral_modes_c[(2, 2)].real()\n", - "hp22_py_real_inspiral.start_time=0\n", - "hp22_c_real_inspiral.start_time=0\n", "plt.figure(figsize=(10, 4))\n", - "plt.semilogy(hp22_py_real_inspiral.sample_times,np.abs(hp22_py_real_inspiral-hp22_c_real_inspiral),label='Python')\n", + "plt.plot(hlm_py_real_inspiral.sample_times,rel_diff)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel('absolute 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(r\"Re [$h_{22}$]\")\n", - "plt.legend()\n", + "plt.ylabel('absolute difference')\n", "plt.grid()\n", - "# plt.xlim(1.05,1.25)\n", - "# plt.ylim(-1e-21,1e-21)\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": 31, - "id": "30c6969d-b5fc-43db-a4a7-f50a46462b54", + "execution_count": null, + "id": "d58e9bd0-9bab-4d13-9ce0-faa44199ca7f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dict_keys([(2, 2), (2, -2)])\n" + "Mode | Mismatch (%)\n", + "-------------------------\n", + "(2, -2) | 0.0235\n", + "(2, -1) | 0.0059\n", + "(2, 1) | 0.0059\n", + "(2, 2) | 0.0235\n", + "(3, -3) | 0.0305\n", + "(3, -2) | 0.0221\n", + "(3, -1) | 0.0512\n", + "(3, 1) | 0.0512\n", + "(3, 2) | 0.0221\n", + "(3, 3) | 0.0305\n", + "(4, 4) | 0.0324\n", + "(4, -4) | 0.0324\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "print(inspiral_modes_py.keys())\n", - "hp22_inspiral = inspiral_modes_py[(2, 2)].real()\n", - "hc22_inspiral = inspiral_modes_py[(2, 2)].imag()\n", - "plt.figure(figsize=(10, 4))\n", - "plt.title(\n", - " rf\"\"\"Inspiral-ESIGMA\n", - " ==============================\n", - " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \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", + "from pycbc.filter import match\n", + "from pycbc.psd import aLIGOZeroDetHighPower\n", "\n", - "plt.plot(hp22_inspiral.sample_times,hp22_inspiral,label=r\"Re [$h_{22}$]\")\n", - "plt.plot(hc22_inspiral.sample_times,hc22_inspiral,label=r\"Im [$h_{22}$]\")\n", + "f_low = 20.5\n", + "print(f'{\"Mode\":<10} | {\"Mismatch (%)\":>12}')\n", + "print('-' * 25)\n", "\n", - "plt.xlabel(r\"$t (s)$\")\n", - "plt.ylabel(r\"$h$\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.tight_layout()" + "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", + " flag = \" <--\" if mm > 0.1 else \"\"\n", + " print(f'{str(mode):<10} | {mm:>12.4f}{flag}')" ] }, { @@ -342,47 +352,87 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "id": "311c00be", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================ Python ================\n", - "time taken = 0.8143424987792969 secs\n", - "3113 False\n", - "Orbital evolution took: 1.0490614450027351 seconds\n", - "Modes generation took: 0.4306351409977651 seconds\n", + "Orbital evolution took: 0.8255226190012763 seconds\n", + "Modes generation took: 0.9974629320022359 seconds\n", "WARNING: You entered a very large initial eccentricity\n", "0.4. The orbital eccentricity at the end of inspiral was\n", - "0.038731628947603076. The merger-ringdown attachment with a quasicircular\n", + "0.04529686788536343. The merger-ringdown attachment with a quasicircular\n", "model might be affected.\n", - "Generating MR waveform from 27.66742430543558Hz...\n", - "Hybridizing the following modes: [(2, 2), (2, -2)]\n", - "By aligning (2, 2) mode\n", - "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", - "INSPIRAL mode (2, 2) goes from -234.69690036721462Hz to 161.82422506567798Hz\n", - "MERGER mode (2, 2) goes from -799.1291300472742Hz to 1128.8976373558457Hz\n", - "INSPIRAL mode (2, -2) goes from -161.82422506567798Hz to 234.69690036721462Hz\n", - "MERGER mode (2, -2) goes from -1049.0987227068747Hz to 781.0501686609422Hz\n", + "Generating MR waveform from 26.534760077516623Hz...\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 (3, 3) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (4, 4), (4, -4), (3, 3)] modes\n", + "INSPIRAL mode (2, -2) goes from -101.3546327642494Hz to 232.91499581554135Hz\n", + "MERGER mode (2, -2) goes from -1445.6670577656257Hz to 1960.0435620552219Hz\n", + "INSPIRAL mode (2, -1) goes from -40.93098520474169Hz to 48.64173777218727Hz\n", + "MERGER mode (2, -1) goes from -1494.4500188070979Hz to 638.6670205711117Hz\n", + "INSPIRAL mode (2, 1) goes from -48.64173777218727Hz to 40.93098520474169Hz\n", + "MERGER mode (2, 1) goes from -904.0487073878803Hz to 1570.6508051408562Hz\n", + "INSPIRAL mode (2, 2) goes from -232.91499581554135Hz to 101.3546327642494Hz\n", + "MERGER mode (2, 2) goes from -765.0123834327239Hz to 1422.1370130561534Hz\n", + "INSPIRAL mode (3, -3) goes from -148.29287498947824Hz to 252.27611774527918Hz\n", + "MERGER mode (3, -3) goes from -1838.6690771837468Hz to 578.5158946006699Hz\n", + "INSPIRAL mode (3, -2) goes from -186.96965685318918Hz to 152.28707890607078Hz\n", + "MERGER mode (3, -2) goes from -1907.947484729697Hz to 848.0514925883384Hz\n", + "INSPIRAL mode (3, -1) goes from -96.76611359952858Hz to 1006.143153104066Hz\n", + "MERGER mode (3, -1) goes from -1735.3304783700637Hz to 1461.0162129235505Hz\n", + "INSPIRAL mode (3, 1) goes from -1006.143153104066Hz to 96.76611359953785Hz\n", + "MERGER mode (3, 1) goes from -756.1697297819906Hz to 1785.4736608878568Hz\n", + "INSPIRAL mode (3, 2) goes from -152.28707890607058Hz to 186.96965685318918Hz\n", + "MERGER mode (3, 2) goes from -1809.121151166399Hz to 1800.330071935664Hz\n", + "INSPIRAL mode (3, 3) goes from -252.27611774527932Hz to 148.29287498949677Hz\n", + "MERGER mode (3, 3) goes from -1274.7265246727698Hz to 1304.1247413942117Hz\n", + "INSPIRAL mode (4, 4) goes from -391.1483343239296Hz to 292.58362783222134Hz\n", + "MERGER mode (4, 4) goes from -1228.5894136511452Hz to 1539.0787139288514Hz\n", + "INSPIRAL mode (4, -4) goes from -292.58362783222134Hz to 391.1483343239296Hz\n", + "MERGER mode (4, -4) goes from -950.8725340696178Hz to 1285.7310793344852Hz\n", "blended.\n", "================= C =================\n", - "Orbital evolution took: 0.015542758999799844 seconds\n", - "Modes generation took: 0.04799114799970994 seconds\n", + "Orbital evolution took: 0.01063374300065334 seconds\n", + "Modes generation took: 0.12618732999908389 seconds\n", "WARNING: You entered a very large initial eccentricity\n", "0.4. The orbital eccentricity at the end of inspiral was\n", - "0.03873155501634525. The merger-ringdown attachment with a quasicircular\n", + "0.04327354478674823. The merger-ringdown attachment with a quasicircular\n", "model might be affected.\n", - "Generating MR waveform from 27.667424305474597Hz...\n", - "Hybridizing the following modes: [(2, 2), (2, -2)]\n", - "By aligning (2, 2) mode\n", - "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", - "INSPIRAL mode (2, 2) goes from 9.234580422265868Hz to 163.44646629577284Hz\n", - "MERGER mode (2, 2) goes from -799.1291322431845Hz to 1128.8976566699175Hz\n", - "INSPIRAL mode (2, -2) goes from -163.44646629577284Hz to -9.234580422265868Hz\n", - "MERGER mode (2, -2) goes from -1049.0987416676346Hz to 781.0501706322736Hz\n", + "Generating MR waveform from 26.53687260414924Hz...\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 (3, 3) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (4, 4), (4, -4), (3, 3)] modes\n", + "INSPIRAL mode (2, -2) goes from -101.13788122025537Hz to -9.344887798394275Hz\n", + "MERGER mode (2, -2) goes from -1602.2350110324426Hz to 815.9996534590972Hz\n", + "INSPIRAL mode (2, -1) goes from -40.35333777290539Hz to -4.213193305246234Hz\n", + "MERGER mode (2, -1) goes from -1818.998563105842Hz to 611.6671267873415Hz\n", + "INSPIRAL mode (2, 1) goes from 4.213193305246234Hz to 40.35333777290539Hz\n", + "MERGER mode (2, 1) goes from -950.5339786859378Hz to 1673.0240311970988Hz\n", + "INSPIRAL mode (2, 2) goes from 9.344887798394275Hz to 101.13788122025537Hz\n", + "MERGER mode (2, 2) goes from -836.9570900557211Hz to 1636.851885880366Hz\n", + "INSPIRAL mode (3, -3) goes from -146.3772907860718Hz to -13.353196079332903Hz\n", + "MERGER mode (3, -3) goes from -1258.1511597463514Hz to 613.3064561354477Hz\n", + "INSPIRAL mode (3, -2) goes from -205.0465309682471Hz to -9.04060303994033Hz\n", + "MERGER mode (3, -2) goes from -1310.1967127355997Hz to 936.634685338843Hz\n", + "INSPIRAL mode (3, -1) goes from -108.25866887482478Hz to -8.752817115557857Hz\n", + "MERGER mode (3, -1) goes from -1312.746914232246Hz to 1880.0058565988686Hz\n", + "INSPIRAL mode (3, 1) goes from 8.752817115557784Hz to 108.25866887482478Hz\n", + "MERGER mode (3, 1) goes from -1763.1509420426182Hz to 1866.5824036770528Hz\n", + "INSPIRAL mode (3, 2) goes from 9.04060303994033Hz to 205.0465309682471Hz\n", + "MERGER mode (3, 2) goes from -733.1580996282454Hz to 1243.2247832792584Hz\n", + "INSPIRAL mode (3, 3) goes from 13.353196079332758Hz to 146.37729078605327Hz\n", + "MERGER mode (3, 3) goes from -1528.9189421384854Hz to 509.8709756686946Hz\n", + "INSPIRAL mode (4, 4) goes from 18.728407634539536Hz to 302.89715013651397Hz\n", + "MERGER mode (4, 4) goes from -1340.1955570824953Hz to 1547.4317942831349Hz\n", + "INSPIRAL mode (4, -4) goes from -302.89715013651397Hz to -18.728407634539536Hz\n", + "MERGER mode (4, -4) goes from -977.3154617421369Hz to 1237.298819625818Hz\n", "blended.\n" ] } @@ -417,6 +467,7 @@ " 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", @@ -433,19 +484,20 @@ " 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": 33, + "execution_count": 7, "id": "05fba2cf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -455,8 +507,8 @@ } ], "source": [ - "hp22_py_real_imr = imr_modes_py[(2, 2)].real()\n", - "hp22_c_real_imr = imr_modes_c[(2, 2)].real()\n", + "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", @@ -469,14 +521,11 @@ "# hp.plot(label=r\"$h_+$\")\n", "# hc.plot(label=r\"$h_\\times$\")\n", "\n", - "hp22_py_real_imr.start_time=0\n", - "hp22_c_real_imr.start_time=0\n", - "\n", - "plt.plot(hp22_py_real_imr.sample_times,hp22_py_real_imr,label='Python')\n", - "plt.plot(hp22_c_real_imr.sample_times,hp22_c_real_imr,label='C',ls='--')\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(r\"Re [$h_{22}$]\")\n", + "plt.ylabel(f\"Re [h {plot_mode}]\")\n", "plt.legend()\n", "plt.grid()\n", "plt.tight_layout()" @@ -484,20 +533,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "b445d311", - "metadata": {}, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dict_keys([(2, 2), (2, -2)])\n" + "dict_keys([(4, 4), (4, -4)])\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -508,8 +563,8 @@ ], "source": [ "print(imr_modes_py.keys())\n", - "hp22_imr = imr_modes_py[(2, 2)].real()\n", - "hc22_imr = imr_modes_py[(2, 2)].imag()\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", @@ -533,130 +588,290 @@ }, { "cell_type": "markdown", - "id": "bf734c70", + "id": "19faf573-1338-4d47-8080-580a00f7e45a", "metadata": {}, "source": [ - "## Checking evolution of dynamical quantities" + "## ============== Extracting waveform =================" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "a375d82e-ff58-4b81-bb9c-e7c6feba6a28", + "execution_count": 80, + "id": "e950c6ea-b5a3-49cb-aa9e-f154615fa72d", "metadata": { "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samanwaya/lalsuite-esigma-install/lib/python3.11/site-packages/lalsimulation/_lalsimulation_swig.py:8: 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" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "No version information file '.version' found\n" + "================ Python ================\n", + "Orbital evolution took: 0.8285874569992302 seconds\n", + "Modes generation took: 0.9951321380031004 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.04529686788536343. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 26.534760077516626Hz...\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 -101.3546327642494Hz to 232.91499581554135Hz\n", + "MERGER mode (2, -2) goes from -1445.6670577642733Hz to 1960.0435620551568Hz\n", + "INSPIRAL mode (2, -1) goes from -40.93098520474169Hz to 48.64173777218727Hz\n", + "MERGER mode (2, -1) goes from -1494.450018807084Hz to 638.6670205710793Hz\n", + "INSPIRAL mode (2, 1) goes from -48.64173777218727Hz to 40.93098520474169Hz\n", + "MERGER mode (2, 1) goes from -904.0487073879406Hz to 1570.650805140606Hz\n", + "INSPIRAL mode (2, 2) goes from -232.91499581554135Hz to 101.3546327642494Hz\n", + "MERGER mode (2, 2) goes from -765.0123834329463Hz to 1422.1370130546898Hz\n", + "INSPIRAL mode (3, -3) goes from -148.29287498947824Hz to 252.27611774527918Hz\n", + "MERGER mode (3, -3) goes from -1838.6690771814121Hz to 578.5158946004476Hz\n", + "INSPIRAL mode (3, -2) goes from -186.96965685318918Hz to 152.28707890607078Hz\n", + "MERGER mode (3, -2) goes from -1907.947484730364Hz to 848.0514925929518Hz\n", + "INSPIRAL mode (3, -1) goes from -96.76611359952858Hz to 1006.143153104066Hz\n", + "MERGER mode (3, -1) goes from -1735.3304783691326Hz to 1461.0162129238377Hz\n", + "INSPIRAL mode (3, 1) goes from -1006.143153104066Hz to 96.76611359953785Hz\n", + "MERGER mode (3, 1) goes from -756.169729782954Hz to 1785.4736608872824Hz\n", + "INSPIRAL mode (3, 2) goes from -152.28707890607058Hz to 186.96965685318918Hz\n", + "MERGER mode (3, 2) goes from -1809.12115116626Hz to 1800.3300719358306Hz\n", + "INSPIRAL mode (3, 3) goes from -252.27611774527932Hz to 148.29287498949677Hz\n", + "MERGER mode (3, 3) goes from -1274.726524678106Hz to 1304.1247413937763Hz\n", + "INSPIRAL mode (4, 4) goes from -391.1483343239296Hz to 292.58362783222134Hz\n", + "MERGER mode (4, 4) goes from -1228.5894136503114Hz to 1539.0787139291294Hz\n", + "INSPIRAL mode (4, -4) goes from -292.58362783222134Hz to 391.1483343239296Hz\n", + "MERGER mode (4, -4) goes from -950.8725340695437Hz to 1285.7310793342444Hz\n", + "blended.\n", + "================= C =================\n", + "Orbital evolution took: 0.010596448999422137 seconds\n", + "Modes generation took: 0.12539378100336762 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.04327354478674823. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 26.536872604149245Hz...\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 -101.13788122025537Hz to -9.344887798394275Hz\n", + "MERGER mode (2, -2) goes from -1602.2350110337395Hz to 815.9996534588564Hz\n", + "INSPIRAL mode (2, -1) goes from -40.35333777290539Hz to -4.213193305246234Hz\n", + "MERGER mode (2, -1) goes from -1818.9985631079078Hz to 611.6671267875454Hz\n", + "INSPIRAL mode (2, 1) goes from 4.213193305246234Hz to 40.35333777290539Hz\n", + "MERGER mode (2, 1) goes from -950.5339786859888Hz to 1673.0240311976638Hz\n", + "INSPIRAL mode (2, 2) goes from 9.344887798394275Hz to 101.13788122025537Hz\n", + "MERGER mode (2, 2) goes from -836.9570900554431Hz to 1636.8518858814962Hz\n", + "INSPIRAL mode (3, -3) goes from -146.3772907860718Hz to -13.353196079332903Hz\n", + "MERGER mode (3, -3) goes from -1258.1511597478336Hz to 613.3064561359943Hz\n", + "INSPIRAL mode (3, -2) goes from -205.0465309682471Hz to -9.04060303994033Hz\n", + "MERGER mode (3, -2) goes from -1310.1967127427145Hz to 936.6346853426041Hz\n", + "INSPIRAL mode (3, -1) goes from -108.25866887482478Hz to -8.752817115557857Hz\n", + "MERGER mode (3, -1) goes from -1312.746914232343Hz to 1880.0058565994848Hz\n", + "INSPIRAL mode (3, 1) goes from 8.752817115557784Hz to 108.25866887482478Hz\n", + "MERGER mode (3, 1) goes from -1763.1509420447535Hz to 1866.5824036765525Hz\n", + "INSPIRAL mode (3, 2) goes from 9.04060303994033Hz to 205.0465309682471Hz\n", + "MERGER mode (3, 2) goes from -733.1580996279768Hz to 1243.2247832772853Hz\n", + "INSPIRAL mode (3, 3) goes from 13.353196079332758Hz to 146.37729078605327Hz\n", + "MERGER mode (3, 3) goes from -1528.9189421382907Hz to 509.8709756688243Hz\n", + "INSPIRAL mode (4, 4) goes from 18.728407634539536Hz to 302.89715013651397Hz\n", + "MERGER mode (4, 4) goes from -1340.1955570834773Hz to 1547.4317942803186Hz\n", + "INSPIRAL mode (4, -4) goes from -302.89715013651397Hz to -18.728407634539536Hz\n", + "MERGER mode (4, -4) goes from -977.315461742174Hz to 1237.2988196264664Hz\n", + "blended.\n" ] } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import lalsimulation as lalsim\n", - "from esigmapy.python_codes import esigma_pn_main as espn\n", - "import lal" + "print('================ Python ================')\n", + "hp_py, hc_py = gp.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": 6, - "id": "4e7c8da2-ed95-495a-a859-753676dbb471", + "execution_count": 58, + "id": "04ba5692-e543-4c24-9110-7406324cee82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "radiation PN order is 8\n", - "mode PN order is 4\n", - ":/home/samanwaya/nrsur7dq4\n" + "2476\n", + "2476\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)" + }, + { + "data": { + "image/png": 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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", + "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", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(\"$h_+$\")\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", + "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": "61abe72a-af7b-4343-8b9a-24ad6112456c", + "id": "f8212777-6614-4ece-b493-e7978c2bab28", "metadata": {}, "source": [ - "### LALSuite (C) and python" + "### Match between waveforms" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "b4e8a2c7-07ee-4e5a-91bc-4ed30c142804", + "execution_count": null, + "id": "ac35d7b2-0dea-410f-8f59-dfb2c6227345", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mismatch is: 0.0501%\n" + ] + } + ], "source": [ - "m1 = 20.0 # masses (in solar masses)\n", - "m2 = 30.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = -0.5\n", - "eccentricity = 0.1 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\n", + "from pycbc.filter import match\n", + "from pycbc.psd import aLIGOZeroDetHighPower\n", "\n", - "distance = 400.0 # source luminosity distance (in Mpc)\n", - "inclination = 30 * np.pi / 180.0 # orbital inclination with line-of-sight\n", + "f_low = 20.5\n", "\n", - "f_low = 20.0 # starting frequency (in Hz)\n", - "delta_t = 1 / 4096. # time grid-spacing (in s)" + "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": 8, + "execution_count": 63, "id": "ca70e4cd-94b4-4ef9-bf3c-6054f4e724c3", "metadata": {}, "outputs": [ @@ -664,13 +879,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "time taken = 2.5000295639038086 secs\n", - "4783 False\n", "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", @@ -696,13 +911,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 64, "id": "73c89a53-73d7-4b80-a8df-52a9be1e6443", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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EpHdXL4Qj87su+Dj5fbQpG4e1rwUhgAHnkbAnpxC2trZyx+6YmBh57OLiUqT9oMxmM7KyspCRkQGbe23roFElVTfRgyMCjmgD0RaiTYiI9C7y+D8ov2kIKiMB12yq4b9DfOHh4aZ0sTSLIeceqlevLr/mBJ2ifkCnp6fD2dlZd5tllnTdRMDJaQsiIj0L/+c31PrzFbga0nHBti7KvboVlavXUrpYmsaQcw/iA1wsqle1alUYjcYinVTx/L1796JDhw66u8xSknUTr88eHCIqDUK3r0SzAxPhaDDitEMLeI7dArfyHIPzuBhyHkB8yBb1g1Y8Pzs7G05OTroLOXquGxGREnb/thrtD4+HrcGCYy7t0OSNn+HkXPRNmuluDDlEREQKXf5fuPsC/rvPCSsdvGCp6AXvsctgZ+/A9rAShhwiIqISZjaZ8MlvZ7HkQBQAR+wPXIwJ3VrBoLPJKkpjyCEiIipBxqxMhM0fCrc7rgCex/SeTfHKE3XZBsWAIYeIiKiEpKUkImJ+f/hlhKC1rS2aPTMKXRhwig1DDhERUQlIjLuFG4t6o1X2GaRbHHC+0wJ0ad+O574YMeQQEREVs5hrF2H8sT8am68gEWVwo+ePaOX3NM97MeMIJyIiomKUkXAdNsu6o475CmJQEfEDt6IxA06JYMghIiIqJieuJiI0MhruiMUVgwfML29HnSa+PN+lOeTMmjULfn5+cHV1lSsO9+nTB+fOncv3nJEjR8pVifPeAgMDFSszERFRXnvP38awpUew1tgec8tOQpkxO1G9VkOepNIecvbs2YNx48YhODgYO3bskCvsdu3aFampqfme161bN9y4cSP39vvvvytWZiIiohyH//gRU5bvQFqWCY3KmfHy61NQsWoNnqASpsqBx9u2bct3vHTpUtmjc/ToUblnUg5HR0du3khERKoS/NNn8D87G9/b1saSRgvQzjUOZRxV+XGre5o464mJifJrxYoV892/e/duGX7ETtUdO3bEp59+Ko8Lk5mZKW85kpKScjecLOoGnA+S83rWfl01YN20i22nXWw7bbCYzTjy4ztoe20JYABSKvvgo36tsGvXX/w8sKKifLYaLGLzDBUTxevduzfi4+Oxb9++3PvXrl2LsmXLonbt2rh06RKmT58uL2uJ3h7Rw1PQjBkzMHPmzLvuX716NVxcXIq9HkREpF9msxlup5fjKePf8vgXl/4weT0Hg41B6aLpTlpaGoYMGSI7QNzc3LQdcsTYnN9++w379++Hp6fnPZ8nxuSIwLNmzRr069fvoXpyatasidjY2AeepEdJmWIsUZcuXXS3Uzfrpl1sO+1i26lbZnoqzn8zFD5p+2G2GHCo6Xvw7fcv+RjbzvrE53flypUfKuSo+nLVG2+8ga1bt2Lv3r33DTiCu7u7DDkRERGFPi56dwrr4REhpLiCSHG+ttJYN+1i22kX2059kjOMCPnmNTyZth9ZFjuEB/4HQc++dNfz2HbWU5TPVVWGHNG5JALOpk2b5LibunUfvHFZXFwcrly5IsMOERFRcbudnImRSw8jMb476jicQlqXz+H9xHM88Spip9ZLVGKszJYtW+RaOTdv3pT3lytXDs7OzkhJSZFjbPr37y9DTVRUFN577z3ZfdW3b1+li09ERDp35VYcXvzxBKLj0lC5bA2kjdiP5jXzT44h5alynZxFixbJa22dOnWSISbnJgYbC7a2tjh58qQckNywYUOMGDFCfj148KAMRURERMXlwslgOCzyRaP4PahZ0RnrX2/LgKNSquzJedBYaNGbs3379hIrDxERkRB+4HfU2v4yXA3p+Jfz76g0egqqluMMXbVSZcghIiJSm2N/rkTTfybC0WDEaYcWqDFmM8ox4KgaQw4REdEDhGz4Ej4nZsLWYMExl7ZoMv5nOLmU5XlTOYYcIiKi+6xiHLzifQRdWiBXMT5cvjt8xi2Hnb0Dz5kGqHLgMRERkdLMZgs+/vUMzv3f+msHPUbA781VDDgawp4cIiKiArKyzZiy/jg2h12HAcPh4dsTXfuM4HnSGIYcIiKiPFKTE/Dnd+/jt5gusLOxx39e8EFX7xo8RxrEkENERPR/4m5dxZ3v+qBvdgRMDldRaehidG5UledHoxhyiIiIAFy7GA7Lin7wstxEPFzRsvcENGTA0TSGHCIiKvUiwvah4uahqIREXDdUhWnIejT0alXqz4vWMeQQEVGpdmL3BjT4ewxcDJm4YFsP5V7dgsrVayldLLICTiEnIqJSa+vhc/D8+00ZcE45eqPqm7sYcHSEPTlERFTqiD0Sv917EZ//EYkgmwmYWOUovMcuh4Ojk9JFIytiyCEiolLFnJ2N+Rt3YW5otjxu3q4n/J59GzY2BqWLRlbGy1VERFRqZKSnImxePwwPfwn1Ddfwfo8mmNajKQOOTrEnh4iISoWkhFhcXdgHPlknkQVbzOrgCP/29ZQuFhUjhhwiItK9mGuXkPpDHzQ1RyHF4oyop7+Ff/veSheLihlDDhER6Vr02VA4rhmAuriNWJRHYv+f0LxlW6WLRSWAIYeIiHQrPOwgPDf3Rzmk4orBA7bDN6F+3cZKF4tKCAceExGRLu04fQuD1sfgrLkmztk1Qtmxf8GDAadUYcghIiLdWR0cjdErjiA52w4ran+OmhN3oEIVd6WLRSWMl6uIiEg3LGYzgpe+jcRLt2C2DMZA35r4tG9z2Nny//SlEUMOERHpgjErE2ELRyAo4Q8E2QHlvftgUL8WMBi4yF9pxZBDRESal5x4B1EL+8MvMxTZFhscbf4+Bvd/XulikcIYcoiISPNr4CT/0BctzJeQZnFERMf5CHhygNLFIhVgyCEiIs26ePowyqwbhPqIk2vgxPddiVat2ytdLFIJjsQiIiJNOhAZiwVrf0c1xCHaxhNZI7bDiwGH8mBPDhERac6mY1cxZf0JGE1+qFntHbw0/GWUq1RN6WKRyjDkEBGRpqaI71/1Kf4dXhtGVEKPlu54/YVucLK3VbpopEIMOUREpAnZxiwcXTQK7e9swVKHWvjFfwXe6t4KNjacIk6FY8ghIiLVS01OQOTCFxCQfhhmiwFJTYdgSs/WSheLVI4hh4iIVC325mXEf9cXrUyRyLDY40y7eQjo+qLSxSINYMghIiLVij4XBvs1L8DLEoN4uCGm1zJ4+z6ldLFIIxhyiIhIlQ5fugPDT2PghxhcNbgDQ9ejUYPmSheLNITr5BARker8euI6XvzhEN7MGIODjm3hMmYXPBlwqIjYk0NERKqaIr7599/xr/3/mzHVomlTtB70K5wdOEWcio4hh4iI1DNF/JtX0TduM3bYvImqgYMwvWdT2HKKOD0ihhwiIlJcSnICLi8ejICMEDlFfFgzRwT2agqDgWvg0KNjyCEiIkVlpdxB/Pyn0cochXSLA862m4ugrsPYKvTYGHKIiEgxF08eRMfzM1HVEC93Eb/T+0d4+3Rki5BVMOQQEZEi9h85Bp9fnoeLIROXbGrBacR6NKzdiK1BVsMp5EREVOKW/XMJwzdcxyrTUzhqaI7yY3fAnQGHrIw9OUREVGJM2dmY/Wsovg2+LY8jW76FynZRaFmuEluBrI49OUREVCLSUhJxYm4vPBn6BhxgxNRujfFxnxawseX/t6l48J1FRETF7vb1aCT+0BfepgvINNhj2TP2aNupPoxGI88+FRuGHCIiKlaXwg/B+echaIBYucnmrR5L0db/aZ51KnYMOUREVGxO7N6Aen+PQ1lDOi7b1IDdi+vRuF5TnnEqEQw5RERULP7ZtBABYdNgZzDjtEMLeI7eCLdKVXm2qcQw5BARkVWZzRb8e9tZ7Dxsh00OTjhX7gm0GrsCDo5OPNNUohhyiIjIatIzszHp5+P449RNADWw0W81RnTvCIMNJ/NSyVPlu27WrFnw8/ODq6srqlatij59+uDcuXP5nmOxWDBjxgx4eHjA2dkZnTp1Qnh4uGJlJiIq7WKvRyNqzhNIOL0LDrY2mDewNUb27MyAQ4pRZcjZs2cPxo0bh+DgYOzYsQPZ2dno2rUrUlNTc58ze/ZszJ07F/Pnz0dISAiqV6+OLl26IDk5WdGyExGVRpEnDsC0uDOaZJ/B5w5LsPLlNujjXUPpYlEpp8rLVdu2bct3vHTpUtmjc/ToUXTo0EH24sybNw/Tpk1Dv3795HOWL1+OatWqYfXq1Rg9evRdr5mZmSlvOZKSkuRXsUaDtddpyHk9Pa7/wLppF9tOu9Tedsd3/oQmwW+hjCET0QZPWAavhnetCg9VXrXX7XHpuX5GhepWlJ9nsIjEoHKRkZHw8vLCyZMn0bx5c1y8eBH169dHaGgovL29c5/Xu3dvlC9fXgaegsSlrZkzZ951vwhFLi4uxV4HIiK9sZgtQOQfeC5lLWwMFrkH1aUm42DvWEbpopGOpaWlYciQIUhMTISbm5u2Q44onggv8fHx2Ldvn7zvwIEDaNeuHa5duybH5OR47bXXEB0dje3btz9UT07NmjURGxv7wJP0KClTXGYTl8/s7e2hJ6ybdrHttEuNbZeVlYVT341CQMLv8ji4Uh+0emUh7OwdNF83a9Jz/YwK1U18fleuXPmhQo4qL1flNX78eJw4cQL79++/6zGDwXBXICp4Xw5HR0d5K0g0THE1TnG+ttJYN+1i22mXWtouPjULr684jgFxSfC1MSCk8RQEDHr3nn9/tVS34qLn+tmXcN2K8rNUHXLeeOMNbN26FXv37oWnp2fu/WKQsXDz5k24u7vn3h8TEyPH5RARUfGIjEnBK8tDEB2XhvOOY1D36bEIbN+Dp5tUSZWzq0SPjOjB2bhxI/766y/UrVs33+PiWAQd0U2Wt+tUzMpq27atAiUmItK/k/u2IHzhEFyJS4FnBWesGdMRPgw4pGKq7MkR08fFgOAtW7bItXJEj41Qrlw5uSaO6BKdOHEiPvvsMzkgWdzEv8UAYjEYiYiIrOvQuv+gTfinaGEw43aVpugzeiYql717CACRmqgy5CxatEh+FQv8FZxKPnLkSPnvKVOmID09HWPHjpWDkgMCAvDnn3/KUERERNZhys5GyOKxCIxZCxiAI25P48Ux0+HkzIBD6qfKkPMwE75Eb46YFi5uRERkfcmJd3Dxm0EITD8kjw/Wfh2BI2ZxBWPSDFWGHCIiUtb1qHPI+HEAWpmjkGGxR3jAHAR1f4nNQpqiyoHHRESknKPR8Xh3xS54mK7hNirgcu8NaMOAQxrEnhwiIsq1Jewa3l5/AlnZtfFJ5fcxfmAvNKxZn2eINIkhh4iIYDaZELxkMr6/WB9Zlnp4ukk1vDvoGZRx5McEaRffvUREpVxKUjwivxmMtmkHsdihIlb7bcDE7t6wtXn0FYyJ1IAhh4ioFLt28QyMKwegtfkyMi32uNZmCib39FG6WERWwZBDRFRKnT7wG9z/HI0KSEYsyiOu11L4+j6pdLGIrIYhh4ioFDr083/gc+oz2BtMiLBrgHIjf0Yjz3pKF4vIqjiFnIioFDGazPhw8wkkn/hVBpyjrk+i5qQ9qMqAQzrEnhwiolIiIS0L41aH4p/IOGzAOMxvFImOQ6ZyBWPSLYYcIqJSIPpsKPZuWIh/kvvAxcEOXwxsj07NXlC6WETFiiGHiEjnjv+1DvX2vIlhhnTcLFsRPV95H03c3ZQuFlGxY8ghItIpi9mMQ6tnwj/iK9gYLDht3xyvvDoBFasy4FDpwJBDRKRDmRlpOLHoJQQmbgMMwOEKPdH69R/g4OikdNGISgxDDhGRzsTevIzY7wfAL/sMsi02ONL4bQQMfIcDjKnUYcghItKRU9cSsWjZRnxlPIckQxlEP7UAgR36Kl0sIkUw5BAR6cQvx6/j7fXHkWH0gkf5CRjevy9aeLVSulhEimHIISLSOFN2Ng4unYJ5Fxsjw1IDHRtWwfjB01DO2V7pohEpiiGHiEjDEuNjEfXtIDyREYLF9u7YGLAOk55twR3EiRhyiIi0K+psKGzXDkUry3WkWxyQ4D8Zb/doqXSxiFSDPTlERBp0bMcqeO2fjLJigT9UQUq/5WjTqp3SxSJSFYYcIiINMZtMOLT8PQRd/kauf3PaoQWqv7IGDap5Kl00ItXhLuRERBqRkpmNcatCYBO1Wx4fqtwPXm/tQkUGHKJCsSeHiEgDbqcDAxYfQkRMKsJsJ+JL73gE9h2rdLGIVI0hh4hI5U7t34LM8F8RYXwBVV0dsWBYN/jUqqB0sYhUjyGHiEjlG2z6RXwFb1sLEio0x6hR41HNjftPET0MhhwiIhXKSEvBqW9GIDBppxxg/LddB0x49VWUdWXAIXpYDDlERCpz83IEUpYPhK/pgtxgM6TRW0h0bgZHJxeli0akKZxdRUSkIqcP/gGHJU+igekC4uGGc11XwveFKTDYGJQuGpHmMOQQEamAxWLBioNRWPBrMCoiCRds6yHjpZ1o1q6H0kUj0ixeriIiUliG0YT3N5/C+qNXAfjDp/YMDBk6Cs5lXJUuGpGmMeQQESnoRvR5XFs5Bv8kj4CNoRKmdGuMlzt0h8HAy1NEj4shh4hIIaf2bUGNXePhiyTMcTIBQ9fjCa/KbA8iK2HIISJSYv2bVTPgF/k1bA0WRNrWR/3h38K9NgMOkTUx5BARlaDU5ASc+3Y4AlP2yPVvQso/ixavfQ8nl7JsByIrY8ghIiohly+dh3lFf/iYL8NosUVo06nwf+FtGGw40ZWoOPA3i4ioBOw6cwvPLzuD9GwgFuVxocdaBAycyoBDVIzYk0NEVIzMJhO+2hWBr/66IP/kfu35ET7u54PGHrV53omKGUMOEVExSYyPxaXFQ5GdVAtAH4wIqo1pPZrCwY6d6ESaCDmnT59GeHg4YmJi5LoOVapUQfPmzdGkSRPrlJCISIMunQ6B/c/D0NpyAw3tjqFx97Ho1a650sUiKlUeKeScOXMGixYtwrp163D79u3cJcmFnAWsKleujAEDBmDMmDFo2rSpNctMRKRqR3/7Hk0OvwcXQyZuoApS+y1Dr1beSheLqNQpUsiJiorClClTsGHDBjg7O6N9+/YICgpC/fr1UalSJRl07ty5g8jISAQHB2Pp0qVYuHAh+vfvj9mzZ6NOnTrFVxMiIoVlG7Nw5Ps3EXjrJzk9/KSjNzxH/QT3Ku5KF42oVCpSyGncuLG8DLVkyRIZXMqWvf+6DikpKVi/fj2++uor+X3p6emPW14iIlWKS87A5fk9EZgZIo8Pug+H/ytfwtaOQx+JlFKk375Vq1bJcPOwRAgaOXKkvG3cuPFRykdEpHphVxIwduVRdExpiUZ2J3A2aDaCuo1UulhEpV6RhvgXDDhTp06Vl6YeRr9+/Ur9ySYi/W3PsG7vMbzwzQFcT8xAcIXncHPEP/BhwCFShceaxzhnzhwcPnzYeqUhItKI9JQkHJ03AEE7X4CLKRndmlXH1vHtUK+el9JFI6L/U6yLNaxevRo1a9Yszh9BRFTirkScwM25T8A3aQfcDXGY45uIRS/6wNXJnq1BpCJFHhH39ddfY8eOHfD395fHSUlJ93yuyWTC9evXH6+EREQqErp9BbwOvA1XQ7rcnuHWM4vQtW13pYtFRNboySlfvjyOHDmCDz/8UK6JM27cOLkmTpcuXeQYnbVr1yIiIgLZ2dk4cOCAfKyo9u7di169esHDw0P+jM2bN+d7XAxkFvfnvQUGBhb55xARFWV6+MFvx8Hn4HgZcM7YNwNe24NmDDhE+unJGT58uLyJNXPq1auHnj17wsbGBqGhodi1a1e+BQGFYcOGFblQqampaNWqFV566aV7zubq1q2bXIcnh4ODQ5F/DhHRw4hJzsD+byegX8oaeRxcbRDavPI17B0ceQKJVOyRF3AQC/v17t0bo0ePloFDiIuLw9GjR3Hs2DFcvHhRhqA333yzyK/97LPPytv9ODo6onr16o9afCKihxISdQfjVoUiM/lJNHM8gNTASQh89iWePSINeKxVqjZt2pTvWKx63LVrV3krbrt370bVqlXl5bOOHTvi008/lcf3kpmZKW85csYSGY1GebOmnNez9uuqAeumXWy7ok8P3/nbWrxxpBJMZqBBlWrAwN1oUc2txH+32XbaxbazvqL8/hksOZtOqZS49CXCVJ8+fXLvE+N+xEKDtWvXxqVLlzB9+nQ5Bkj0IokensLMmDEDM2fOLHQGmIuLS7HWgYi0JTsrHdXP/oB2psN4xzgK58t3wqD6ZjjaKl0yIkpLS8OQIUOQmJgINzc364WcnTt34umnn36kM/yo31tYyCnoxo0bMvCsWbPmnosOFtaTI6a3x8bGPvAkPUrKFDPQxGBse3t9TSll3bSLbfdwos8dg8OGkahluQajxRYhjafAt/+kfGMNSxrbTrvYdtYnPr/FpKaHCTlFulwlxsm0bdsWEydOlAOOH/QBLhr3119/xbx583Dw4EFkZWWhOLi7u8uQI2Z13Yvo4Smsl0fUobiCSHG+ttJYN+1i293b0d++Q5PD0+Tu4TGoiDs9v0Nbv0f7j11xYNtpF9vOeoryuVqkkBMWFobJkyfLGU8VKlTAU089hYCAALkLecWKFeUu5PHx8XKrB7ES8l9//SWPxRgd8b3FRQx4vnLligw7RERFlZWZgWM/jEdAzM9y9/BTjq3h/vIqNK7myZNJpGFFCjnNmjXDtm3bEBwcjEWLFsnLSGKX8YLduCLsiC4kcelozJgx8PPzK1KhxO7leffEEuNuREgSQUrcxPgaEbREqBFT2d977z3ZddW3b98i/RwiopuJGfh62Up8cme9DDjBNUbA76W53D2cqLTOrhIL74nbkiVL5Po44eHhuH37tgw7VapUQfPmzeHt7S3Xz3kUYrHBzp075x5PmjRJfh0xYoQMVydPnsSPP/6IhIQEGXTEc8VgZFdX10f6eURUOu09fxsT14bhTqo7KjgNQ9d2gQjsMlTpYhGRGqaQ29rayl6aovbUPEinTp1kb9C9bN++3ao/j4hKF1N2NoJ/fA8fRjbEHbM7mnm4YcDQz1G7Uhmli0ZESoUcsQqxj4+P/Cp6alq3bo1y5cpZszxERMUq9uYV3Fz6ItplhmGBXW2sbr0c7/dqCSd7zg8nKtUhx8nJCevWrcPy5ctzx+GIWU05gSfnq6cnB+sRkfqcPvgHqmwfg+aIR5rFEWm+Y/HJc95KF4uI1BByDh06BLPZjHPnzsmtG3Jue/bskYOQc4KPGBycE3rEbfDgwcVVfiKiBzKbTDi08gP4X1wAW4MFUTa1YBiwHL6NfXj2iHSsyGNyxGDiJk2ayJtYcTDH5cuX5QyovOFHbNgpgg9DDhEpJf5OLC5/NxhB6Yfl7KmQcs+g2avfwaUsL7UT6d1jDTzOq1atWvL23HPP5d53584dGXaIiJRw7HI8Jq46ii/T7yDDYI8TLd+HX983YXjEmZ9EVEpDTmHEZSuxYCARUUkSm2su++ciPtt2HkaTBf+u+A4+e7Ym/FsEsiGISpFiDTlERCUtKSEOkd+NQGZieRhNg9GjhTs+798Crk763GKFiO6NIYeIdOPiyYMo+8so+FhuobmtHSp3Hof+T3orurkmESmHIYeIdHF5Kvvi36gTuhKOBiNuoAqSe/+A532ClC4aESmIIYeINC01OQGnF7+C/sk75eypMJcg1H1lOdwrVVO6aESkMIYcItKs8zcTYV78NPzMEci22OBw/fEIenEmZ08RkcR5lESkOWJvu5+PXEHvBQexMKMrYlAR62u8C7/BHzDgEFEu9uQQkeYuTy3cuAsLzjjJ43iv3rD0mgjngweVLhoRqQxDDhFpxoWTwXDY+BKGm9OwzjALI7v6Y0zH+jCZspUuGhGpEEMOEWli9lTIhi/Q6tS/5eypGENFLO1fE819G8jHTSalS0hEasSQQ0SqX9wv4vuX4Z+yW86eOu4cgFovL0fzKu5KF42IVI4hh4hUK+LYXrhsHYU2llswWmxx1OtN+A+eDhtbW6WLRkQawJBDRKqcPbXsQBTKbZ+Dfja35OJ+Sb0WI9D3SaWLRkQawpBDRKqSkJaFt9efwI7Tt1AGI1C+WiW0GTEH7hWrKF00ItIYhhwiUo2zITsRvu077EgdBgdbW0zp4YvOQf259xQRPRKGHCJSnNlkwuFVM+B7YT4aG8y46FYfz46YiuY1yildNCLSMIYcIlLUnZhruLJkBAIzQuTsqaOuT+L1UW/BtRwDDhE9HoYcIlLM6QO/o/Kf49AKd5BhsceJltPg13cCt2YgIqtgyCGiEmcyW7Bv1Sy0j5wNW4MF0TaeMPdfAv9mAWwNIrIahhwiKlExSRn417owpF9wxhMOBoSU74Zmo76FS1leniIi62LIIaIS88+xk3jz15uIS82Cs30T/NV5E7p26sQWIKJiwZBDRMUuMyMNx5b+Cz43N6JS1seo6t4M/x3sjQZVy/LsE1GxYcghomJ1JeI4Mte8hEDTBTl76q16V9Bh5KtwsufWDERUvBhyiKj4dg7fugjNj82EiyET8XBF9BNz0PXpwTzjRFQiGHKIyOqSE+/g3A+vwj9pp+y9CXdoiSojfkTrGnV5tomoxDDkEJFVhV1JwL4fP8Ybxp3IttggpO5o+L/4CWzt+OeGiEoW/+oQkVWYzRYs3ncR/9l+Dmbzk2jgchF1u41HkH8XnmEiUgRDDhE9ttibl3Fs5TR8GdsP2XBAjxY10LbfepRztufZJSLFMOQQ0WM58fd61NgzCV2QiHcdjHDqORsD/Wpy53AiUhxDDhE9kqzMDIQumYjAWz/J44s2ddB50BTUblyLZ5SIVIEhh4iK7ErkSWT8NBKBpkh5fKhyf7R65b9wci7Ds0lEqsGQQ0RF8s8fP6F18ESUMWQgAWVxqe2/EdD1RZ5FIlIdhhwieigpmdmYvvkUQsIy8buDDcIdWqDy8OXw9qzPM0hEqsSQQ0QPdOr0KYz99TYu30mDjaEKtrZZisHdn+baN0Skagw5RHRPpuxsHF7xPnyjFqOecTJM5YMwb1Br+NWpyLNGRKrHkENEhboRfQ7xK19GkPGU3JphZNVIeI+ewrVviEgzGHKI6C5HfvsODUM+gDvSkGJxxhmfD9Cx1+sw2NjwbBGRZjDkEFG+jTXPLnkdfonb5fE5u8YoO2QZ/Oo14VkiIs1hyCEi6Wh0PDas+hafZW2HyWLA4VqvwG/4LNjZO/AMEZEmMeQQlXLZJjMW/H0BX/8VAZO5JRqX6Q//rgMRFPCM0kUjInosDDlEpdj1qHO48tMErEgcDhPKoXdrD/Tp8y3cnLixJhFpH0MOUSl19Jdv0PDIDAQY0vGJI5DR5wf08a6hdLGIiKyGIYeolElJjMOFH8fDN2mHnBp+1r4pWg6ZB4+6DDhEpC8MOUSlSEZMBNIXvAVfS8z/BhfXfhV+wz7l4GIi0iVVLnqxd+9e9OrVCx4eHjAYDNi8eXO+xy0WC2bMmCEfd3Z2RqdOnRAeHq5YeYm0MLj4t/VL8PzVT+BhicF1QzVE9PgZQS/PYcAhIt1SZchJTU1Fq1atMH/+/EIfnz17NubOnSsfDwkJQfXq1dGlSxckJyeXeFmJ1O7KnTQMXByMaccr4YqlKkLcusB1YjAa+3dRumhERKXvctWzzz4rb4URvTjz5s3DtGnT0K9fP3nf8uXLUa1aNaxevRqjR48u9PsyMzPlLUdSUpL8ajQa5c2acl7P2q+rBqybdljMZgTv2oBxhyogOdOMso5l8VONGZjwYh/Y29vr6v2p5/el3uun57rpvX5GhepWlJ9nsIjUoGLictWmTZvQp08feXzx4kXUr18foaGh8Pb2zn1e7969Ub58eRl4CiMub82cOfOu+0UwcnFxKcYaEJU8Y3oy3COWo53pMD40jsA+l654sYEJlZzYGkSkbWlpaRgyZAgSExPh5uamvZ6c+7l586b8Knpu8hLH0dHR9/y+d999F5MmTcrXk1OzZk107dr1gSfpUVLmjh075CU08T9mPWHd1C98/xbUOPY+qiAeRostnvYqh3cGdYHFbOL7UqP4e6ddbDvry7kS8zA0F3Ly9vDkJTqkCt6Xl6Ojo7wVJEJIcQWR4nxtpbFu6pORloLjSycg4PZ6eXzZpgayen2D9t4d8nXxsu20i22nXWw76ynK56rmQo4YZJzTo+Pu7p57f0xMzF29O0SlReSJg7Df/CoCzFfk8aHK/dDypa/hXMZV6aIRESlGlbOr7qdu3boy6IjLQTmysrKwZ88etG3bVtGyEZU0k9mCBX9HYsq6I/AwXUcsyuNExx8QMH4pAw4RlXqq7MlJSUlBZGRk7vGlS5cQFhaGihUrolatWpg4cSI+++wzeHl5yZv4txg8LAYiEZUWV2/F4l+bziMkKh5APSz1nI4B/QehZZX/38NJRFSaqTLkHDlyBJ07d849zhkwPGLECCxbtgxTpkxBeno6xo4di/j4eAQEBODPP/+Eqyu75kn/xNTwkC0L0CDs30jOmoayjvUw47lm6O/T/b7j0oiIShtVhhyxgvH9ZraLP+RiSri4EZUm8bdvIGrZKPin7pf7Tr1VfjcavToCNStyGQQiIk2EHCK62/G/f0aNPW/BGwnIstjiaL0x6Dx0Jmzt+GtMRFQY/nUkUrn01GScWPomAmI3yuNom5ow9v4WQa3aKV00IiJVY8ghUrETVxPw14pZmJj5v4ATXOUFtH5pHpxcyipdNCIi1WPIIVLpruGLdl/AV7siYDY/gaYuJ+He6VUEduirdNGIiDSDIYdIZa5dDMf5ddMxP2EYsuGAHi1qwL/vZpR3cVC6aEREmsKQQ6SiqeGH13+BFuFz0NmQiXcdnVGuz7/Rp3UNTg0nInoEDDlEKhBz7RJurngVARkhcmp4uEMLPDP0A7jX9lS6aEREmsWQQ6Rw783R37+H15EZaIlUZFrscazRBPgPfA82trZsGyKix8CQQ6SQO6lZ2LNkGvrGLZbHEXZecHh+MQIb+7BNiIisgCGHSAG7ztzC1A0nYZfSAh0dXXGu9mD4vvgJ7B0c2R5ERFbCkENUgpIT7+D3dd9j6oVm8tiram3c6HMIQfVqsB2IiKyMIYeohIT/8xsq7piAgbiN7bZvo37bfpjctRGc7Dn2hoioODDkEBWzjLQUhC2bhMCYtfL4uqEqJvf0RbOgpjz3RETFiCGHqBhFHNsLh1/GINB8VR4frtgLTUf+Fx5uFXjeiYiKGUMOUTEwmsw4sGIm2l36GnYGM2JRHtc6zIH/kwN4vomISghDDpGVRdxKxqR1x+Fxw4CODmYcde2M+iO+QavK1XmuiYhKEEMOkZWYsrOxYcdevP9PJrKyzbji0hb7n/DDE0/24DkmIlIAQw6RFVy/dBYJP43C05mXMCd7Npo18sK/+7dENTcnnl8iIoUw5BA97qaaG75E81Oz4WHIQBocMae9AR27+3FTTSIihTHkED2im5cjcHvVawjIDJWbap62b45yg79Hp3pNeE6JiFSAIYeoiCwWC0I2fY2mx2ehuiEdGRZ7hDWaAL8B78LWjr9SRERqwb/IREVwIzEd72w4iacv7oW/XTrO2jVBmYHfItCrFc8jEZHKMOQQPeTYmy0hEZj+RxSSM7Jx3O5F1G3oi6ABb7P3hohIpRhyiB7g9vUoXF/xGqqkpCDF+C5a1ayIL15ohQZV+/LcERGpGEMO0X16b47++g0ahn6CVkhFlo0dZrezQd/uQbCzteF5IyJSOYYcokLE3ryMqz+Ohm/aAXkcYecF+/7f4IUmvjxfREQawZBDlIfFbMGxP35Aw9CP0RopyLLYIrTu6/AdOgN29g48V0REGsKQQ/R/4lIy8eN5Cz5P/RrlbVIQaVsftv0WIbBZAM8REZEGMeQQAfj1+FVM33Ia8WkOmGI7Bu/UuYw2L34CewdHnh8iIo1iyKFS7U7MNUT9OAan46sj3tQbNVwsmDlyCFrVqqR00YiI6DEx5FCpFbptGeoET4cPktDYzhEugS/DPfsWmrq7KV00IiKyAs6DpVIn/vYNHPmiH3yCJ6AiknDJpg6u99uI0d3awI6/EUREusGeHCpVQv9YitqHPoQvEpFtsUGI5wj4DPsMjk4uMBqNShePiIisiCGHSoXbyZn4YsPfmHnpbTgajIiyqYWsnvMR5NNR6aIREVExYcgh3e8YviXsOmb8Eo6ENBPc7AbiyToO8H7xU9l7Q0RE+sWQQ7oVc+0Srq0aix/juyLB0lAOKO79wmdo5lFO6aIREVEJYMghXe45FbJ5PhqfmAVvpOFTh2vY1WE9RndqAHvuOUVEVGow5JCu3Ig+h9s/jYF/xlF5fN6uIZz7L8L4Jg2VLhoREZUwhhzSBbPJhJANX6B5+BdwN2Qgw2KPMK9x8B04jXtOERGVUgw5pHnRcalYt/JbvB3/KWAAztg3RdkB3yDQq5XSRSMiIgUx5JBmmcwWLDsQhTnbzyLD2BDejr4o2/hJ+A94Bza2tkoXj4iIFMaQQ5oUfS4M1zZOw5eJI5EBFwTVq4yG/baiVuUySheNiIhUgiGHNCXbmIWQnz6Gz4VFqG0w4l3HsjB0n4PB/jVhMBiULh4REakIQw5pxqXww8jeNBZB2RFy7M0JJ188NeQTVK9VS+miERGRCjHkkOoZszJxdOV0+ER/DweDCUkog7Ot3oVf73Ew2HBHTSIiKhxDDqnaiasJOLNiMgZmrpe9N8dc2sLzxUXw96ijdNGIiEjlGHJIldKzTJi74xx+2H8J5S1Pw8/pIO74TkSb7qPYe0NERA+FIYdU59S+LQjfsx7fpQySx+1bN0a5HkdRz9VZ6aIREZGGaHJAw4wZM+RMmry36tWrK10sekyJd27j8LzBaL5rOAZmb8WAsiewZKQvvhrkjUoMOEREVFp6cpo1a4adO3fmHtty8TdNC922DLWCP4Q/EuTxocr98MHw11HWrYLSRSMiIo3SbMixs7Nj740OxF6PxpVVY+GTul8eR9t4Iv2ZLxEQ0FXpohERkcZpNuRERETAw8MDjo6OCAgIwGeffYZ69erd8/mZmZnyliMpKUl+NRqN8mZNOa9n7ddVA2vVzWKx4OcjV+HzZ194IwpGiy1CagxDy0Ez4ehcRpFzp+d203v99Fw3vddPz3XTe/2MCtWtKD/PYBGfNhrzxx9/IC0tDQ0bNsStW7fwySef4OzZswgPD0elSpXuOY5n5syZd92/evVquLi4lECpKUdsBrDmgg0ikmzQweY43nH4GSdrvwynilzUj4iI7k98/g8ZMgSJiYlwc3PTX8gpKDU1FfXr18eUKVMwadKkh+7JqVmzJmJjYx94kh4lZe7YsQNdunSBvb099ORx6ia2ZDi2fjZ+jUzH6qyOcLK3wcSnGmBEgKe8/Kg0Pbeb3uun57rpvX56rpve62dUqG7i87ty5coPFXKU/2SxgjJlyqBFixbyEta9iMta4laQaJjiapzifG2lFbVuF04Gw7xlPNpmR6CFwRnxdTrhnRc6oHYl9W2oqed203v99Fw3vddPz3XTe/3sS7huRflZugg5oofmzJkzaN++vdJFoQIy0lNxbOV78L26AvZySwYXnGs5FQv7duOifkREVKw0GXLeeust9OrVC7Vq1UJMTIwckyO6r0aMGKF00SiPM4e2ocz2SQgyX5NbMoSW6YBaQ+fDz6M2zxMRERU7TYacq1evYvDgwXI8TZUqVRAYGIjg4GDUrs0PTzVIzjDim617MTF8iOy9iUV5XA76BD7PDFO6aEREVIpoMuSsWbNG6SLQPew8fQvTt5zCjcQMuNl1g3cVAxoN/wo+FSrznBERUYnSZMgh9Ym9HoWo1RPx77hnccPiiVoVXdCi71fw96qidNGIiKiUYsihx2I2mRCyYS6anv4CvkjHx/Y3sTtoGSY85QVnB1ueXSIiUgxDDj2yqDNHkLHxDQQYT8vj83YNUaX3PLzTojHPKhERKY4hh4rMlJ2FkGVT4Ht1ORwMJqRanHCy8QT4vTAFtipY1I+IiEjgJxIVyaFLd3Du5AH0wxI5LfyYSxDcB89HYM0GPJNERKQqDDn0UBJSM/HZH2ex7shV2KIz2jsfh0vACHh3HcZF/YiISJUYcui+LGYzjv7+PQxHlmJLxhQADgisZkCTV35FJTdubEpEROrFkEP3dP3SWcSuHQ/fjBB5PKHcPvi88A5uhR+Em7M+92AhIiL9YMihQncLP7L2U7SMWAQPQyayLHY4WmcURg2ZCYONLX4P50kjIiL1Y8ihfCKP7QV+nYhA0wU5sDjcoQVcn5+PoIat5eNGo5FnjIiINIEhh6TUzGzM3XEeTxyajs62F5CIMjjfcip8+7zBgcVERKRJDDmE3aevY9rWs7iWkI7thpH4osIfaDB0LvyqefLsEBGRZjHklGKx16MRvfpNXE8w4Fr2q/Cs4IxP+vRAQKOXlC4aERHRY2PIKYVM2dk4suELND39JdoY0tHS1hbxbd7ASz06wMWBbwkiItIHfqKVMpHH/4H5l4kIyD4vBxZH2HnB0GsexrV6QumiERERWRVDTimRkpyAUyunwu/mWtgaLEi2OON004nw7f8W95siIiJdYsjROYvFgu3ht/Dl1mCszvxNBpyjrp1Ra/A8BHjUUbp4RERExYYhR8euX43CBztjsPPsbQD2+MJtHAYFeaFN5+eVLhoREVGxY8jRIWNWJo6KFYsjv4GjcTTsbYMwukN9jH+yG5zsbZUuHhERUYlgyNGZsyE74fjHZASao+TA4qFuYZj40hR4VXNVumhEREQliiFHJxLv3MbZlZMQcGerPI6HKyJaTUFQ7/FcsZiIiEolhhwdDCw+tG0VvA69hwAkyvsOl+8Or6Fz4V/FXeniERERKYYhR8Mu3k7B9C2nYL54HT85JCLapiZSu8yBf9CzSheNiIhIcQw5GpSRnorNv/+BD0LLIMtkhqNdC/ze7As8/dyLcHB0Urp4REREqsCQozGn9m+F266p6GWOw1em/8CrYWN83LsZalcqo3TRiIiIVIUhRyNib17BpdX/gl/Sjv8dG8rji67lEdTZDwaDQeniERERqQ5DjkY202xyZh78kAazxYCQKv3Q5MU5aFu+ktLFIyIiUi2GHBU7Hh0HhxXdEZB9Vh5H2taHucdcBPh0UrpoREREqseQo0IJaVmYs/0cVh++jLdtG6KG3WWcaTKBm2kSEREVAUOOiphNZhzZugBfnnTAwTRPed/lZmOR1fkzBFSvqXTxiIiINIUhRyUuhR9CxuZ/wd8YjinmBpha5T/4qG9LBNbjuBsiIqJHwZCjsJSkeJxa9S58b66FncGMNIsjMht0x2+D28He3kHp4hEREWkWQ45CLGYzQrctQ83DHyMQd+RmmsfKtIfHwC8RWMtLqWIRERHpBkOOQtsxbF2zGBPjZsrjq4bquNPhE3h3fkGJ4hAREekSQ04JyjCasODvSHy75yKyTV54wrERjLU7wnvwDHg6c8ViIiIia2LIKSFhu9bA9M98fJc2CVlwRMeG1VDluV2oXdm1pIpARERUqjDkFLMb0edwc+1EeKcdkMcTyuxE3b7T8Uyz6tyOgYiIqBgx5BTjdgwhKz+Ad9T3cDdkwWixxVGPQRg+5DOUcS1fXD+WiIiI/g9DTjE4c+BXeJ98H3VxXc6aOu3QAmX6zkNgE9/i+HFERERUCIYcK5v1+xm0OfhftLS9jjiUwyWfd9Gm52gYbGys/aOIiIjoPhhyrMy/bkV8uG84zM6V4T/qK/hWdbf2jyAiIqKHwJBjZU81qYbaE/oi/FAFuFaobO2XJyIioofEayjFoHYll+J4WSIiIioChhwiIiLSJYYcIiIi0iWGHCIiItIlhhwiIiLSJYYcIiIi0iWGHCIiItIlTYechQsXom7dunByckKbNm2wb98+pYtEREREKqHZkLN27VpMnDgR06ZNw7Fjx9C+fXs8++yzuHz5stJFIyIiIhXQ7IrHc+fOxSuvvIJRo0bJ43nz5mH79u1YtGgRZs2addfzMzMz5S1HUlKS/Go0GuXNmnJez9qvqwasm3ax7bSLbaddbDvrK8pnq8FisVigMVlZWXBxccHPP/+Mvn375t4/YcIEhIWFYc+ePXd9z4wZMzBz5sy77l+9erV8LSIiIlK/tLQ0DBkyBImJiXBzc9NfT05sbCxMJhOqVauW735xfPPmzUK/591338WkSZPy9eTUrFkTXbt2feBJepSUuWPHDnTp0gX29vbQE9ZNu9h22sW20y62nfXlXIl5GJoMOTkMBkO+Y9EpVfC+HI6OjvJWkAghxRVEivO1lca6aRfbTrvYdtrFtrOeonyuanLgceXKlWFra3tXr01MTMxdvTtERERUOmmyJ8fBwUFOGReXhPKOyRHHvXv3fqjXyBmKVJRur6J0T4prhuK19daTw7ppF9tOu9h22sW2s76cz+2HGVKsyZAjiPE1w4YNg6+vL4KCgrB48WI5ffz1119/qO9PTk6WX8W4HCIiItIW8Tlerlw5fYacgQMHIi4uDh999BFu3LiB5s2b4/fff0ft2rUf6vs9PDxw5coVuLq63nMcz6PKGdQsXt/ag5qVxrppF9tOu9h22sW2sz7RgyMCjvgcfxDNhhxh7Nix8vYobGxs4OnpieIkAo7eQk4O1k272HbaxbbTLraddT2oB0fTA4+JiIiIHoQhh4iIiHSJIacYiPV4Pvzww0LX5dE61k272HbaxbbTLradsjS5rQMRERHRg7Anh4iIiHSJIYeIiIh0iSGHiIiIdIkhh4iIiHSJIcfKFi5ciLp168LJyUnur7Vv3z6o3axZs+Dn5ydXf65atSr69OmDc+fO5XvOyJEj5crQeW+BgYH5npOZmYk33nhDbqBapkwZPPfcc7h69SqUNGPGjLvKXb169dzHxbh78RyxcqazszM6deqE8PBw1dcrR506de6qn7iNGzdOc+22d+9e9OrVS7aFKOfmzZvzPW6ttoqPj5dbwojFxMRN/DshIUHR+on9jaZOnYoWLVrIcovnDB8+HNevX8/3GqLOBdtz0KBBitfvQW1nrfehGttOKOx3UNzmzJmj6rab9RB/+7X+e8eQY0Vr167FxIkTMW3aNBw7dgzt27fHs88+K/fUUrM9e/bID8Xg4GC5yWl2dja6du2K1NTUfM/r1q2b3EIj5ya20chL1H3Tpk1Ys2YN9u/fj5SUFPTs2RMmkwlKatasWb5ynzx5Mvex2bNnY+7cuZg/fz5CQkJkAOrSpUvu3mZqrpcgypy3bqL9hBdeeEFz7Sbeb61atZJtURhrtdWQIUMQFhaGbdu2yZv4t/iDq2T9xIa+oaGhmD59uvy6ceNGnD9/Xn5YFPTqq6/ma89vv/023+NK1O9BbWet96Ea207IWy9xW7JkiQwx/fv3V3Xb7XmIv/1a/70TKY2sxN/f3/L666/nu69x48aWd955R1PnOCYmRiwrYNmzZ0/ufSNGjLD07t37nt+TkJBgsbe3t6xZsyb3vmvXrllsbGws27Ztsyjlww8/tLRq1arQx8xms6V69eqWzz//PPe+jIwMS7ly5SzffPONqut1LxMmTLDUr19f1k3L7Sbef5s2bbJ6W50+fVq+dnBwcO5zDh48KO87e/asYvUrzOHDh+XzoqOjc+/r2LGjbON7UUP9CqubNd6Haqjbw7adqOuTTz6Z7z4ttF1Mgb/9evi9Y0+OlWRlZeHo0aMyBecljg8cOAAtSUxMlF8rVqyY7/7du3fLLs2GDRvK/5HExMTkPibqLrrc89ZfdG+KjVOVrn9ERIQsi7iMKLqHL168KO+/dOkSbt68ma/MYuGujh075pZZzfUq7D24cuVKvPzyy/k2ndVqu+VlrbY6ePCg7CoPCAjIfY64bCLuU1N9c34PRTuWL18+3/2rVq2SlwVED+Vbb72V73/Uaq7f474P1Vy3vG7duoXffvsNr7zyyl2Pqb3tEgv87dfD752mN+hUk9jYWNk1V61atXz3i2PxJtEK8R+VSZMm4YknnpBv0hzispu4BCJ2eRdvfNGt/uSTT8o3uHjTizo6ODigQoUKqqq/+KX68ccf5R9W8cfnk08+Qdu2beU15ZxyFdZm0dHR8t9qrVdhxDgBcY1bjH/QersVZK22El/FB21B4j411TcjIwPvvPOO7OLPu8nv0KFDZVgXlwxOnTqFd999F8ePH8+9TKnW+lnjfajWuhW0fPlyOcalX79++e5Xe9tZCvnbr4ffO4YcK8v7P+icN07B+9Rs/PjxOHHihLyumtfAgQNz/y1+AXx9feUfLPE/loK/zGqqv/jjmkMM6gwKCkL9+vXlH6KcgY+P0mZK16swP/zwg6yv+F+U1tvtXqzRVoU9X031Ff8rFj2OZrNZTmTIS/SA5G1PLy8v2aZiHI+Pj49q62et96Ea61aQGI8jAo2YfKKltht/j7/9Wv+94+UqKxFdkLa2tnelUtElWzAFq5UYHb9161b8/fff8PT0vO9z3d3d5R8pcSlIEP87EZdLxAh6NddfjPwXYUeUO2eW1f3aTCv1Ev+r2rlzJ0aNGqXLdrNWW4nniB69gm7fvq2K+oqAM2DAANnbIf6Hn7cXpzDiw9He3j5fe6q5fo/zPtRC3cRsWjE76UG/h2pruzfu8bdfD793DDlWIrrrxJTxnK7HHOJYXB5RM5GmRYoXMzr++usv2aX6IHFxcbhy5Yr8YyWIuotf2Lz1F7MHRLesmuovpjqeOXNGljun6zhvmcUvq5hxkFNmrdRr6dKlsuu3R48eumw3a7WV6MkT4w4OHz6c+5xDhw7J+5Sub07AER96IrBWqlTpgd8jLruK78tpTzXX73Hfh1qom+hNFXURM7G00HaWB/zt18XvXbEOay5lxOhyMcr8hx9+kKPJJ06caClTpowlKirKomZjxoyRo+V3795tuXHjRu4tLS1NPp6cnGyZPHmy5cCBA5ZLly5Z/v77b0tQUJClRo0alqSkpNzXETPLPD09LTt37rSEhobK2QViZlN2drZidRPlFvW6ePGiHNnfs2dPi6ura26biFkDou4bN260nDx50jJ48GCLu7u76uuVl8lkstSqVcsyderUfPdrrd1EeY8dOyZv4k/T3Llz5b9zZhdZq626detmadmypZzdIW4tWrSQ7wsl62c0Gi3PPfecLHtYWFi+38PMzEz5/ZGRkZaZM2daQkJCZHv+9ttvcvamt7e34vW7X92s+T5UY9vlSExMtLi4uFgWLVp01/erte3GPOBvvx5+7xhyrGzBggWW2rVrWxwcHCw+Pj75pmGrlfilLey2dOlS+bh4w3ft2tVSpUoVGeLEB6qYEnr58uV8r5Oenm4ZP368pWLFihZnZ2f5Bi74nJI2cOBA+Qspyu3h4WHp16+fJTw8PPdxMUVSTDMX0yQdHR0tHTp0kL/Iaq9XXtu3b5ftde7cuXz3a63dxIdfYe9DUWZrtlVcXJxl6NChMuyKm/h3fHy8ovUTH3z3+j0U3yeIeog6i7qJvy9iqYA333xT1kfp+t2vbtZ8H6qx7XJ8++23stxiSnVBam07POBvvx5+7wz/V1EiIiIiXeGYHCIiItIlhhwiIiLSJYYcIiIi0iWGHCIiItIlhhwiIiLSJYYcIiIi0iWGHCIiItIlhhwiIiLSJYYcIiIi0iWGHCIiItIlhhwi0iSxi7OdnR3+/PNPq7ze+vXr4ejoiIsXL1rl9YhIedy7iohUa/ny5UhISMCECRPueqxbt25ISUnB/v37892flJSE8uXLi82H4ePjg6NHj971vampqWjcuDGuXr0qg01ycrIMTN7e3mjQoIEMPESkfezJISLVmjp1aqE9NcHBwdi+fTsmTpx412OhoaEy4Dg7O+P06dMwmUx3PWfWrFmIiYmR/27evDns7e1hMBjk623YsEH2EhGR9jHkEJEqRUVF4datW/D397/rsUWLFsneml69ehUacoR+/fohIyMDkZGR+R6/dOkSvvjiC/Tt21cet2nTJvex/v37w8XFRb4+EWkfQw4RqU6fPn1Qt25d+e8ZM2bIXhZxe/vtt5GdnY2NGzfiqaeekpeaCsq5PPXyyy/LrydPnsz3+OTJk1GxYkV5uUsQl7RyuLq6on379li3bp3sDSIibbNTugBERAW99tpr8jLTr7/+ivnz56NcuXLyftGrI3pqxFicgICAQk+ceNzT0xMdOnSAk5MTTp06heeff14+tmvXLmzatAk//vgjzpw5c1dPjhAUFCQvhYlLVuJSFhFpF3tyiEh1unfvDhsbG1SoUAHjxo3Diy++KG8NGzbMHS9Tv379u75PhJ/z58/L3hkxkLhZs2a5PTmiB0gMYA4MDJSvJXp8xFicFi1a5HuNnNfluBwi7WNPDhGpkuiREbOdCrp9+7b8Ki45FRQWFgaz2Zx7Cap169bYu3ev/PfChQvlQORDhw7JS1/i9UUIKnjJq1KlSvJrzsBkItIu9uQQkerExsbK6d15x8vkEAFFKGzMTM54nJxLUCLkXLhwAVeuXJFje0aOHAk/Pz9cvnxZ/ozCXj/ndXN+DhFpF0MOEalOTlgpLIRUqVJFfo2Pj7/nzKq8PTmiZ2fgwIFyjI+YOp739QuOxxHu3LmT7+cQkXbxchURqc6xY8fuGXJyBgMXnBqeE16qVasGDw8PedyqVSvZI3Pw4EHMmTNHPlZYGMor53U56JhI+xhyiEh1crZWyJlGnpcYp+Pm5obDhw/nuz89PR1nz57FM888k29K+OzZs+V6OXlXTRZhyNbWVoagwhYarFy5Mpo2bWrlWhFRSWPIISLVqVevnvz65ptvom3btjKQDB48WM64Ev8WC/1t2bIFmZmZuQOHxaBjcUmqYO/MW2+9ddfri56cJk2ayFWR8xLbO+zbtw8jRozgmBwiHeCYHCJSHRFuhg0bJrdYEIFjypQpMuDkGDNmjByTI9bRyXG/S1B5Xbt2Ta6kXNjzxM9LS0uTr09E2scNOolIk8SKxWKjTdHzYg05G3qKXiQRdohI+9iTQ0SaJPafEgOKC9vA81GIrSLEOjpiDA8R6QN7coiIiEiX2JNDREREusSQQ0RERLrEkENERES6xJBDREREusSQQ0RERLrEkENERES6xJBDREREusSQQ0RERLrEkENERES6xJBDRERE0KP/By1oK8n34QNJAAAAAElFTkSuQmCC", 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iotKVnafD5B+icDavK7K8O+Ddvs+YxSlnGDLwJbOH6bMijs3Pz5dzITEMERGRsftgWzTOJmWgkrMdpjzfERoz6eJhHn8FERERlaqoPzZiYOQgNNHE4cPn/FChnK3ZnHGGISIiInqgm0lXUP2PKahvlYC3qx9F2/qVYE4YhoiIiOi+FL0eF756Ce64hYtWXvAbsQTmhmGIiIiI7uvw5mVokbkfuYo1dL1Xwd7xf4uhmwuGISIiIipW/Nnj8Dn2nrx9tN541PFtBXPEMERERET3yMvNQeb6kSinycEJW18EDHwH5ophiIiIiO6xctffuJ5jgzQ4wv2FL2FlxjP8m+9fRkRERI8k7GwyPtp/HVBm4qvO5dG2hrdZn0mGISIiIip0IzVDzjKtKMDAwJpo+4TpL7fxb3iZjIiIiAqH0V/69FlMzvoETSpp8XaPJrAEbBkiIiIi6eD69xGSdQBNrG0Q3HkGHGxLvsSUKWPLEBEREeFM1J9oGbtInomoRlNRxyfIYs4KwxAREZGFS0lOhNNPI2CrycffjiEI6D8DloRhiIiIyILp8vMRv3oQquI6EjQeqPXSN2azGn1JWdZfS0REREUcWvMafHMikKXYIu/Zr+HqZl6LsJYEwxAREZGF2nbsKj6Lq4xbSjmcCngPtZpYTj+hO3E0GRERkQWKuJiCVzdEIVfvh8+bb8RrPUJgqRiGiIiILEzC+ZN479vDyM2vhA6NquDVZ1rCkvEyGRERkQVJvZkE3bfPYo1uJp6tnIClA5vB2koDS8YwREREZCEyM1KRsLI3augTkKVxxIznO8HRlheJGIaIiIgsQHZWJs4t643GeSeQhnLIem4d3D1rqV0so8AwREREZOby8nJxatmzaJoTiUzFDld7fI3aFjpyrDgMQ0RERGZMr9MhatkgNM/8CzmKFnEdVqOBfwe1i2VUGIaIiIjMlKIomPNTJHJTriBPsUbMk8vQ5MleahfL6LDXFBERkZkGofnbY7A2PAnrNdOwpj3QqkMftYtllBiGiIiIzNB/N67HqiPOADSY27clWgXUULtIRothiIiIyMwc+Pot9D+/FDdsesK+y1wMYBB6IIYhIiIiM3Lw+/kIOb9U3m7hXQNBT9RRu0hGjx2oiYiIzMShjUsQHPu+vH2w+kgEDZundpFMAsMQERGRGQj/+VMEHJstbx/yGIigkYvULpLJYBgiIiIycZGh36L5kZmw0ig4XLE3AkevgMaKX/ElxTNFRERkwg7H3cSWPyNgBQXh5bvAf9yXDEIPiR2oiYiITFRsYjpeXBuOtLwOcKzVFFNHvQAra2u1i2VyGIaIiIhM0NX4c5j4dTTSsrVoWdMNr4zsAhstg9Cj4GUyIiIiE5N64xpy1/TC8pw3EOKehS+G+cPBlkHoUTEMERERmZCs2xm48mlv1NTHo7xVFpb090N5R1u1i2XSGIaIiIhMhC4/HzGf9EejvJNIgyMy+/+AKjW81S6WyWMYIiIiMgGKXo/wT19C88y/kKvYIKHzl6jVOFDtYpkFhiEiIiITcPDb2QhO3iRvnwj+EI1CuqpdJLPBMERERGTkfjlyGtXPrpO3D3lPRfOuI9Quklnh0HoiIiIjFnYuGa9uPgs33Wz8p14sOg1+W+0imR2GISIiIiMVezkJY74+ijydggDfxujw/GC1i2SWeJmMiIjICF0+dwIun4fgqbx9CKxVAYue84OVlUbtYpklkw5D+fn5ePPNN1G7dm04ODigTp06mDt3LvR6feExiqJg9uzZ8PT0lMe0a9cOJ06cULXcRERED5IYfw7W3/RCVSRjkv02rHrBD/acXbrUmHQY+uCDD/Dpp59i+fLlOHXqFBYsWICFCxdi2bJlhceIfYsXL5bHhIeHw8PDAx07dkR6erqqZSciIipOSlICcr/siaq4jniNJ1xHb0V5J0eerFJk0mHowIED6NWrF7p3745atWrh2WefRadOnXDkyJHCVqElS5Zg1qxZ6Nu3L3x8fLB27VpkZmZi3bp/euUTEREZi7ysNKR/0Rs1lAQkohK0I7bC3aOG2sUyeybdgfqJJ56QLUOnT59G/fr18ffff2P//v0yAAlxcXFITEyUAamAnZ0d2rZti7CwMIwZM6bY183JyZFbgbS0NPkzLy9PboZS8FqGfE1iPZgivhfUxzpQ37X482gRMx91kIAbKI+c53+EZ9Va/I54DCX9fjXpMPT6668jNTUVDRs2hLW1NXQ6Hd577z0MHDhQPi6CkFClSpUizxP3L168eN/XnT9/PubMmXPP/tDQUDg6Gr6pcteuXQZ/TXp4rAf1sQ7UxzpQx80cIOPkr5iIBFxT3LC3zgzYxV5EVOz9v6vo34krQWYfhn744Qd8++238pJXkyZNEBUVhcmTJ8vO0sOGDSs8TqMp2vteXD67e9+dZs6ciSlTphRpGfLy8pItTC4uLgZNrOKDR/Rh0mq1BntdYj2YGr4X1Mc6UM/FG5kY9tURJGT3hNYuF52ffwV96vmoWCLzUXBlx6zD0LRp0zBjxgw8//zz8n7Tpk1li49o2RFhSHSWLmghqlq1auHzkpKS7mktupO4lCa2u4nAUhqhpbRel1gPpobvBfWxDsrWyYh9GPVLKq5mWaFWxXKwr9kb1ev58DvBQEr63Wpl6s1fVlZF/wRxuaxgaL0Yci8C0Z3Nvrm5udi7dy9atWpV5uUlIiIqELnjK9TZ2hdz8j+Cn6cT1o0KgNu9/w6nMmDSLUM9e/aUfYRq1KghL5MdPXpUDqMfOXKkfFxcChOXzebNmwdvb2+5idui38+gQYPULj4REVno6vMH181F0JklsNIoqOKkxfejWkBryySkFpMOQ2I+obfeegvjxo2Tl75EXyExQuztt/+3bsv06dORlZUlj0lJSUFQUJDsCO3s7Kxq2YmIyPLk5+XiyGejEZK8GdAAh9z7wv/l1bC2seGoMRWZdBgSgUYMoy8YSl8c0TokZqAWGxERkVpup9/CmRUDEJx1EHpFg8P1X0XQwLeguau7B5U9kw5DREREpiApLRuXlvWFf14EshUtToV8iOAuw9UuFv0/hiEiIqJSFJuYjhFrDqNyRi+stLuI1J6r0Ny/Pc+5EWEYIiIiKiUHT8bhpR9OIz0nH/bufsgdegQNK7vxfBsZXqgkIiIqBeE/LUfDH1qjZu5pBNaqgE3jWqEmg5BRYssQERGRoYfOf/U6Qi6tkiPGplWJQNCocbDXWvM8GymGISIiIgPR63QI/2Q4Qm5ulfcPeg7Fk6OWwMqaQciYMQwREREZgC4/H5HLBiModQd0igYRPrMQ/Nw0nlsTwDBERERkgMkUo5YORED6buQrVvg7YAECe7zE82oi2IGaiIjoMeTm6/Hq+gik37qOPMUax0I+QksGIZPCliEiIqJHlJOvw/jvjmL3qZv4w3oq1nSwhn/bHjyfJoZhiIiI6BFkZ2Zg3ecfYvcVf9jaWGPZkBD4N6jMc2mCGIaIiIgeUtbtdJxd2hMjc44iy7Yvmg1diNb13HkeTRTDEBER0UMuuHphWQ80zY1GpmKHdl37owmDkEljGCIiIiqhtNSbSFjeHU3yTiJdcUBC92/QJLAjz5+JYxgiIiIqgdSb13FtRTc0yj+NNJRDYq/v0bBFW547M8AwRERE9C9upmfh+idd0UB3BrfghOQ+G1DfrzXPm5ngPENEREQPkJSWjQGrD2NVVnskozxSntuEegxCZoUtQ0RERPdxOSUTgz8/hIs3MpHm0hGpQyehbnVPni8zw5YhIiKiYsSfjcaVpZ2ReeMKqrs54L9jWjEImSm2DBEREd3ldOQfqLh1KAKRikVO38D75c2o6urA82SmGIaIiIjuEBn6LRr99SocNLk4Z10HjV9cDXcGIbPGMERERPT/Dn4/D4ExC2ClUXDMPgB1xv0XTi5uPD9mjmGIiIgsXk52JqJWjUHwza2ABjhc4Rm0GPsFbLS2Fn9uLAE7UBMRkUW7cisLQz8PQ/nkSOgVDQ7WeQUBE9YyCFkQtgwREZHFCjuXjInrjuLG7VxMc5iG99o6I7hdP7WLRWWMYYiIiCyOXqfDoXVzsDc2GTfyu6NxVRd8MuQpeFVwVLtopAKGISIisig3k64gfs0whGQdRqC1Bjbe7TFhYBc42FqrXTRSCcMQERFZjJMHtsN95zj44SayFS2ONZ2B1/r2gcaKXWgtGcMQERGZPZ1Oh8Nfz0LghU9hrVFwyaoadP3WILBJkNpFIyPAMERERGYtKS0L8St6IyT7oBw2H+7aGY1fXIVyzuXVLhoZCbYLEhGR2frrbDK6Lf0Lm9MbIVOxQ3iz9xDw6gYGISqCLUNERGSWo8U+33kI8/9MgaIA4ZX74NozLyOgXkO1i0ZGiGGIiIjMSmpKMuJWDUbn2+exXPkPugU0wjs9m3C0GN0XwxAREZmNC6eOwHrDEDRTriBHo8XytgradPVVu1hk5BiGiIjILBzduRYNwqbBUZODRFRCep81aNPsSbWLRSaAYYiIiEyaotfj0LfvIPj8Ujla7LhdM3iOWgfvytXULhqZCIYhIiIyWfk6PXZ+8Ta6X1km7x9y74eWYz7lIqv0UBiGiIjIJKVn52HCuqOIPt8EjW09cK3hEAQPelPtYpEJYhgiIiKTc+3GTQz/9gROXU2DvbY8zj4bio6+NdUuFpkoTrpIREQmJf7M39AtD4Jv0ha4O9nih9EhDEL0WBiGiIjIZJyO3Aun73rAU0nCONvt2DQ6AH5eXFaDHg/DEBERmYRjezej+pbn4IY0nLGuB6eXd6JGZQYhenzsM0REREbvyC+r4Bs+A7YaHaLtmqP2+M1wcnFTu1hkJhiGiIjIqB1c9x8En14o5xCKcH4aTSd8D1s7e7WLRWaEl8mIiMgo6fQK5v16CmEn4uT9Q5WeRfPJPzIIkcGxZYiIiIxOZm4+Jq2Pwq6T1wD0Qb0WbdGzzwvQWPHf8GR4DENERGRUkhLicGztFPyV9gJsbRzx4XN+eMbPU+1ikRljGCIiIqMRE74bFba9hA64ifkOCqoPX4OWNdlRmkoXwxARERnHYqvr56Fl7GJoNTpcsPJCwJD34ckgRGWAYYiIiFSVnnoTZ1YPR3DG3n9GjDm1Q4PRX3HoPJUZhiEiIlLN8aMHUH7rcLRQEpGnWCOy4VQEDpjJjtJUphiGiIiozGXn6bAoNBY/7r+IUNssJGrckdrzMwT5d2BtUJkz+TGKCQkJeOGFF1CxYkU4OjqiWbNmiIiIKHxcURTMnj0bnp6ecHBwQLt27XDixAlVy0xEZMlOHY9E94/3YfWfcUhRnPFd3Q/hOPkwGjAIkUpMOgylpKSgdevW0Gq12L59O06ePIlFixahfPn/rVWzYMECLF68GMuXL0d4eDg8PDzQsWNHpKenq1p2IiJLk5uTjQOrJ8P7v+3R5OYuVHK2wxfD/DF5aH+4lK+odvHIgpn0ZbIPPvgAXl5eWLNmTeG+WrVqFWkVWrJkCWbNmoW+ffvKfWvXrkWVKlWwbt06jBkzptjXzcnJkVuBtLQ0+TMvL09uhlLwWoZ8TWI9mCK+F8y/DuKOH4LNz+MRor8gO0k/Wykeb44MgZujLT8Dy6gOLFFeCc+lRhGJwUQ1btwYnTt3xuXLl7F3715Uq1YN48aNw0svvSQfP3/+POrWrYvIyEg0b9688Hm9evWSrUciGBVHXFabM2fOPftFgBKX4oiIqGT0unzYnN2GLrd/kkPmbyrO2F1pOOy8AngKqdRlZmZi0KBBSE1NhYuLi3mGIXv7fxbqmzJlCp577jkcPnwYkydPxmeffYahQ4ciLCxMXkYT/YpEn6ECo0ePxsWLF7Fz584StwyJFqjk5OQHnsxHSay7du2Sl+3EpT5SB+tBfawD86yDi7FRUDaPhbfujLwf6fgEqg9aDrcq1Q3y+uaG7wPDE9/f7u7u/xqGTPoymV6vh7+/P+bNmyfvi9Yf0Tl65cqVMgwV0Gg0RZ4n8t/d++5kZ2cnt7uJD4jSCC2l9brEejA1fC+YRx2IBVa/3B+H/bv2YK31GaShHE63eBste4zmkPkyqgP6R0nPo0mHoapVq8pLZXdq1KgRNm7cKG+LztJCYmKiPLZAUlKS7DdERESGFZ+UgqmbY3A47iaAJviy0qvo2W8Y/KvV5qkmo2XSo8nEJbDY2Ngi+06fPo2aNWvK27Vr15aBSDT9FsjNzZX9i1q1alXm5SUiMuflNA5vXAK7T5rjyoUYlLO1xvy+TTFi4juoxCBERs6kW4ZeffVVGWrEZbL+/fvLPkOrVq2SmyAuhYk+ROJxb29vuYnbohO06FBFRESPL/XmdZz/fCgCM8PkSLEZbnvhO2oFalTkgBOykDAk5vYR/XTEpScRPipVqgQfHx95uaq0BQQEYPPmzZg5cybmzp0rW4LEUPrBgwcXHjN9+nRkZWXJUWZiXqKgoCCEhobC2dm51MtHRGTuYo/8DpdfRqM5riNXsUGk9wR0ff4tWNuY9L+1ycI80v+tp06dkp2UN2zYgOvXr8t9BYPSCjomi97borVm7Nix9/TrMaQePXrI7X5EecRQebEREZEBV5n//l20PP2xHDJ/WeOB7D5fINjvCZ5iMu8wdOHCBdnSIjooi6UtnnzySYSEhMi5fMRyGCIQ3bx5E2fPnsXBgwflZIgrVqxAv3795EzQd06ISEREpikzNx9bVs/FwOsfy8tikU5t4f3iGlTnLNJkCWGoYcOG8vLXl19+KQOOk5PTA4/PyMjAjz/+iI8//lg+T1yuIiIi05VwKwsvrT2Cc1ebo7ZtY2ia9Ebgc9M4ZJ4sJwx99913MgSVlAhLw4cPl9umTZsepXxERGQkTkQdwIifU5F0Wwd3JydoX/gZLWu5q10sorIdWv8wQehuBWuDERGR6Qn/aTm8N/fAiJxv0aiqC7ZMeIJBiMzGY80z9Prrr8v+QUREZJ50+fk48OlYBETNgq0mH0Gut/Dj6EBUK++gdtGIjCMMLVy4UM7tQ0RE5ic99SaiF3VDSOI6ef9g9VFoNmULyjncu1wRkSkr1RmoxSrvYoFTIiIyLQnnTyH547ZolnUI2YoWEQGLEPziYlhZW6tdNCL15xlaunSpXN4iMDCwcEXY+9HpdLhy5crjlZCIiMrUoTNXUOO77qiNG0hCBaT2XouWzduwFshsPXQYKl++PI4cOYJt27bJCQ3Hjx+PN998U64Y36JFi8JNzAYdFhYmJ18kIiLTsOHIZbzz8yl0xUBMdAxF+REb4M21xcjMPXQYGjp0qNzEBIx16tSRsz9bWVkhMjISv/32W5FZqIUhQ4YYtsRERGRwOdlZ+PNMIn488M/XguLbD15934GDvS3PNpm9R148Rswm3atXL4wZMwZdunSR+27cuIGIiAgcPXoU58+fl2HplVdeMWR5iYjIwK5ciMXtbwZjWv517NXMxwvtA/BK+3pF/mFLZM4eayU9sUjqncSSHJ06dZIbEREZv6jf1qP2n1Pgidu4BSes7OoK/zbeaheLqExxWWEiIguUmZGK6DWvIOjGT/L+aZv6iKr9MvqE8B+zZHkeamj97t27H/kXPc5ziYjIcGLCd+PmoqDCIHSwcn9Um/w77Jw44IUs00OFoa5du6Jt27by8lheXt6/Hi+OEceK53Tr1u1xyklERI8pK1eHD3bEIHrrUlRXruIaKuJ4+68RPG41bO3seX7JYj3UZbKoqChMnTpVrlHm5uaG9u3bIygoCHXr1kWFChWgKApSUlLkEh1iZurff/9d3hd9iMRziYhIHX8cv4g3t53D5ZQsOGMIKleqjGZD3oePG1uDiB4qDDVp0gQ7duzAwYMHsXLlStnq8+OPP94z4kCEIhcXF7k469ixYxEQEMAzTUSkgsT4s7i6fhKs027hct5MVHV1wOxnWqJtk+dYH0SP04E6ODhYbl9++aWcX+jEiRO4fv26DEWVKlWCj4+PnIRRzD9ERERlLy83BxEb5sP3zAp4aHKQb2WFt/z1eL5nW5Sz49gZojs91jvC2tpatvqw5YeIyHjEHA6F3Y5pCNZfADRAjLYx7Hp/jFFN/llGiYiKeuymmwULFsjJFbVaLSpXriw7Sq9Zs6ZEHayJiMhwUlJu4vDHg9Dw1+dQW38BKXBGuO9c1J+xH7UZhIhKJwzNmTMHM2bMwLVr1+RlM7FC/d69ezFq1Cj4+fnhzJkzj/PyRERUAqKf5oYj8ei09ADcbvwzWOWwW3doxocjoO8krjRPZMjLZBs2bICvry/q168v+wOtXr0aISEhslO1s7OzPCYzMxMbN27EtGnT8NRTT8k+RaLFiIiIDC8u5ije2JOBAxfT5P3lFSdjbJtaCAzi5IlEpdIy9Pzzz8sRZU5OTvD390diYiLq1auH06dPIzc3Vx7j6OgoF2cV65OJf63Mnj37YX4FERGVcAbpA59NRPXv26Pp5e/goLXGG90a4sMpL6IhgxBR6bUM7dq1C8eOHZNbdHQ09Ho9vvnmG3z77bewsbGRQalFixYyKInRZCNHjsQXX3yBFStWPFypiIjovk4e2A7X0EkIUa7JDtLt3ZIw7MW2qFbegWeNqLTDkJhkUWwFxESLAwcORLNmzeRq9eKS2HfffSeH3N8595AIRSIoiSH3YqtWrdqjlJWIyKJl3U7H32unIDhpg7yfCHcktn4XQR0HqV00IssdWt+hQwfs27cPixYtwksvvST35efny3mHjhw5IvsYidak9evXIzs7Wz4uQpJOpzNM6YmILMSpI3vgvG0sgpWr8v5htx5oNHwZmrlWULtoRJYdhubOnSsviT355JOyM7VoIRKXy8RIMrH9/PPPsvUoKSlJLtEhLq2dPHnScKUnIjJzou/lF/vjsHXHSfzXJhlJmgpIbPchAtv1U7toRGbjscJQw4YNsWnTJgwYMAAtW7ZEgwYNZJ8hBwcH7N+/X3asHj16tBx5JkagiU2sa0ZERP8uPSsH0zcex/bjiQBqYVXt9zG0X2/4cj0xIoN67DnZxSKsp06dwsKFC+WQ+nXr1sn9IgANGjQIixcvNkQ5iYgsyvmTh6H8+CIuZ70ErXVdvNWjMYYEd7tnLUgienwGWaDGw8ND9hsSm1ij7MaNG/D09JSLtRIR0cM5snUlGke8A0dNDt61XwdlxK9oXsONp5GolBh8tT6xUKvYiIjo4eRkZyJq9VgE3fhJDpk/Zt8SNUd9B7dKDEJEpYlLFxMRGYGrF2OR/s0LCMo/Db2iwaEaLyJw2PuwtuHHNFFp47uMiEhlh44cRoNf+qAqMnALTrjU7mOEPPWs2sUishgMQ0REKtHpFXz82xks//0aVtt4o4ZdBpyGfAffmg1YJ0RliGGIiEgFyYnxmL71HH4/f1suExnWbD6e6O4HO3tH1gdRGWMYIiIqYyf+2oYqu8ahm84XB7TjMa+vD/o0r856IFIJwxARURnR5efj8DdvIvDCp7DWKPDXXsC2EU1Rx4vrNRKpiWGIiKgMXD57HOk/vISQvJNy2Hx4+a5o8uJncHRy5fknUhnDEBFRKVL0ehz+74doevJDVNfkIENxwKnmbyKg9wSedyIjwTBERFRKYhPTsXDLIcy78omcTfq4rR8qDl6NAI4WIzIqDENERAaWknwNH/2ZiG8PX4ZeATTaMRjVSEHggBmwsrbm+SYyMgxDREQGkpebg4gfP0Sj058gK28w9Eo7dGnigZnd2qFmxXI8z0RGimGIiMgA/t7zI8r/+Q6C9Zfl/ecdwtFn4OtoVded55fIyDEMERE9houxUbj10zT4ZR2W92/CBWeaTIZ/n0lcV4zIRDAMERE9gtTMPPz1/fvoeOkj1NTokKdY44hHfzQe+B6CylfkOSUyIQxDREQPIV+nx/fh8VgcGouaWS7oZqdDlEMwKvZdgBBvP55LIhPEMEREVELH92/Fnv1hWHTrSXnfvXIzHHliG/wDn+A5JDJhDENERP8i4fwpXPvxNbTI3A9vRYsdDk3Qv+OTGBxUAzbWVjx/RCaOYYiI6D6ybqcj6vu30SL+G1TT5CFfsUJU5V5Y93wnuFaswvNGZCYYhoiIillCI3LHWlQ7/B+EIFmuJRZt1xwuvRchqFFLni8iM8MwRER0h0s3MrHgxz+w8Mp0OGhycRWVkBjyFpp1HAKNFS+JEZkjs3pnz58/HxqNBpMnTy7cpygKZs+eDU9PTzg4OKBdu3Y4ceKEquUkIuOj0+nxxf44dF6yD7/EKVimfw4HvF5C+WlH0bzzMAYhIjNmNmEoPDwcq1atgq+vb5H9CxYswOLFi7F8+XJ5jIeHBzp27Ij09HTVykpExuViTCROv98K27b9hKw8HYLrVED/SQsRMupDOJRzVrt4RFTKzCIMZWRkYPDgwVi9ejXc3NyKtAotWbIEs2bNQt++feHj44O1a9ciMzMT69atU7XMRGQcfYMO/7gYlb/vjEZ5p/CO7XeY19sH614MRi13riVGZCnMos/Q+PHj0b17d3To0AH/+c9/CvfHxcUhMTERnTp1KtxnZ2eHtm3bIiwsDGPGjCn29XJycuRWIC0tTf7My8uTm6EUvJYhX5NYD6ZIjfdCRuoNnP9qDAIz/pAdpI/ZtYT7wJV4rpondLp86HSwKPw8Uh/rwPBK+pli8mFo/fr1iIyMlJfA7iaCkFClStEhsOL+xYsXH9j3aM6cOffsDw0NhaOjIwxt165dBn9NenisB8upg+zr59A6fgVaaq7LZTS2OT0HpV4XxP0dA4jNgvF9oD7WgeGIK0FmH4bi4+MxadIkGVLs7e3ve5zoVH0ncfns7n13mjlzJqZMmVKkZcjLy0u2MLm4uBg0sYr/6UUfJq1Wa7DXJdaDqSmr94Jer2Drzh3oG/8faDU6XNFUxq2uK9C9RTtYOn4eqY91YHgFV3bMOgxFREQgKSkJLVv+b94PnU6Hffv2yQ7TsbGxhS1EVatWLTxGPOfu1qI7iUtpYrub+JAujQ/q0npdYj2YmtJ8LyRn5GDqhr+x97QVKmh94ebqgrovroEnF1UtszqgkmEdGE5J/1826TDUvn17REdHF9k3YsQINGzYEK+//jrq1KkjR4+Jf3E2b95cPp6bm4u9e/figw8+UKnURFTWjv/1Kybs0eFChhXsbKyR3HUVng6ux+HyRGT6YcjZ2VmOELtTuXLlULFixcL9Ys6hefPmwdvbW27ituj3M2jQIJVKTURlJS83B0fWTkfQ5bWYqH8Cn1aehuWDWqCBB4fLE5GZhKGSmD59OrKysjBu3DikpKQgKChI9jESQYqIzNeVuBikfzcMIfkxcrSYl7srto4JhoP9vZfAiciymV0Y+uOPP4rcFx2lxQzUYiMiyxCx7XPUP/wmPDVZSIMjzgT+B4HdRqldLCIyUmYXhojIcqXeTMLpteMRkBoqW4NibBrB5YW1aFmrgdpFIyIjZhYzUBMR7TyRiD6f/IVatw5Bp2hwoNoI1Ht9HzwZhIjoX7BliIhM2vVrlzH3tyT8fOwqAFssdJuCke2bIsS/vdpFIyITwTBERCY7Uiziv+/D5/RK2OQNh7VVG4xuUweT2neBvdZa7eIRkQlhGCIik3P8zy1w3jMLwfp42TdooFMERgydAd/q5dUuGhGZIIYhIjIZ56IPIuPXt+GXdUjevwkXnG06Ff69J8LKmq1BRPRoGIaIyOhdSL6N6PXvoGfyank/X7FCROW+aDjoAwS6uatdPCIycQxDRGS0rqVl4+PfzmBDeDz8FE/0tAMinJ9G5V5zEVSvqdrFIyIzwTBEREYn9cY1nPxxLo4kZGNdbl+5z7nBEzgdvA8tG/mpXTwiMjMMQ0RkNDLSUhCz5UP4XPwaIZosNNPY4qhXP7zcLRiBtSuoXTwiMlMMQ0SkuuzMDChnfgWOjkcI0uUIsfNWtZD+xEx80a4LV5cnolLFMEREqsnN12NP6Ga0ODwVvZEi98VrPJHkPxXNu4zgCDEiKhMMQ0RU5hRFwfbjiXh/ewxyb97GXrsMXIE7LvlOhP8z4+CltWWtEFGZYRgiojJ15ug+RPy2ATOSu8r7lZw9sdv3U+TmAj2e6QUbrZY1QkRlimGIiMpEcuIlxH0/Va4o7w1gq7Yu/Nt0x8tt60CrUfDrr7+yJohIFQxDRFSq9Dodwjd9hEYnFiEAmXJfuGsnfPTcM6hSva68n5eXx1ogItUwDBFRqblw6giyNk1EUN5Jef+MjTeUbosQ0KItzzoRGQ2GISIyOJ1eweo9J9FnXz/U0tzCbcUe0Q0mIqD/DFjb8GOHiIwLP5WIyKDib2bi1R+icORiCo5ajcDL5Q+h6sBlCPaqxzNNREaJYYiIDELR63Fk6wp8ffQWjuQ0h5OdDTo98yKatXgHGo2GZ5mIjBbDEBE9tlvJiTj/1UsIyNiHenBCttdneHvgk/Cq4MizS0RGj2GIiB5L9L7NqPL7FLTATeQp1oipMxyfDu7CvkFEZDIYhojokWRn3UbUmlcRnPSDvH/Rqjpyn/kUIc2e5BklIpPCMEREDy3mUiJsv+qMYP0Fef+Qe1/4jlgKh3LOPJtEZHIYhoioxPR6BV/+FYcFO2LxjqYuXGxuIaHNQgQ93Z9nkYhMFsMQEZXI5bPH8UHoWfx86Z+1w/5qMBldutWBX5XqPINEZNIYhojogfLzcnHk+3fR7NxKPK+vj93at/Bmj8YYFFiDQ+aJyCwwDBHRA1eY1/wyGcG6c4AGcHW0Reiw5vDy9OBZIyKzwTBERPdIvnIRcRteR8Ct7fJ+Gsohxm8GAnpNgMbKimeMiMwKwxARFcrO0+Hn7dvQLeIlBGiy5b4jLh1Ra9BiBHrU4JkiIrPEMEREyM3X478R8Vj++1lcT1XQ3LYC9Fon6Lu8D3//9jxDRGTWGIaILLxzdOQvn0Eb/T3mZE5HLrSo6uqE022/Q5cgP1hZW6tdRCKiUscwRGSBdPn5OLr9C1SJXIJA5YrcN9wxDFXbj8XAwBqw1zIEEZHlYBgisiC5Odn4e9tnqBq9Ev7KVbkvBc6IrTsSr/Z5DQ5OLmoXkYiozDEMEVlIx+ifDpxA29/7IgDXC0NQTK0X0LTv6wh2cVO7iEREqmEYIjJjt7OysS78Clb9eR7X03OwRusJrXUeztYbgaa9JiPEubzaRSQiUh3DEJEZSk1JxsktH6LOhR/wefZcXEcFVCvvgBtBC+EU7INgh3JqF5GIyGgwDBGZkYy0FERv+gBNLqxFCDLlvped96NcpzfRu3k12NpwwkQiorsxDBGZgeys24jatAj1z6xGCNLkvgtWXkhuPhFDuoyAjdZW7SISERkthiEiE6bTK9gYfh4h27sjGP+MDrusqYrEFq+ieddRqGXDtzgR0b/hJyWRiQq/cBNzfj6B4wlpmGXTHD1t8nCp6QQ07zkO1W3t1C4eEZHJYBgiMjHXLp9D/A/TMCf5KRxX6sDZzgbatm/ArVVdeNg7ql08IiKTwzBEZCIUvR7hm5ei8bH34a/JwtvaBGxu9jmmdqoPdye2BBERPSqGISITaQ269u0YBGaHAxog1qYhKvRcjPl+TdUuGhGRyWMYIjL21qAty9Hw7/nwRSZyFC2Oeo9HwPNvwZqdo4mIDIJhiMhIJaZmY+M3yzE++V15P9amAeyfW4XgBs3ULhoRkVlhGCIyMoqiYGNkghwpdju7AVrYNoGmXnsEDHob1jZatYtHRGR2GIaIjMj1KxcQ/f1bmHW9L3JgCz+vCnDvtx3eHq5qF42IyGwxDBEZSd+giF8+Q/3Id/E0bmOaVo/c9nMw+sk6sLHmEhpERKWJYYjICEaKJX43Fv5Zh+T9Mzbe6DDgVdRqVE/tohERWQSGISI1R4ptWoJG0Qvgp8lCrmKDyNpj4D94NtcSIyIqQwxDRCo4fz0DUV9PQ9/0df8/b1AD2PdbieBGLVkfRERlzKQ7I8yfPx8BAQFwdnZG5cqV0bt3b8TGxt4zMmf27Nnw9PSEg4MD2rVrhxMnTqhWZrJsGTn5mL/9FDov2YeF10NwXXHFQe8pqDcjDDUZhIiIVGHSYWjv3r0YP348Dh48iF27diE/Px+dOnXC7du3C49ZsGABFi9ejOXLlyM8PBweHh7o2LEj0tPTVS07WRa9TocjWz/FLx8MxWd7zyNPp6Bhg4bIHBuJ4MHvcAJFIiIVmfRlsh07dhS5v2bNGtlCFBERgTZt2shWoSVLlmDWrFno27evPGbt2rWoUqUK1q1bhzFjxhT7ujk5OXIrkJaWJn/m5eXJzVAKXsuQr0nGVQ+iX9DxfRvhemAB/PVx8Aew37UVevbsi6cbVCq132tq+F5QH+tAfawDwyvp56tGEYnBTJw9exbe3t6Ijo6Gj48Pzp8/j7p16yIyMhLNmzcvPK5Xr14oX768DEbFEZfV5syZc89+EaAcHbkqOJVM1rUYNLn6I5oqp+X9dMUBe8r1QH7dzrC2seVpJCIqZZmZmRg0aBBSU1Ph4uJi/mFI/Bki5KSkpODPP/+U+8LCwtC6dWskJCTIPkMFRo8ejYsXL2Lnzp0lbhny8vJCcnLyA0/moyRWcXlPXLbTajmzsFoMWQ/i/8PDx07AbedENMmLlvuyFS0iPfrDu89MlK/oYaBSmxe+F9THOlAf68DwxPe3u7v7v4Yhk75MdqcJEybg2LFj2L9//z2PaTSae76w7t53Jzs7O7ndTXxRlkZoKa3XpbKrh3ydHr8eT8TKP87h3NUb2GuXgFxY46h7T9TpNwetPGuxOkq5DsgwWAfqYx0YTkk/T8wiDE2cOBFbt27Fvn37UL169cL9orO0kJiYiKpVqxbuT0pKkv2GiB5XdmYG/v5lBaxifsGrma9BB2s42tpjd4N30alNCIKq1+VJJiIyciYdhkQLjwhCmzdvxh9//IHatWsXeVzcF4FIXAIp6DOUm5srR6F98MEHKpWazEHqjWs4+fNHqH9hHYKQKvc95xABzydewNCQmijvyD5BRESmwqTDkBhWLzo1b9myRc41JFqABFdXVzmnkLgUNnnyZMybN092rBabuC06QYsOVUQP68qFWFzathBNk7YiRPNPv7KrqIRLDUfhnZ4T4FDOmSeViMjEmHQYWrlypfwpJlK8e4j98OHD5e3p06cjKysL48aNk52rg4KCEBoaKsMTUUlFX07Fxt378GbcUHhq9HLW6HPWtZHi9zL8uoxAVdt7+5gREZFpMOkwVJKBcKJ1SAyVFxvRQ/3/pdfjSNRRLD6ShwPnb8g5Srvb1oO9vRPQ+hX4PNELGiuTnreUiIhMPQwRlVYIit63Gbb7F6JRXhxO5XwMaysX9PStinKtfkbjGhweT0RkThiGiO4IQcf2/Ai7vxbAN/+fNe6yocW0Jmlo17MXqpV34LkiIjJDDENEIvQkX8DpRR3hm/u3PB9Zii3+9uiLer3ewGDPmjxHRERmjGGILNqVW1lY8fNfeOvSXNhp8pGjaHHU41nU6/MGgj1qqF08IiIqAwxDZJFy8vKxal8clu85i5x8PeratIdPBT2q95uH4Jr11S4eERGVIYYhsjgnwn6F/e6Z+DlrLHIULwTUckOG00A0G9CDS0EQEVkghiGyGCnXr+Lst5MRkLpD3p9pvxFpvb5C18aVsH37drWLR0REKmEYIosYJRa5/UvUCZ+DAKRBr2gQ7t4LLV5YBFc3d7lSNBERWS6GITJryYmXEP/NWLS8vV/ej7OqhZxuixHk317tohERkZFgGCKzJGYn/ykqAee2LMRr2I88xRpHao5Cy8HvwtbOXu3iERGREWEYIrOTeCsLs346jt9ikmCNjmjqmoh6PaYipGmw2kUjIiIjxDBEZtU3KHzLctj9/TX2Z8+CrbU9XmlfH0+3/QFaa64hRkRExWMYIrNw8VQEMjZPQmButLz/WsUDaDv0LdSv4qx20YiIyMgxDJFJy7qdjqjv3oB/wnfQanT/LKNR92WMGPgWbLS2ahePiIhMAMMQmSS9XkHkr5+j2pEPEILrgAaIcgxB5f4fI7hWA7WLR0REJoRhiExO2LlkzP81BuOTfoC/9XUkohKutpqN5p1eULtoRERkghiGyGQ6R8cc2okvo3Pw3/NauW+53WCUr9kSvs+9AQ8nV7WLSEREJophiIxafm4OonZ9Ddejn6FR/hm0zG+HzVZjMDioBia27wB3Jzu1i0hERCaOYYiMUtqtGzj5yzLUOvst/EWfIADZihZeVdyxa2Bb1HYvp3YRiYjITDAMkVG5nJKJM/+djYCErxGsyZL7bsIFsTUGokGPSWhduZraRSQiIjPDMERG4e9LKVi9Pw7bjydiotV1PGWThYtWXrjWZBR8u41GiANbgoiIqHQwDJFqdPn5OPb7etiFr8TS253xm76l3B9TcwD+rt8NTdv0RU1ra9YQERGVKoYhKnOZGamI3vYpqsWsQXPlqtw30gZw9XkGo56ojSaeHBlGRERlh2GIykzylYs4s20xGiX8iCBkyH1pKIcTnv3g3X0KFlerzdogIqIyxzBEpe5sUjpW7TuPwcdGIMTqnNyXoKmCyw2Gw6f7OIQ4l2ctEBGRahiGqNTEHN6FZSfssC02Xd7PtuqCceX2IMv/Zfi2H4xqNvzfj4iI1MdvIzL4TNHH9/8Mmz/fR6O8k/DIE0tkdEOnxlUwrM1UNKw1j2eciIiMCsMQGcypQzuh/+1dNM2NlvdzFRu09rTCwOfaol5lJ55pIiIySgxD9NgunIpA2pbp8M0+UhiCjlbujTp93sLTnrV4homIyKgxDNEjy8rVYenvZxAUNgXtrKKQp1gjsmJ31OzzDoK86vHMEhGRSWAYokey52QC3vo5FpdTsrBDMwRObs6o+uwHCKrThGeUiIhMCsMQPZRrl88h4ftJuJxqh8v5I+Hpao+Zz/SAf5MXeSaJiMgkMQxRieTn5eLIhvfR9PQnaKHJho+1NW75v4KRXVujnB3/NyIiItPFbzH6V7FHfofN9ikI1sUBGiBG2xi2vZZgok8Qzx4REZk8hiG6r9SbyYj5bioCkrfASqMgFeUQ23Qa/Hu/AisuoEpERGaCYYjuoSgKtkRdwfJfDuC/+btlEAov3wV1By1GYOVqPGNERGRWGIaoiAvnTuGN324h7PxNAA742G0C+j/pi4BW3XmmiIjILDEMkXQ7LQXR389Cyyvr4ZT3CuxsgvBKe2+89GRX2NpY8SwREZHZYhiycGItscjtX8Ir/D0E46bsID2w4lm8OXw6alR0VLt4REREpY5hyIKdDNsG69/noGV+rLyfoKmC5Cfn4qmnn1e7aERERGWGYcgCnbiSivjvX0WX9I3yfqZih79rjUDz599GNYdyahePiIioTDEMWZDIC8lYsfcCdp+6hjZW9dBea43ISr1Q99k5CPGooXbxiIiIVMEwZAF9go7v3wKr/YvxZ2Y97M5/FhoN4OrTBYmtnkNQLW+1i0hERKQqhiEzlZmRiuM7vkDFU1+jqZg5GkBl64u44jsBo59qgLqVnNQuIhERkVFgGDIz8Wf+RsKuT9A46WcEIlPuy1JscaxKL9Tq8To+qMGWICIiojsxDJmBfJ0ev8Uk4duDF9E+7kMMtwmV+y9rPJBQdyAadh2LoIpV1C4mERGRUWIYMmHJifE4s2MFPk+oid/SveS+q1Yd0dwhDZrAl+DTpg+qcw0xIiKiB2IYMsEO0bHhu5Hx12fwTd2DEI0OSbpWOFpuCgYEeGFQ4FPwqjBa7WISERGZDIYhE3E7/RaO7/gclU59g4b6C//s1ACxNg1QtUVvhHV9GvZaa7WLSUREZHIYhozc+esZ+PrARfSPGIwgTVxhh+joCh3h1nYsGjR7Uu0iEhERmTSGISN16vBv+PhUOeyMuQFFAbTWIXCxzcbleoPQqOtYBFaopHYRiYiIzILFLEe+YsUK1K5dG/b29mjZsiX+/PNPGBu9Toejod8i5r0QNPq1L2xjt8og1KFRZbR5YRY83zyB4MFvw5VBiIiIyGAsomXohx9+wOTJk2Ugat26NT777DN07doVJ0+eRI0aNYyiU3TUrnVwOfABmv9/f6BcxQZdvfLwSr82qFfZWe0iEhERmS2LaBlavHgxRo0ahRdffBGNGjXCkiVL4OXlhZUrV6oegrKvROPCgtZo9tdY1NFfQLrigAOeQ5E2JhJdxy5kECIiIiplZt8ylJubi4iICMyYMaPI/k6dOiEsLKzY5+Tk5MitQFpamvyZl5cnN0OZ8H0URl/dgvpWZ+TK8VGeA1C/9wz4V6hc+Puo9BWcZ55v9bAO1Mc6UB/rwPBK+rlu9mEoOTkZOp0OVaoUnYFZ3E9MTCz2OfPnz8ecOXPu2R8aGgpHR0eDlc0pW4OP8p/DEOdIpNfuDlsHF1w/eMRgr08PZ9euXTxlKmMdqI91oD7WgeFkZv6zLBUsPQwV0Iil2u+gKMo9+wrMnDkTU6ZMKdIyJC6ridYkFxcXg5WpTVY2ft6poE33CdBqtQZ7XXr4fzmID5+OHTuyHlTCOlAf60B9rAPDK7iyA0sPQ+7u7rC2tr6nFSgpKeme1qICdnZ2crubCCyGDC1i3XhXW8O/Lj0a1oP6WAfqYx2oj3VgOCX9bjX7DtS2trZyKP3dzY7ifqtWrVQrFxERERkHs28ZEsQlryFDhsDf3x8hISFYtWoVLl26hJdfflntohEREZHKLCIMDRgwADdu3MDcuXNx9epV+Pj44Ndff0XNmjXVLhoRERGpzCLCkDBu3Di5EREREVlUnyEiIiKiB2EYIiIiIovGMEREREQWjWGIiIiILBrDEBEREVk0hiEiIiKyaAxDREREZNEYhoiIiMiiMQwRERGRRbOYGagfh6Io8mdaWppBXzcvLw+ZmZnydblqvXpYD+pjHaiPdaA+1oHhFXxvF3yP3w/DUAmkp6fLn15eXoaoGyIiIirj73FXV9f7Pq5R/i0uEfR6Pa5cuQJnZ2doNBqDJlYRsOLj4+Hi4sIzrRLWg/pYB+pjHaiPdWB4IuKIIOTp6Qkrq/v3DGLLUAmIE1i9enWUFhGEGIbUx3pQH+tAfawD9bEODOtBLUIF2IGaiIiILBrDEBEREVk0hiEV2dnZ4Z133pE/ifVgyfheUB/rQH2sA/WwAzURERFZNLYMERERkUVjGCIiIiKLxjBEREREFo1hiIiIiCwaw5CKVqxYgdq1a8Pe3h4tW7bEn3/+qWZxTNa+ffvQs2dPOcOomCH8p59+umcG0tmzZ8vHHRwc0K5dO5w4caLIMTk5OZg4cSLc3d1Rrlw5PPPMM7h8+XKRY1JSUjBkyBA5gZfYxO1bt26Vyd9o7ObPn4+AgAA5S3vlypXRu3dvxMbGFjmG9VC6Vq5cCV9f38IJ+0JCQrB9+3aef5XfF+IzafLkyawHYyeW46Cyt379ekWr1SqrV69WTp48qUyaNEkpV66ccvHiRVbHQ/r111+VWbNmKRs3bhRLyyibN28u8vj777+vODs7y8ejo6OVAQMGKFWrVlXS0tIKj3n55ZeVatWqKbt27VIiIyOVp556SvHz81Py8/MLj+nSpYvi4+OjhIWFyU3c7tGjB+tLUZTOnTsra9asUY4fP65ERUUp3bt3V2rUqKFkZGSwHsrI1q1blW3btimxsbFye+ONN+RnjKgTvg/K3uHDh5VatWopvr6+8vO9AD+PjBPDkEoCAwPlF/CdGjZsqMyYMUOtIpmFu8OQXq9XPDw85AdQgezsbMXV1VX59NNP5f1bt27JLw0RUAskJCQoVlZWyo4dO+R9EVjFax88eLDwmAMHDsh9MTExZfTXmY6kpCR5bvbu3Svvsx7U4ebmpnz++ec8/2UsPT1d8fb2lv+4atu2bWEY4vvAePEymQpyc3MRERGBTp06Fdkv7oeFhalRJLMVFxeHxMTEIudaTGzWtm3bwnMt6iIvL6/IMeKSmo+PT+ExBw4ckJfGgoKCCo8JDg6W+1hn90pNTZU/K1SowHpQgU6nw/r163H79m15uYzvg7I1fvx4dO/eHR06dCiyn/VgvLhQqwqSk5Plh1WVKlWK7Bf3xRc3GU7B+SzuXF+8eLHwGFtbW7i5ud23PsRP0RfmbmIf66wo0UA3ZcoUPPHEEzJQsh7KTnR0tAw/2dnZcHJywubNm9G4cePCwM73QekTITQyMhLh4eH3PMbPI+PFMKQi0bHu7i+Ru/eReuf67mOKO551dq8JEybg2LFj2L9/P+uhjDVo0ABRUVGyY//GjRsxbNgw7N27t/Bxvg9KV3x8PCZNmoTQ0FA5MOZ+WA/Gh5fJVCBGLFlbW9/TopCUlHTPv9zo8Xh4eMifDzrX4hhx6VKMFnvQMdeuXbvn9a9fv846u4MYkbd161bs2bMH1atXZz2UMdHCWa9ePfj7+8uRTH5+fvj444/5Pigj4pK7+NwQo4NtbGzkJsLo0qVL5e2CzxN+HhkfhiGVPrDEm2XXrl1F9ov7rVq1UqNIZktMXSCCzJ3nWgQf8QFVcK5FXWi12iLHXL16FcePHy88Rlx6EP1gDh8+XHjMoUOH5D7W2T8tZKJFaNOmTfj999/leWc9GEe9iGkj+D4oG+3bt5eXKkXrXMEmgungwYPl7Tp16vDzyFip3YPb0ofWf/HFF3Kk0uTJk+XQ+gsXLqhdNJMcuXH06FG5if+lFy9eLG8XTFMgRpKJ0WObNm2SQ+sHDhxY7ND66tWrK7t375ZD659++ulih9aLYbJiFJnYmjZtyqH1/2/s2LHyHP/xxx/K1atXC7fMzMzC88d6KF0zZ85U9u3bp8TFxSnHjh2TQ+vFiMjQ0FCefxXdOZpM4PvAODEMqeiTTz5Ratasqdja2iotWrQoHIZMD2fPnj0yBN29DRs2rHA46zvvvCOH2NvZ2Slt2rSRoehOWVlZyoQJE5QKFSooDg4OMuRcunSpyDE3btxQBg8eLOcsEpu4nZKSwur6/ykNitvE3EMFWA+la+TIkYWfJ5UqVVLat29fGIR4/o0nDPF9YJw04j9qt04RERERqYV9hoiIiMiiMQwRERGRRWMYIiIiIovGMEREREQWjWGIiIiILBrDEBEREVk0hiEiIiKyaAxDREREZNEYhoiIiMiiMQwRERGRRWMYIiKzd+LECdjY2CA0NNQgr/fjjz/Czs4O58+fN8jrEZG6uDYZEZmFtWvX4tatW5g0adI9j3Xp0gUZGRnYv39/kf1paWkoX768WLAaLVq0QERExD3PvX37Nho2bIjLly/LAJSeni6DVfPmzVGvXj0ZjIjItLFliIjMwuuvv15sy8/Bgwexc+dOTJ48+Z7HIiMjZRBycHDAyZMnodPp7jlm/vz5SEpKkrd9fHyg1Wqh0Wjk623cuFG2OhGRaWMYIiKTd+HCBVy7dg2BgYH3PLZy5UrZ+tOzZ89iw5DQt29fZGdn4+zZs0Uej4uLw6JFi9CnTx95v2XLloWP9evXD46OjvL1ici0MQwRkUnr3bs3ateuLW/Pnj1bttqIbdq0acjPz8emTZvQvn17eYnrbgWXxUaOHCl/RkdHF3l86tSpqFChgrzMJohLaQWcnZ3x5JNPYsOGDbJ1iYhMl43aBSAiehyjR4+Wl7d++eUXLF++HK6urnK/aCUSLT+ir1BQUFCxzxWPV69eHW3atIG9vT2OHz+OZ599Vj7222+/YfPmzfj6669x6tSpe1qGhJCQEHkJTlwqE5fQiMg0sWWIiExat27dYGVlBTc3N4wfPx4vvPCC3OrXr1/Yn6du3br3PE+EpNOnT8vWHtEhukmTJoUtQ6JFSXTEDg4Olq8lWpBEX6GmTZsWeY2C12W/ISLTxpYhIjJ5ooVHjO662/Xr1+VPcanrblFRUdDr9YWXvpo1a4Z9+/bJ2ytWrJAdqg8dOiQvuYnXF2Hp7kttFStWlD8LOlgTkWliyxARmbTk5GQ57P3O/jwFRJARiuvTU9BfqODSlwhD586dQ3x8vOx7NHz4cAQEBODSpUvydxT3+gWvW/B7iMg0MQwRkUkrCDXFhZVKlSrJnykpKfcdSXZny5BoKRowYIDsgySG1N/5+nf3FxJu3rxZ5PcQkWniZTIiMmlHjx69bxgq6NR895D5gpBTpUoVeHp6yvt+fn6yhefAgQNYuHChfKy40HSngtdl52ki08YwREQmrWBJjILh9XcS/YhcXFxw+PDhIvuzsrIQExODzp07Fxkqv2DBAjnf0J2zWIvQZG1tLcNScRM6uru7o3Hjxgb+q4ioLDEMEZFJq1Onjvz5yiuvoFWrVjK4DBw4UI4wE7fFhIpbtmxBTk5OYQdo0XlaXAq7u7Xntddeu+f1RctQo0aN5CzVdxLLcvz5558YNmwY+wwRmTj2GSIikyZC0JAhQ+TSGCKYTJ8+XQahAmPHjpV9hsQ8RAUedOnrTgkJCXJm6+KOE78vMzNTvj4RmTYu1EpEZk/MIC0WXBUtOYZQsLCraJUSoYiITBtbhojI7In1xUTH6OIWcn0UYokPMQ+R6GNERKaPLUNERERk0dgyRERERBaNYYiIiIgsGsMQERERWTSGISIiIrJoDENERERk0RiGiIiIyKIxDBEREZFFYxgiIiIii8YwRERERBaNYYiIiIhgyf4PAR9s7M53PMgAAAAASUVORK5CYII=", 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", 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jIiIiswmcwMCJiIiI6CXSplyxUMKY2OJEREREJi/Jwh5hutJQWzkZ9TwYOBEREZHJ2+X6DgKSl+C01ztGPQ8GTkRERGTyNM9Sh1tacMoVIiIiohdKeRY4KZmOgIiIiOjFOkWsxTbrT1H90U4YE7vqiIiIyOS5qu6igcV12KmjjXoeDJyIiIjI5Cl0qXmcFMzjRERERPRiirS56pTMHE5ERET0QhY6Bk5EREREerFI76qzgjFxjBMRERGZPAu2OOXfsmXLULlyZdja2qJhw4Y4dOhQjmXDwsLQv39/1KxZExYWFhg/fny25bZu3YpatWrBxsZGrrdv326AMyUiIqL8SIQNnursAUsbGJPZtjht3rxZBj/Tp09HSEgIWrRogS5duiA0NDTb8snJyXB1dZXl/fz8si0TGBiIvn37YuDAgThz5oxc9+nTB8ePHy/gn4aIiIheZI7TJ/BLXoWoip1hTMYdmp4PCxcuxLBhwzB8+HD5eNGiRdi1axeWL1+OuXPnZilfqVIlfPPNN3J7zZo12R5THKNDhw6YOnWqfCzWBw4ckPs3btyYY0AmljQxMTFyrVar5WIoaccy5DGJdWBu+DkwPtaBaSiO9aDWaFM3dNoC+bn1PaZZBk4qlQpBQUGYMmVKhv0dO3bE0aNH83xc0eI0YcKEDPs6deokA6eciCBt1qxZWfbv3r0b9vb2MLQ9e/YY/JjEOjA3/BwYH+vANBSnenj8VCmGhiPo5EnEXUudfsWQEhISim7gFBkZCY1Gg3LlymXYLx4/fPgwz8cVr83tMUWr1MSJEzO0OHl6esogzsnJCYaMhMUHRLSIWVkZ946C4op1YHysA+NjHZiG4lgPluf7oaTVAzjU/A98G7Y0+PHTeoxeeh4wYwpFxhmSdTpdln0FfUwxiFwsmYlf5IL4ZS6o4xLrwJzwc2B8rAPTUJzqwUdzFVWUd3BZm1Bg369FdnC4i4sLlEpllpag8PDwLC1GueHm5mbwYxIREZHh8jhZMHN47llbW8v0A5n7dsXjZs2a5blSAgICshxTjFXKzzGJiIgo/5TQmkTgZLZddWJckUgX4O/vLwOe77//XqYiGDlyZPrYo/v37+PHH39Mf83p06flOi4uDhEREfKxCMJEviZh3LhxaNmyJb788kv06NEDv/32G/bu3YvDhw8b6ackIiIiQfksAaaFpXG7Js02cBL5lqKiojB79myZ3NLX1xc7duyAl5eXfF7sy5zTqX79+unb4q68DRs2yPK3b9+W+0TL0qZNmzBjxgx88sknqFq1qswX1aRJk0L+6YiIiOh5FngWOCkZOOXZ6NGj5ZKddevWZdknBnq/TO/eveVCREREpsMSaWOcGDgRERERvZBaZwkVlFCyq46IiIjoxdpqlyFRrcEhl5owJrNMR0BERETFi0abOtzGUpm/fI35xcCJiIiITJ5am5qOQJnPRNfF9q46IiIiKh5SNFp8bzlfzlVno/YHYGu0c2HgRERERCZNnaJBB2Ww3I43cl8Zu+qIiIjIpKlUSenb1tnMD1uYGDgRERGRSUtRJadvW1oZr5tOYOBEREREJk2dnJi+rVBaG/VcGDgRERGRSUtRp7Y4qXVKwMK4oQsDJyIiIjKPwElh/HvaGDgRERGRSUtRqeRaDePOUycYP3QjIiIieoF4x8qolLQeXiWtcADGxRYnIiIiMmkqjcgaroCFlXFTEQgMnIiIiMikqVNSp1uxMvI8dQK76oiIiMikWUVdwlKrRUhM9gTQyqjnwsCJiIiITJoy/hG6KU/gRkqUsU+FXXVERERk2jTPModrFMa/q45jnIiIiMik6TSpgZOWeZyIiIiIXkybkprHSWNh3OlWBLY4ERERkUnTpTxrcbJgVx0RERHRC+metThpOcaJiIiISM/AScmuOiIiIqIXOuPeCz5Ja7C1whQYG8c4ERERkUlL1lggEbZQWDsY+1QYOBEREZFpU2vSplwxfnsPM4cTERGRSasWvhvzLPfA8mlHALWMei4MnIiIiMiklYu9gOaWB3EywcvYp8KuOiIiIjJtCk1S6oaljbFPhYETERERmTaLZwkwYWVn7FNh4ERERESmzeJZi5PCytbYp8LAiYiIiEybUpsaOFlY2Rv7VBg4ERERkWlTalK76iys2VVHRERE9EJW2tTASWlj/MCJ6QiIiIjIpE21/xRh4VFYXLGlsU+FXXVERERk2p6k2CACpWBtxylXiIiIiF4oSa2RaxtLJYyNXXVERERk0sao10JnqUIJdU0AJY16LgyciIiIyKT11O1HSct4PNJ+YuxT4RgnIiIiMl1arQ62UMltaxvmcSIiIiLKUbI6BTYKtdzm4HAiIiKiF0hMjE/ftuVddUREREQ5S34ucFJas6uOiIiIKEeqpNTASa1TAkrj39NmYewTICIiIsqJOjFBrpMV1jAFxg/diIiIiHIQY18ejZOWolJJBX6B8TFwIiIiIpOVlKJAOJxR0qYETAG76oiIiMhkxSWnyLWDjWm09ZjGWRARERFlw+pRCD61/BEpKbUANIexmXWL07Jly1C5cmXY2tqiYcOGOHTo0AvLHzhwQJYT5atUqYIVK1ZkKbNo0SLUrFkTdnZ28PT0xIQJE5CUlFSAPwURERHlxDb6MoZa7kST5KMwBWYbOG3evBnjx4/H9OnTERISghYtWqBLly4IDQ3NtvytW7fQtWtXWU6UnzZtGsaOHYutW7eml1m/fj2mTJmCzz77DJcuXcLq1avl+0ydOrUQfzIiIiJKo02Kk2uNpfFzOJl1V93ChQsxbNgwDB8+PL2laNeuXVi+fDnmzp2bpbxoXapYsaIsJ/j4+ODUqVOYP38+evXqJfcFBgaiefPm6N+/v3xcqVIl9OvXDydOnMjxPJKTk+WSJiYmRq7VarVcDCXtWIY8JrEOzA0/B8bHOjANxaketMmx6YFTQf68+h7bLAMnlUqFoKAg2Tr0vI4dO+Lo0eyb8kRQJJ5/XqdOnWSrkrhYVlZWeOWVV/Dzzz/LQKlx48a4efMmduzYgcGDB+d4LiJImzVrVpb9u3fvhr294aPjPXv2GPyYxDowN/wcGB/rwDQUh3qwDL8v14/jU+R3ckFJSEjQ73xghiIjI6HRaFCuXLkM+8Xjhw8fZvsasT+78ikpKfJ47u7ueOuttxARESEDKJ1OJ58bNWpUlgDteaIbb+LEiRlanMTYKBGkOTk5wVBEcCc+IB06dJBBHhU+1oHxsQ6Mj3VgGopTPZy4sx1IBkq6eqBN164F9j5pPUZFMnBKo1AoMjwWwU7mfS8r//z+f//9F59//rkcdN6kSRNcv34d48aNk0HVJ598ku0xbWxs5JKZ+EUuiF/mgjousQ7MCT8Hxsc6MA3FoR4sU1JbghS2jgX6s+p7bLMMnFxcXKBUKrO0LoWHh2dpVUrj5uaWbXlLS0uUKVNGPhbB0cCBA9PHTdWpUwfx8fF499135SB0CwuzHUtPRERklpTPAiclE2DmnbW1tUwrkLlvVzxu1qxZtq8JCAjIUl6MQ/L390+PMkX/ZubgSARoomUqrXWKiIiICs+KEmPQLnkeIit1gykw2yYUMa5o1apVWLNmjUwdIPItiVQEI0eOTB97NGjQoPTyYv+dO3fk60R58ToxMHzy5MnpZbp37y7vytu0aZNMXyACLdEK9dprr8kAioiIiArXQ00J3NCVh7WjK0yBWXbVCX379kVUVBRmz56NsLAw+Pr6ytH2Xl5e8nmx7/mcTiJRpnheBFhLly6Fh4cHFi9enJ6KQJgxY4Yc7yTW9+/fh6urqwymxLgnIiIiKnzxyRq55pQrBjB69Gi5ZGfdunVZ9rVq1QrBwcE5Hk+MdxLJL8VCRERExtc3YROSLZPgpK4GoLSxT8d8W5yIiIio6HtT8xfKWMbgvvZ9mAKzHeNERERERZtWo4Uj4uW2jZPxW5sEBk5ERERkkuISYmGtSB3jVKJkauogY2PgRERERCYp9nGUXKfoLGBrb7jZOPKDgRMRERGZpMTY1MApTuEgpvmAKWDgRERERCYpKSZaruNF4GQiGDgRERGRSUqOeyzXicoSMBUMnIiIiMgk3XZqIKdbWeM6BaaCgRMRERGZpMcqSzndSnxJkfzSNDBwIiIiIpMUk6iWaydbK5gKBk5ERERkktzC9mGC5RbUVp+DqWDgRERERCap8uNDGGe5DVUTz8JUFMhcdRcvXsSFCxcQHh4OhUIBV1dX+Pr6wsfHpyDejoiIiIogK1WsXCtsS6HIBU6XLl3C8uXL8csvvyAiIkLu0+l0ci2CJ8HFxQV9+vTBqFGjUKtWLUO9NRERERVBVimpgZOVgzOKTOB0+/ZtfPTRR9i6dSvs7OzQokULBAQEoGrVqihTpowMnqKjo3H9+nUcO3YMa9euxbJly9CrVy989dVXqFSpkmF+EiIiIipS7FNiil7g5O3tLbvg1qxZI4OhEiVenKQqLi4OW7ZswTfffCNfl5iYmN9TICIioiKohPapXNuULIsiEzitX79eBkz6EoHVkCFD5LJt27b8vj0REREVQTqdDiV1sYACKFG6HIrMXXW5CZoye+ONN/L79kRERFQExcXFwF6RLLedilLglNnHH38sxzMRERER5VV0kgJtkhegv2YW7EqUKrqB07x583DixAlDH5aIiIiKkehEDW7p3HHHoa64PR/FNgHmhg0b4OnpWdhvS0RERGbkcYJKrks7WMOUGCSP0+LFi7Fnzx40btxYPo6JSb19MDsajQYPHjwwxNsSERFREaW7H4wJlv8FFHUBvIIiFTiVKlUKp06dwl9//SWTXY4ZMwYzZsxA/fr10aBBg/SlcuXKOHr0qEyESURERJQT24dBGGe5HSHJTwBMRJEKnAYNGiQXkQyzSpUqePXVV2FhYYHg4GD8888/GbKHCwMHDjTE2xIREVFRlRAlVym2ppP80uBz1Yks4D169MB7772Hzp07y31RUVEICgpCSEgIbt68KQOrsWPHGvJtiYiIqIixSEwNnHR2ZWBKDD7J7/bt2zM8FtOudOzYUS5ERERE+rBKfizXihKmNbyn0O+qIyIiInoZW5UY2wRYORaxwGnv3r1GeS0REREVXfaaZ/PUObqiSAVOXbp0QatWrWQXnVqtfml5UUaUFa/p2rVrft+eiIiIiiAnbWqLk72z6Uy3YpAxTqdPn8akSZPknHXOzs5o164dmjRpgqpVq6J06dJykr7Hjx/LaVhERvF9+/bJx2LMk3gtERER0fOSUzR4PXkmyuExVlXwRpEKnGrXro2dO3fi2LFjWL58uWxN2rJlS4b0A4IIoJycnOTEvqNGjUKjRo3y+9ZERERUBEXFqXBXVw4PlW4o6VQSRfKuuqZNm8plzZo1Mn/ThQsXEBERIQMoV1dX+Pr6yoSYIr8TERERUU7CY5Pl2rWETZaGmCKXjkCpVMrWJLYoERERUV4k3j2LSZa/IMm6JoB2MCUF0vzz1VdfyUSXVlZWKFu2rBwEvnbtWr0GjxMREVExF3YaH1j+ii4pqbOPFOnAadasWZgyZQoePXoku+48PT1x4MABDBs2DH5+frh27Zqh35KIiIiKEG1MmFwn25pWKgK9AycxsPvgwYNyyeyXX37B5cuXodVq5eOVK1ciICAADx8+xKFDh+R0K2Ks0w8//IDo6Gi0adMG4eHhhv9JiIiIqEhQxKfGCRr7sjDLwEmlUqF169Yy6MnsrbfeknfWlShRAv7+/jJgqlatGq5evSpfJ9jb28uJfcV8dSIImzlzpuF/EiIiIioSrBIj5VrhaFo5nPQeHC7GKom75bIb2b5nzx6cPXtWLufOnZMtTz/99BN+/vlnWFpayqCqQYMGMqgSd9UNHToUq1evxrJlywri5yEiIiIzZ6+KkGurku4wy8BJpBAYMmRIts+JhJdiSSOSXvbr1w/16tWT3XQiNcH69euzBF4igBJBlUhTIJby5csb4uchIiIiM+eYEi3X9mU8YGoMno6gffv2cizUggULMGLECLkvJSVF5nU6deqUHBMlWqk2bdqEpKQk+bwIqDQajaFPhYiIiMyMTqdDaTHdigJwLFO+6AdOs2fPlt1yLVq0kAPFRcuT6LITd9SJ5Y8//pCtUmKAuJiGRXTvXbx40dCnQURERGYoJikFr6rmoCyeYL1HVRT5wMnb2xvbtm1D37590bBhQ9SsWVOOcbKzs8Phw4floPF3331Xdv/VqFFDLmKeOyIiIqKI2CQ53coT2/KwtbMv+oGTICbwvXTpEubNm4etW7diw4YNcr8Ilvr374+FCxcWxNsSERFREZlupayjDUxRgQROgpubmxznJBaRxykqKgoeHh5yol8iIiKi7KhDgzDZcjOSlXUAtEaxCZyeJyb5FQsRERHRi1g+OIn3LX/Dac1jAB/C1BTIXHVEREREeRLzQK5UDqaXikBg4EREREQmwzo+NXCCk+mlIhAYOBEREZHJcEhKnafO2rkCTJFZB05i2pbKlSvD1tZWpj4Qkwq/yIEDB2Q5Ub5KlSpYsWJFljJPnjzBmDFj4O7uLsv5+Phgx44dBfhTEBERUZpSKanTrTi4VoQpMtvAafPmzRg/fjymT58uJw8WCTe7dOmC0NDQbMvfunULXbt2leVE+WnTpmHs2LEyXUIaMSlxhw4dcPv2bWzZsgVXrlyRSTw5HQwREVHBEzONuOii5LazWyWTvOSFclddQRC5oIYNG4bhw4fLx4sWLcKuXbuwfPlyzJ07N0t50bpUsWJFWU4QLUliCpj58+enJ+AU8+lFR0fj6NGjcmJjwcvLq1B/LiIiouIqKvwByik00OoUcC5nmi1OZhk4iZYhMYHwlClTsiTeFEFPdgIDA+Xzz+vUqRNWr14NtVotA6Xff/8dAQEBsqvut99+kykURMLOjz/+GEqlMtvjJicnyyVNTEyMXItjisVQ0o5lyGMS68Dc8HNgfKwD01BU6+FOgjV6JX+DWo4JWAoFtIX48+l7Lc0ycIqMjJSTApcrVy7DfvH44cOH2b5G7M+uvGgWFMcTY5pu3ryJffv2YcCAAXJc07Vr12QQJcp8+umn2R5XtG7NmjUry/7du3fD3t7wqeLFBMlkXKwD42MdGB/rwDQUtXo4HaXAPZ0rrHS6Qh9fnJCQUHQDpzQKhSLLjMqZ972s/PP7tVotypYti++//162MImB5A8ePJBTx+QUOE2dOhUTJ07M0OLk6ekpW7cMmSVdRMLiAyLGYKV1I1LhYh0YH+vA+FgHpqGo1sOjo3eAq1dQy8sdXbvWLdT3TusxKpKBk4uLiwxsMrcuhYeHZ2lVen4KmOzKW1paokyZMvKxaHUSv4DPd8uJsVDidaJ70NraOstxbWxs5JKZOE5B/DIX1HGJdWBO+DkwPtaBaShq9eASugMfWp6Eo2UXWFk1LNT31vc6muVddSKAEa1BmZsoxeNmzZpl+xoxdilzedGd5u/vn36xmjdvjuvXr8uWpzRXr16VAVV2QRMREREZToXwgxhj+Tu8U67AVJll4CSI7rFVq1bJO+EuXbqECRMmyFQEI0eOTO9CGzRoUHp5sf/OnTvydaK8eJ0YGD558uT0MqNGjZKTEY8bN04GTH/99RfmzJkjxzkRERFRwbJPeiTXVqVNM/ml2XbVCX379pVBzuzZsxEWFgZfX185kCwtfYDY93xOJ5EoUzwvAqylS5fCw8MDixcvTk9FIIixSaIVSpSpW7euzN8kgihxVx0REREVrJLPkl/au5hmKgKzDpyE0aNHyyU769aty7KvVatWCA4OfuExRZfesWPHDHaORERE9HLiDvay2ghAAZR2qwxTZbZddURERFR0hIeFwkaRghSdBcp4MHAiIiIiylH0/etyHWHhAgtL071TkC1OREREZHTxj27K9WNrd5gyBk5ERERkdMfsW6J50jf42+sjmDKzHhxORERERcPdJ2rchyts3WvClLHFiYiIiIzu3uPUueIqONvBlLHFiYiIiIzu9UdL0cJSgUo21WDKGDgRERGR0XM4vZ6yA9aWGkQ4fmrStcGuOiIiIjKq8LA7sFZoUnM4uVUy6dpg4EREREQmkcMp3MLVpHM4CQyciIiIyKjiHt6Q6yfWbiZfEwyciIiIyKg0EaktTvElvEy+Jhg4ERERkVFZP03NGq5zrmLyNcHAiYiIiIzKMfGBXNu61TD5mmA6AiIiIjIanU6HXqqZcE55hJ982pl8TTBwIiIiIqN5FJOMeLUOSRblUMGtrMnXBLvqiIiIyGhuRsbJtaezHayUph+WsMWJiIiIjEZ9YQe+sdqIR7avAGhj8jXBwImIiIiMxirsJHooj+KEwvRzOAmm3yZGRERERZbN01upG2WqwhwwcCIiIiKjcUlMzeHkUL62WdQCAyciIiIyioSEOFTQpuZwcq/RwCxqgYETERERGcW9a2egVOjwBCVQulxFs6gFBk5ERERkFI9vnZHrMOvKgEJhFrXAwImIiIiMIiYqDCqdErFO1c2mBhg4ERERkVH8pOiO2slrEVp/ktnUAAMnIiIiMsocdZfCYqCGJSpXKG82NcDAiYiIiArdw5gkRMQmQ2mhQC13J7OpAWYOJyIiokJ3/8Tv+Mv6cwTZvwI7665mUwNscSIiIqJCp7p9DLUt7qCWbbRZXX0GTkRERFToHCLPyrXWo75ZXX0GTkRERFSoNBoNvJIvy+0yNQLM6uozcCIiIqJCdfP8CZRCHBJ0NvCq1cSsrj4DJyIiIipUEef3yPUN+7qwtLYxq6vPwImIiIgKld29o3KdWL6Z2V15Bk5ERERUaJLUGgTFu+KRrhTK1GlvdleegRMREREVmkPXIvF/qrfQy3Y1qtR5xeyuPAMnIiIiKjQ7zz+U6/a13aGwML8wxPzOmIiIiMxSbMxjxJ7/Gwpo0bWOO8wRp1whIiKiQnF+5yp8bzEXpxzqo2GlV83yqjNwIiIionzRarQI3vdfJN06DoVjWVRpPwLurmUylElKjEeli9/JbV21dlAoFGZ51Rk4ERERUZ4lJiTgwre94Z94RD6O0DmhxblaeKtpVYxrVx3ODtbQ6XQIWjsZzRGBcJRBnR7jzfaKM3AiIiKiPDv9/QgEJB5Bss4KF5zbIkhbA0nhSqw7ehvbgkLxjesfKJF4F83jD8ryj5rPRFl7R7O94gyciIiIKE/OHPodAU/+lNs32n2HBi17oQEAH5Fy4K+L8AzfjzaR69PLn6w2Do06DDLrq83AiYiIiHJNp9XC+sAcuX3S9XU0atkr/blXqrtgx9gWOBOYiBPn+gEWSrg2fQuN6rYw+yvNwImIiIhy7fTJg6ifckl20VXpNTvL8xYWCtRv3gkQSxHCwImIiIhybdV1J1xIXoD3vFXo51ax2FxBBk5ERESUK08SVNhz8RFUOnf4dTD/7rfcYOZwIiIiypXfQ+5CpdGitocTank4FaurZ9aB07Jly1C5cmXY2tqiYcOGOHTo0AvLHzhwQJYT5atUqYIVK1bkWHbTpk0yOVfPnj0L4MyJiIjMl9+BYVhu9TWGeGtR3Jht4LR582aMHz8e06dPR0hICFq0aIEuXbogNDQ02/K3bt1C165dZTlRftq0aRg7diy2bt2apeydO3cwefJkWZaIiIj+Jyr8AXyTT6OL8iRa+ZjnfHPFMnBauHAhhg0bhuHDh8PHxweLFi2Cp6cnli9fnm150bpUsWJFWU6UF68bOnQo5s+fn6GcRqPBgAEDMGvWLNkqRURERP9z/cg2KBU63FBWQVnPGsXu0pjl4HCVSoWgoCBMmTIlw/6OHTvi6NGj2b4mMDBQPv+8Tp06YfXq1VCr1bCyspL7Zs+eDVdXVxmUvazrT0hOTpZLmpiYGLkWxxSLoaQdy5DHJNaBueHnwPhYB6bBmPVgdXWHXIe7t0XFIvSdpO+1NMvAKTIyUrYMlStXLsN+8fjhw4fZvkbsz658SkqKPJ67uzuOHDkiA6nTp0/rfS5z586VrVOZ7d69G/b29jC0PXv2GPyYxDowN/wcGB/roHjWQ4pahS4JpwAFcNeiIsJ3pAZRRUFCQkLRDZzSZJ5ZWUwi+KLZlrMrn7Y/NjYWb7/9NlauXAkXFxe9z2Hq1KmYOHFihhYn0WUoWrecnJwMGgmLD0iHDh3SW8eocLEOjI91YHysg+JdD2f+2Qx7RTIewgU9B4yEwsJsR/xkkdZjVCQDJxHYKJXKLK1L4eHhWVqV0ri5uWVb3tLSEmXKlMGFCxdw+/ZtdO/ePf15rTb1bgFR5sqVK6hatWqW49rY2MglM/GLXBC/zAV1XGIdmBN+DoyPdVA860FzObWF6Y5rG7hl891nzvS9jmYZKlpbW8u0ApmbKMXjZs2aZfuagICALOVFd5q/v7+8WN7e3jh37pzspktbXnvtNbRp00Zui1YkIiKi4kqj1WHvUw+c1NZAiXrFN1WPWbY4CaJ7bODAgTLwEUHR999/L1MRjBw5Mr0L7f79+/jxxx/lY7F/yZIl8nUjRoyQg8XFeKaNGzfK50VuJ19f3wzvUapUKbnOvJ+IiKi4OX33MVYmtsFm2w4IatoBxZXZBk59+/ZFVFSUvAsuLCxMBjc7duyAl5eXfF7sez6nk0iUKZ6fMGECli5dCg8PDyxevBi9ev1vNmciIiLK3t5L4XLdumZZWCnNssOqeAdOwujRo+WSnXXr1mXZ16pVKwQHB+t9/OyOQUREVBzFn/0dJeGFdj5lUZyZdeBEREREBe/B7SuYnfA5pttYIsnrWrG+5MW3rY2IiIj0Enpsm1zftK6Jks6li/VVY+BEREREL2R3e69cx1RsX+yvFAMnIiIiylHs02j4JKbOqOHe+PVif6UYOBERERVBWo1WLvl1+eAWWCtScFfhgYo16qG44+BwIiKiIiQ25jHO/zABvpE75eMLLp1Qa9DXcCqZt7FJlpe2y/X98p3h+YJpzYoLtjgREREVEUmJ8bj3bVcERG2HoyJRLk2jfkXkN23wJPJRro/3NCYG3vGn5LZbs34FcMbmh4ETERFRERG8bhJ81BcRAwecb70aF9quQyRKQZeSjOmbj0Kdy667Pddi0DJ5Eb6yHYdKPo0K7LzNCbvqiIiIioDzd6Pw+MENQAncbrkQdVv3lvtvla2EwRtv4N5da1Tffx3j29fQ+5hbgu4iAqVg22ggwG46iS1ORERERcDCf25ijHo8ZlXegLpt30rfX9m7Pj58PUBuf7vvOk7fidLreDcfPcWxm9GwUAC9G1YosPM2NwyciIiIzNyZu0+w73I4lBYKDOrWOsvzPeqVx2t1y+EdxZ9w+KEdEuPjXnrMR79Ow2br2RhZ8QE8StkV0JmbHwZOREREZi5kz88ojwi85ueByi4O2Zb5T2cvvGe1A9W1t3Bm3YQXHu9pdCR8H2xFE4vL6FQ9++MVVwyciIiIzDxB5Zt3ZuOQzXgMr5GQY7mSpV0R1mqe3G4a8QvOH/4jx7IXfv1K3pF3y8ILddr0LZDzNlcMnIiIiMzYxV1r4KBIxl1ledTya/rCsnXbvInjZXrIbZe9ExDzJOt4p4d3r8Pvzjq5Hd3wA1golQV05uaJgRMREZEZK31lo1yHVe0DhcXLv9Z9hyzGfUU5uCECd5b3hio5Kf25FLUKET+PkIHYZataqN95aIGeuzli4ERERGSmbpw9iuqa61DpLOHd8V29XuPgWAoJr61Cgs4GdZKDcf7r1xAaGY/H8Sqc+nag3Cees3vjW7Y2ZYOBExERkZl6fGS1XJ9zaolSru56v656/Za40e47GSD9HFMfLef/i/r/2YNr0SlQ65S40mw+vHz8C/DMzRcTYBIRERWQqPAHSIx7Cg+vGgZvvdGoklAnajegAGyb5L5LrU7L13HTsy4i9kUC1yLlvt1l3kbDJkNRv1k3g55rUcLAiYiIyMDEvHA31g5Hw/iD8nGowgMx7efBt/mrBnuPRw/vAtDhvsINtZp1zdMxqlSuip+GVUVskhpqjQ6lHawNdn5FFbvqiIiIDCjmaTSil3VMD5pE11dF3QO47h6Df05fN9j7bHrqjcbJy3DUfxEUFvlrzXK0tWLQpCcGTkRERAai02pxddUwVNHelpPr3njjL6gm3cA+5z7oo/oUY7Zew+WHMfl+n4thMbgTp4BaaYc2rdoa5NxJPwyciIiIDCQwKAi1Yw4jRWeBqFdXo2rdV+Dg5IyW738Hr+p1kKTWYvym00hO0eTrfXYeOSW76dp7l4VLCRvWXyFi4ERERGQAao0WMw7Go23yAvxZfRZq+rdPf85SaYEFb/qhjIM1yoUfxpGf/y/P7xMX8xjvXx6Ev62nYnAdBk2FjYETERGRAWwJuoebEfFQObijbe9RWZ53dbTBN62AH6y/xCu3FuPu9fN5ep8LMlN4Euwt1GjgU8MAZ065wcCJiIgonzQaDXbv3y+3R7WuCidbq2zLNX+lHc7ZNoS1IgXRW8bLMVG5Icq7XPpJboeUaKNXpnAyLF5xIiKifDqzdyPWJo7Fctsl6Ne4Yo7lRKBTqtfXUOmU8Es6idN7N+Tqfc4e3I6q2lsycWVKheasNyNg4EREREVKUmICAjfMQfBXXXHi6744f/SvAn0/nU4Hu1NL5LZz+epwsHlxikTP6n4IqjBQbrsfnYn42Cd6v5fy6CK5PluuB6zsHPN13pQ3DJyIiKjIiAq/j/vzmyPg6pdokHAEjZ/uhO/u/ghcMTrX3WL6unxiD3zUl+R8cdVenaTXa/z6zcYDRVk50e65nz7U6zUXAv+Gr+qsbK3y7Do5n2dNecXAiYiIioSkxHg8/q47qmpuIhpOOFZlLE6U7i6fS7x/AQt3XSyY9z2Q2gp0ukxnuLh76fUa+xIlEdX6S7nd+NF/cfGcSC+QM61Wh9B/16W+j8urKFu+Sr7Pm/KGU64QEVGR8N9tv6Bfyi08Vjghvv8faFqjntx/4M+fMeqwA5IP3IFvRRd0qu1msPcUd8b5xR+V88WV65i7VqA6rd7A/rO7sCnMDdd3x+G3GikokUM338aToZj+dCB623hjap8RBjp7ygu2OBERkdm78jAWn54vi16qmbjT5lt4PguahFavvo0BzWvK7WnbzuFxXJLB3vf+zoWwUOhwxq4xvLzr5/r19YZ+izMlWuJGRDwm/XJatixldj08Dv/35yUxtBw+HYagTLkKBjp7ygsGTkREZPYW7b0KnQ4o7/sK6rXqmeX5j7vURB1XJd5PXolrKwcb5D2fxiXBPeKw3LZs9n6ejuHsYI3lbzeAtdICly+ewaElI6BK/l9gd//mZVxf0R+W6lg0q1oG7zSrZJBzp7xjVx0REeXbjXPHEX5yC6DVoFTdLvBp3KHQruq1S2dw+UIIFAp3jG+ffUJIG0sl5rWyRo0/dsPiqQ7nDv6GOi175Ot9N5y6j6+Tv8Kg0pcwvXnqWKq8qF/RGV+/6YPa28ejUvRD3PnyGB5Ueh1IjEbtsG3ojATYOahQu99vsLBQ5OucKf/Y4kRERHmm0epwYNVHqLq1IwJCv0fAvdXw2dEbpxb2QmJ8XKFc2bg/pmKv9WTMrXgSNcrlfIu+t39bnHR9Q26X2j9FDibPK1WKFj8cvQ0VrODdbmC+E1F2q1cJT1v/nxzU7qW9i4CbixEQ9jOckIArljXh884yzklnIhg4ERFRnn3+1yWsuukMjU6B0/bNEOTUTk5w6x+zF9e+7Qm1KrlAr+6104dQP+GIGJuNgDYvb0HyeXseIuAMT90DhGz4NM/vu+vQUUTExMtpVLr7ucMQ/Nq8CeUHp3Cs+iSccmyLk6W6IKjxAlSbchRlK/AuOlPBrjoiIsqTX0PuY82RWwDqYk/bP9C5VQu5//zh31F1zzDUTTqJwFUfIGD09wV2heN3pU6WG1yyPfz1GJztVKoMghp/BtcT49EwdB1Crw5ExecGkutDlZyMhgffwV5rBYIbLpPdgIZSskw5NB2Q94COCh5bnIiIKNciHz3Awt+Oye1x7aqnB02C7yuv4XLzr3FR64WZd+vjwNWIArnCV4P/Rb3EY7K1q1x3/YONBp0H44xtIzlfXOyWsblOjBny5zJ46MLhZJGMbi2b5uHMyZwxcCIiMhNiXM3643cwaM0JvPV9IDb89heePo40yrnc3zQOW3UTMKDsTbzftlqW5+t3fBu/NPgJV3QV8eF/zyAmSW3wc0jc/ay1ybkTPKvX1ft1YjySa98lSNRZo0LyNew+HKj3a2OfRqPqudSEl9eqD4OtfYk8nDmZMwZORERmIDr8PjYtmoTp28/h4NUIHLsZhQZBHyPxm6a4cS615aewJEXegn/cfpRBDIa2bwArZfZfJVO6+aKyiwPCY5Ox4u8TBj2Hyyf3yklyxXgqj9dy37XlUdkb++p8iXbJCzDtQAKeJKj0et35jTPggie4q/BA/V6c9qQ4YuBERGTiEuKeIur7HhgUtwbv2v6DqV288XU3D5RQquVcZ6W3vok7V0IK7Xyq3tuSPq6oat3mOZaztVJi9ms++NByE8ae7oEbZ48a7Bz2Bp7CU509Qpy7oHyV2nk6RoeeQ+Bctjyi4lX47PcLcrLeF7lyah8ahW2U29EtZsHG1j5P70vmjYETEZGJu7ByBKqnXMNjOGLAW4PwXquqeL1FAziOPYJrltXhjBgoNg9AfOyTAj+Xi0f/hL/unJxo1uP1/7y0fIsa5dDUORa2CjVSfp8ArUaT73M4fjMK8x/UQWv1Ynj0/iLPx7G2tMDcN+pAaaGA5uxWHNv4eY5ln0Q+gsNfo2Cp0CLIsQ382vbJ8/uSeWPgRET0zKN7N3D828F4OLMqwmdWxqmFveVcZMYUvOtnNHq6Sw6ADuu8KsO0HiWdXVDm3d8QjtKoqL2Pi6veLdBzEYOo7Q6mBhfBrj3hUdlHr9dV7LcIcTo71Ey5jJPbFuXvHHQ6fLXritzu2sgb5StUzNfx/CuVxjfNVVhi/S0Crs7D8V++ylLmaaIa720+j8sp7giDK6oNXZWv9yTzxsCJiEiMmTm+G9arWqNJ1K9wQyTKIhr+MXtQ5qe2OH5wl1GuUVzMY1QMnC63T5YfiFpNO2cpU7pseUR2Xi4DKxFgndn3S4GdT8jun1BDcw3xOhtU7vmJ3q9z9aiE894fyG2fCwsQ+TA0z+cQfPBPON3dB1srBca2qw5D6NblNRwr21duN7n4uUzeeevCcXn9D54IQo8lh3H8bhI+Un6MxLf/kAErFV8MnIio2LtxMQjldwySXV7XlVVxru1aXOy4ERes/XBE64tBO5Ox/0p4oV+nc5tmyoHI9xTuqD/oyxzLiYDqpNtbctv+0OeIL4A72FI0WlwOPiC399t3QelcTjTb6M2PcU1ZDU6Ix+314/N2DmoVSh+YgbXW87C4yimUc7KFIYi77JqMXIHAiiNlACqSd1b+b0eUWFgJTn+OwO2oBHiUtMVP7zZDlWr6tbJR0cXAiYgKXFJCHPb8vh7DfziJoetOYuXBm4gtgC/3vEhSazD6zwgc13rjorUvKkw6iDot30CtZl1R88O9+NtnLpK1SozdGIIbEYUzhYjw4MFd1L+/Xm5HBMx46UDkugO/xFZlF/RP/BDzdl81+PlsCbqHaTG90F/xBVSVs7Z8vYzS0hKK175JDUxi/8HZ/akDzHPj1H+/QmXtbTyFA5q89h4MSQRPAUO/xPXXtuO0fYBMVSCUt4jG+83d8Pe4lqjtUdKg70nmiYETERWoO5eDET6/MVoEjcOxS7ex73I4Pt9xCYsWzMbVZy0YxvTdgZu4Eq3Bp7ZTUX7UHxny8lhaWeOLPo3g7+WMuCQVtq1bWOBTiKSZeyASb6um4m+HnqjXvv9Ly9uXKAnXvt/K6UR+CLyN03efGDS4XLT3mtxu3boDlDZ5u5usmt8rOOnWFwk6G2w+cgmP4/VLASCE378F3ytL5Pbl2pNkhu2CULNhG9T7aCesPwlD/KQ7cPn0JiZ3b4iS9lYF8n5kfhg4EZkxcYeS6L4wVXeunEaJTT3lwOVYRQl81qoUPuteC+1L3sfHqqUo/1sfXD61z3jnFx6Npf9el9vTutdBSefS2d55tWxAA6ywXYoP4xfg1E+pY44KUtCdx/jjzAMEwRsVByzWewLZljVc8Xr98hB31W/e/JPBgrx9f6yHZexdlC9lh/6NctdFl1mdt7/EaIcFWB/XAJP/e+alKQAETUoKwn8cghKKRDnhbaM38tbVl9sWMgfHUvmevJeKHv5GEJmha49i8e6Pp9D7sxWw+L+yuPp/jRCy6weYEpFh2WJTP5TBU9xQVoHlmEC82aUD3mleGQtH9cI1G184KJJQ7s9BeBia2ppRmMQdYk/W9sUCxSK8WkWBbnVynqi1rJMtXBr1ltv+oWtw/czhAj2vJb+nHv/NhhVy3T00o5sPvrJdi7lxMxC0KTWzdn48jQ5H87NTsM96Imb7J8HGKn/zsolg5KO3e8iA9J/L4Vi24+WJMU+u+wi+yadlS5Vdn+9hoTTc3HBEucXAicjM/BlyB90WH8bui4+QrAEsFDrUSLmK+oFjcXzxQJNogRJf/ldXviNnoH+EMnB+7084u7pnmGi18tg/5EBsZ8Qi5sf+UCUnFeo5huzZAL/EE+hkcRIft/aAQqF4YfmG3YYjuEQrWCk0UPz+PtSqgjnf4J3rsCxyKMZa/47JHWvm+vVlStigcr3Wctvvxgrcv3khX+dzef2HKIl43FdWQOvWHWEItTyc8J8etVFbcQt9T/TGsR+m5Thf3Nbd/6Lh3XVy+0L9z3I9IS+RoZl14LRs2TJUrlwZtra2aNiwIQ4dOvTC8gcOHJDlRPkqVapgxYoVGZ5fuXIlWrRoAWdnZ7m0b98eJ04YdpoAMj0XzpzE8SVDETKvG44tH4mb54/DVAXv+gme23vCUpOAVjVcMW9MfzwcFoRj5QdDq1OgSfTvCFk6yCBJBvMjZM96NIz7F2qdEo+7fS9vmc9uTI792xvkQF8R+AX/NK1QM3F7BH4mt4M9B8FTzy9jr4HLZBLKqppbCFqf+npDSk5KgPvJL2CnUCGgkpNs6coL/9dG47xNPXmc6M3v53oS2zRXgw+iUeRvcjux/VzZfWUofRtVxHSfcLgoYtD01lIEfd0bkQ/upD//NEGNadvPYdK+eIxQT8ThiqPQqOcYg70/UbELnDZv3ozx48dj+vTpCAkJkQFPly5dEBqafX6QW7duoWvXrrKcKD9t2jSMHTsWW7duTS/z77//ol+/fti/fz8CAwNRsWJFdOzYEffv3y/En4wKS3KKRk4++u7GC/CL+B314w+j6aONqLKlI44tf6/QBgHr6/qZI/A5OhF+FjfxReUzWDOkEWpVcIabZzU0HbEYp5t9K+ftavTkbxz/sfCCkMzik1OQfGy13D5VYRC8G7V/4Xxh1xunJlT0v7u2QLvAnndm/QyZq+mBoiz8+r08+3WaMuUq4IZ/6rxoDW6vxO2LJw16XiFb58FD90gO8PbrMyPPxxHjckq+uRRJOivUSQ7Gia0Lc30M0QJo8dd42aJ5yqkDajXrBkMLeHsmjtX4SP7eijvtnL6rj0ufN8P5ua2w7IsJ2HA89e95w3Z90fyduQZ/f6JiFTgtXLgQw4YNw/Dhw+Hj44NFixbB09MTy5cvz7a8aF0SgZAoJ8qL1w0dOhTz589PL7N+/XqMHj0a9erVg7e3t2yB0mq1+OeffwrxJzM/4o6bfft24sQ3A3ByUX+c2LpI/o/e1M95yJqT+G/QPTxQuGKT5wwc95mK4BIt5fNNH23C+W/eMJngSXyJKX8bJVsQztr6o+vQGXKaiOc16DQQwXVTW0Ea3/4O547+bZRzXbT3KgYmTsBCqxGoP+DlY2wadn1HdoGJqSyCd6yRAW1BunMpCP4Pnt3m33w27Bwcc/X6hl2HI8S+GawVGiRtex8pBjpfMYlvrWvfye3bdSfIFrn88Kzmi9PVU1to6p3/AjfOHsnV64PXTkQ1zQ3Zwlapf+4DL30DvKb9p+N69y24bOkjr6mP+oIcz/Qm/kHNsiWwfngTfNCu+ku7UokKi+HaXQuRSqVCUFAQpkyZkmG/aB06ejT7SSRFC5J4/nmdOnXC6tWroVarYWWV9VbThIQE+Vzp0lnvtEmTnJwslzQxMTFyLV4nFkNJO5Yhj2kIZ+89xeQt52AbfRN/2/yZuvPJX3h47lvcaPc1vJt2gakR3Vgnlo2AKqI+HGxqYWm/emheNe13YxJO7V2POscmyxaoE0sHod7oH+UfeGPWwamfZ6CZ9g6i4QT3IeuggyLb86j/2hgcv3sCd6NisegfDbbXTkCpQryN+vLDWKw5chsaWKJOz4lQWtnodb3K91+Cj1auwS9P/XFn71WMb1ct23L5rQPRZRW3bZwcpxRiFwDfVr3zdKxyfRcjeO0AzIrvj86HbmL4K5WQXzd+Ho9GiMcNi8qo0+Vdg/ye1e89Fae/CUS9xOOw2P4uHngchWvJl6cSOHHkHwSEbQAUwM2mc1C3jHuWa2/Iz0FVv5aA3yHcvHYWkddOQKfVoJRXXfxRu0mGzx6Z/neCOdP3Wppl4BQZGQmNRoNy5TLm8RCPHz58mO1rxP7syqekpMjjubtnvaNGBGbly5eXY51yMnfuXMyaNSvL/t27d8Pe3vAzZ+/Zswem4kZ0MlZcs4dKq4CTVUX8Yt8f9rp4NEw4DHdFJErvHYLtZ0bBxrMxTInu2g70jPsd9a1348dKC/D0ynHsSJ366hln3Cg3Fr0fLUR8dBg+XfsHAtytjFYHSY/v4/XQNfJL7J/Sb8P6ePALy6s9e+OrJ1YIj1dg1Mp/MLB63sa35JZonb167ggstC3gW9oCCddPYkfqnf56cfBsAFxVYPm/N2AXdRWe/0unlEVe6+BqWDTeVt1HIqxxvXxvhO7Ygbw6Vn4GztxQ4uLuK7B8dBFl7fJ8KCQ9vIi+MbvlOLWTHoNwfvduGIraazDsL4Vhpupt3F52CO/X0sDhBbH0jRhg+SULjLXogZr28UhMLol72VyngvsceMi+kId3H+Py3Z0F9B5Fhyl9J5g70VhSZAOnNJmbbkU+kBc152ZXPrv9wldffYWNGzfKcU9iMHlOpk6diokTJ2ZocRJdhqJ1y8nJCYaMhMUHpEOHDtm2jhW2m+ePoVnw29ivm4xS1RphUZ+6cLLrJJ8T3XRBq4fJwcGvRyzHRd+GqNW8O0zB3WtnUD54qwxCzteahHffSJ2fKquu+OMvH0w8ZguLUCX6dm6CmmXtCr0OROvYrQWtZRfGadsm6PHeTL3yylSs/wRvrTyBU5EKjG7ujhb+/5sYtqAEbf8Gr2tXoovNfpR6Zx/cS+XuPw5dxX9wNp3BsQvX4Bq6B+0/+AbWNrYG+xyIiVpnf3MEa5Pn43P/RPTskVPd66eLToe7Pwbj8PUonIlQYcGI7nm6TT42KQVLFgcjWWeFEJfu6DU4dU43Q7oT2QXXVgchIk6F7285YcXrlVDVK2s+pl0hN/DdyVtQa7U4VXU0hvf3g6Wl0qT/FhVXrAfDS+sxKpKBk4uLC5RKZZbWpfDw8CytSmnc3NyyLW9paYkyZcpk2C/GPc2ZMwd79+5F3bp1X3guNjY2cslM/EEpiD8qBXXc3Ih6dA8lf3sHroonmFZqDxoOGSNzsqQRE2DWG78Vpxb3lXM+Pfp3BRxrd0QV1xc0IRQCkURPte192CjUcpxQ0zcnvzAI6dGjN3bFBePv8w/x4bbz2P5eo0Kvgy0HT6FJciTiFbZwG7AM1tn8rmWncRVXjAsog3onP4TvrjuIq3Y8QzoAQ4sKvw+fi6njYBJq9EBd17yNz/lPz9p4fOMdVE8OReBmZwQMW5BtubzUwfzfLyEqXoVqZV3QtecrsMoUEOTFF73q4uevP8aEyA0I3nASAUO/ytXrxX/eZv11Hr/FNsZ1Jy8sGdijQH63qrmXxoYRTTFk7UlYRl+F5099EezaDY4N+8DRxQNRty9AEbwOFePDoUmZhXbeHljSvwHsrJUm/beIWA+GpO/vs1kODre2tpZpBTI3UYrHzZo1y/Y1AQEBWcqL7jR/f/8MF2vevHn4z3/+g507d8rnKOsYkdAfRsANEbir8ECtEaszBE1pxG3LdUb/hB9LDMPopDH4YGNIgQ/6fZmTmz+Hd8olxOnsUHbAdy9tuREtkXNer4OKJbQYGv01zq8bh8J0/0kiPjvwFB1VX+JQk+Xy7rnceK9DXXhZPZYJKG/98F6eb0nXx431E2WuH5Ho0r9PxrGHueHiaIeYxhPktn+ouMsudwOac3IhcCd0wT9CAS3mvlEHNgYImoQKzvZoW78GbBQpCAj9TuZgyo0NR67gt9MP5ED/cQNel/mtCkr1co747f3mGFvuHBwUyWgSuQ21dr0Fz/UtUe/IKPglHkctxR183iAOKwY2fGHQRFScmWXgJIjusVWrVmHNmjW4dOkSJkyYIFMRjBw5Mr0LbdCgQenlxf47d+7I14ny4nViYPjkyZMzdM/NmDFDPlepUiXZQiWWuLjCm9jT1J36fTnqJxyFSqeEutc6lCztmmNZMSlpxxFz4GhvhwsPYvDF35dhLHevnUW9q9/K7Yt1P9Y7CHF2sMbiV9Tob7kfARG/IPHhRRQG0RIxY/s5xKs0qO3ljo6dX8/1MWzt7KF+bbnMpdQg7gCCdqwqkHM9f+RPNH66U47PSemyQM7vlh8NuryDIIeWcgC3xW+j5QTB+c1gXnL3OHxptRLLKh9Fo0o53+yRF41f/wDHyqZ2+9UKnIyz//4vxcmLiCCrzZ4uqKW4jQ871URDL2cUNJcSNnht/BKcb/cjTjm2xUO4IlZnh1CL8jju8gYeDNiPvn0GwEpptl8NRAXObD8dffv2lakFZs+eLdMHHDx4EDt27ICXl5d8PiwsLENOJ5EoUzwvxiyJ8qJVafHixejVq1eGhJrijr3evXvLweJpy/MpC4qzR/euo+bp1NvLg6qMRBXfJi99jVtJW8x/0w9WSIHb8c9xZt8mFDatVoegXxfDVqHGOZsGaPR67lqO6rV+A8fL9JTbrR98j5jHEShop3asQfnrG2CjTO0OssiUekBf1eu1wCmvYanbp2Yi4sFtg56nCGqc/vlIbp906YGa/m3zfUzR0uc1aLm8g7CK9jbOfTcsz61l4nVXVo9ABd1DhMEVzfpMQkHwH7FEpigQv2Pe+9/Fyd+WvfCcTmxZiLqBE+ChiMb08iF4r2UVFCbfFj3gP2k73GZeh+Osh6j46UU0eX8ts3ITFeXASRA5l27fvi3TAYj0BC1bpubgEdatWyeDpOe1atUKwcHBsrxIiJnWOpVGHEv8Tz/zMnPmTBR34o99+E8j4ISE1Ek2++t/Tdr5lMPSqifwnuVf8Dz4ISIfZp+ktKCImeLHR/XEDO1IuPRfkadJO+u8s1h2TboponHrx4LNXhz58C6qn/wU/2e1FstqXUS1svkbG+b/9v/hmrKa7EoL+3G4QbvsgtZ9KCfwjUQpeL+d/XikvHApVwEP2i+FRqdAo6c7ceKXL/N0nBP/nSfH2YkEi0+7rZDj7wqCaGWrPW47gh1awlqRgkYhU3H46/64GZGxtSz06mmELHgNjc/PknmrTpbqgibvLWOOIiIzYtaBU3ES9jQJ142YU3LzsasITbCUmYht3/w+190xLd+ehpsWlVAaMXiwbmiBjrd53u3IeHy5U3QRKuDddTTcvXI/95cgkhHGdl6cmuE4bh9O/fk9CkroT6NRCnFyvFDLN8fm+3hW1jawfnOlvGurbtJJHNuQ/4lfhUPXIvDx3SY4rvXGg1bzDR6U+L7yGk5WT20dLHFxI/4I+d90HPo4vXcj/C+mZps+WfX9F2YwNwRxB6DfhO0IrPiu7LYMjHRA2wUH0GHhAcxf8i3uzPZFxQ2t0CD+kOw+PVZpDPzHbsh31yYRFS4GTmYg7GkiBqwMhOLqH7h5tnCmpHheaFQCZu+8jTHqcfij+RZ41cz9JJu2dg6weHO1DLzEl/fxzXML5S66I+umwUodh2ZVy6B/44r5Ol71Bq2x076H3K5xaiYe3bsBQwvasRYN4g/KL1b0WCqDHkPw8m6A07U+RKjWFf+56IrD1yLzdbwHTxIxYfMZ3NO54s/6q1C3zZsoCE36f4a/3Uejv2o6xv/3AraF6Df9UfDOH1Dr0PtQKnQ44dwNTd/OmmutIIibIgKGzsPNN/7CjaoDIXpYr4XH4eaDcHhp78qASnTphfb6E02HzMlT6ycRGRc/tWagtIM1xtv8jomW/4XDn6MQF/O40N5bq9Hiwy1nkKDSoHHlMujVvlWej1XJxx+nfVIH49e//DVuXThe4HfRDYhbh+02M/Hl67XzPE7oeerq3XHFsgbUOgss3/4PUjSGazl7cPsKqp+YLrdPeQ5G1brZ3yGaV43f/BArav2Ei9qK+GBjMG5k6kbSV2J8LL5fvQKRccnwdnPE1G4+KCgisOg0Yg7aNagJjVaHj7edR+TlA4iPzb75VZWixfxdV7Di0G1YQCun0Kk/am2hByjV/JpjxdCWODm9PdYOaYRu3V7HmZbfIeaDy6j/0d8Gr1siKjwMnMyAuHW61YBpCNOVQQVdGC6tfq/Q3vvE5jkYeO8zeFjHY35vv3wHH036fIQzdk1kLiXF1uH5vmMqJ3cuB6ffRRddZxg8XXI3H1lOLJSWsHpzNV7Xzce6++UxZ8dlg81FF/fz23BCvBxD1nCg4VvkRPDwaa/G8KtQEo8T1Fj83Qrcv3kpV8dISozHtW97YGbsTEyw+wsrB/nD3rpg08GJ3znxu/dB22robHESwxJXQ73YH4FrP8a1kIOIfHAbN88fx75flqDtgn+xZP917Nb4Y031JfAbv91grXZ5UaaEDdp4l0W35g3g1/YtlHJxM9q5EJFhMHAyE+K2/wPlRz4bLLtLpgUojCzbfle+wavK41hQ5y4qlrE3yJd3hSFr5GDiMpoI/PjrXzA0kblc98sgeYfTWdtGaPTGeIMe37OKD6b1SW15W3PkFn49cjZfxxM3ICzfuA1e6puIgQOcBv6UJWO2odhaKbFmSCP0d76Ceeo5sPqxK66HHNTrtU+jI3BtUTfUTQpCgs4GnTr3hGdpw08rlFPwNKljTbzbwU92D7riMQLurED137rD5Xs/VNnSEW0vTkfK43so62iDJf3r4923B8iuMyIiQ2LgZEbsytXEcc/hctsnaCbu37xQYO8lWoJUm4bATqHCOZv6aNorNSmhIZQpVwF32y9HV9UczDlbAttD7hns2GLQ+YWVw1FJexcRcIbHkILppuns645x7aqjpcUZtN/dAcF/r83zsRbuuYqvL5fE4JRpuNVqcZ4HsOemFWTiwF54oPRAWUSj/K+95IBxMSYsJxeP7UTsty1QJzlEBk23Oq6Fd5OMk2YXhjqvdMfJul/guN/nOGPXGI/hKP8zIQLOC9Z+mNGmHA582Aav1vUo9HMjouKB/x0zM/X7z8bFrwNRS30eYesHImHCfnnHl6GdWTUKTTQ3ZS4dt8HrDB581H+lK7rFXsaKAzfw8ZZz8HC0QpNq2U+XkxvHfv4UAU93y7vfIjovRy03TxQUETgdv3gJJR4noc6xSTilTYF/txG5CvKW/XkY3x6NlY979HgTfvkcwK4vFw8vWI89gDMr+sIv6SSaXp2HO3M2Iqz6ALj594BrxZqIS07B5UvnUfLADNRLCJSvewgXxPf6CbWNOEbHwtIKDbq+Byur99Ovo5OFBWoDciEiKkhscTIzllZWKD3oBxnQlE+5iyXrt8oEj4Z0YuvXaBL9u7wD6H7bxXD1qISC8FGnmuji64aGunPw+LkFrp0+lK/jbT9+DW7Xf5Hbp7wno1ZAFxR091HjMWtxyqm9zHLtf3IyAleOh1qVrFc266Cve6NP0ABUVoRhShdv9CukoCmNmN6jzoe7cLz2J7LFxkt7D02vfInLP45D7c92ocmcf/Dx9ovpQdMJ51dhN/aYyQ1s5p1pRFSYGDiZITFdSHi3tRig+QzLbpXDnB2X5DgZQzi7fwsanJ0tt497vYs6LXM/1UduAo+v+/hhusMf8MQjlPu1Dy6fyDifoL42HA/FxF+vordqJnZ5TULTfql3pxU0MYam/tjNOFaun3wccH8tQr9sinMHtmWbqypFrcKpv1Yi7uvG8I/9B86Iw+eNkjGyVVUYg4VSiSZvTgbGn8OxGpNxwboOIhSpU5KI+wDsXSviH4/3ENr/ABqPW//CKXaIiIoDdtWZKZHMb5ClD4I3n8aqw7dQShuNMa82y9f/vo/djMLc/dFYriiF+yX90XTIFyhottaWqPTB77j47auy+9H2r7dwInS6HNCtz88iWnd+/e86TDtXQT7u0rQOOryWGsQUFhE8NR21Aqf+qI9qQbNRVXMT2P8OBh9PQKWa9eQA6hr3tsA2/DQqPQ6EP6Ll68Q8YU+6LUezxh1gbKL1qWn/TwB8glo6Hd5QaeRAcjH5LNDa2KdHRGQyGDiZsZ71y+NJggqb/tyJfkGf48TdTmj47vI8ZSLefyUco34OQpK6POZUWYqFg9sWWhdICSdnVBq3AyHL3pITCIvpKM7c2AnnN+ajYvW6Ob7u8vHdsNw9BW9qbmCPxQRUb/UWJnesabTpK/y7v4fIRl1xbNtsRD28hwPRzjgQmJrt+h/rNahqESa3xYDmyxX7w6/PdLgVwPi0/BLXz8GGfxqIiLLDv45mbkjzyqhxLw5lLsWiTMQWnJt/A26Df4CrR+pkxy8j7qQ68fMM/HrNAkma5mjrXRbzBzSAtZUShUkMcPeb9CcCf/4UDW8uh1/icah/bo0JFTehSR1v+Lg7oZTqARKj7uPJ9eModetPeKtTcxDFwB7vta6Ohh29YWwubp5wGb0SMYkqLLsehZDQx3K6nBtRHRBuo4Bd1Wao9UpPBBRQugEiIipYDJyKgGZ9P0TwTld4B34kbxeP+b4pjtUci3o9x8qpTnJy6cQeWO6eioCUa6hnaY1yvm0x+c2GsLa0MNp4m4DBnyP06pt4vP0jRMSpsP2aGtuvnZPPB9q8Dy9FajeXIKYlCSndBVX6foGGBXj3XF442Vmjax13uaRqYOQzIiIiQ2DgVEQ06DwEt718od46EtVTrqHplS8Q+eUK7K0wDFYB76JSGQdYaRKQePsUYm4eR+k7O+GTckW+NlZnh8t+UzHtjYKdBFVfFWvUQ8WPd+N6WDTGno+UY6/uRsXDJlmNMLgiwrYSEso3Q7UOw9HYrXDvRCMiouKNgVMRIuaC00wJxPFtC+F1cSXcEIGTtyLxw/Ug+Xxri9NYZ/1VenmVzhJnSndC5b5foJEJBiDV3EtjonvqHV6pUhNlprXhEBERFTYGTkWM0tIKTfp8DLVqPM4c3AarSDf4Rlji3uNEWGutcU/hhijbSkjyao2qrQeYZMBERERkqhg4FVFiYlO/9v3gl2GvmCLjQ6TeuE9ERES5xQSYRERERHpi4ERERESkJwZORERERHpi4ERERESkJwZORERERHpi4ERERESkJwZORERERHpi4ERERESkJwZORERERHpi4ERERESkJwZORERERHpi4ERERESkJwZORERERHpi4ERERESkJ0t9C5J+dDqdXMfExBj0kqnVaiQkJMjjWllZsTqMgHVgfKwD42MdmAbWg+GlfW+nfY/nhIGTgcXGxsq1p6enoQ9NREREhfA9XrJkyRyfV+heFlpRrmi1Wjx48ACOjo5QKBQGjYRFMHb37l04OTmxVoyAdWB8rAPjYx2YBtaD4YlwSARNHh4esLDIeSQTW5wMTFzsChUqoKCIoImBk3GxDoyPdWB8rAPTwHowrBe1NKXh4HAiIiIiPTFwIiIiItITAyczYWNjg88++0yuiXVQXPFzYHysA9PAejAeDg4nIiIi0hNbnIiIiIj0xMCJiIiISE8MnIiIiIj0xMCJiIiISE8MnMzEsmXLULlyZdja2qJhw4Y4dOiQsU/JLB08eBDdu3eXmWFFZvdff/01S+bYmTNnyuft7OzQunVrXLhwIUOZ5ORkfPDBB3BxcYGDgwNee+013Lt3L0OZx48fY+DAgTKZmljE9pMnTwrlZzR1c+fORaNGjWR2/bJly6Jnz564cuVKhjKsh4K1fPly1K1bNz15YkBAAP7++29efyN+JsTfo/Hjx7MOzIGYcoVM26ZNm3RWVla6lStX6i5evKgbN26czsHBQXfnzh1jn5rZ2bFjh2769Om6rVu3iqmGdNu3b8/w/BdffKFzdHSUz587d07Xt29fnbu7uy4mJia9zMiRI3Xly5fX7dmzRxccHKxr06aNzs/PT5eSkpJepnPnzjpfX1/d0aNH5SK2X3311UL9WU1Vp06ddGvXrtWdP39ed/r0aV23bt10FStW1MXFxaWXYT0UrN9//133119/6a5cuSKXadOmyb8xok54/QvXiRMndJUqVdLVrVtX/m1Pw8+A6WLgZAYaN24sv6yf5+3trZsyZYrRzqkoyBw4abVanZubm/yDlSYpKUlXsmRJ3YoVK+TjJ0+eyC8YEcymuX//vs7CwkK3c+dO+VgEt+LYx44dSy8TGBgo912+fLmQfjrzER4eLq/NgQMH5GPWg3E4OzvrVq1axetfiGJjY3XVq1eX/wlr1apVeuDEz4BpY1ediVOpVAgKCkLHjh0z7BePjx49arTzKopu3bqFhw8fZrjWIslcq1at0q+1qAu1Wp2hjOjW8/X1TS8TGBgou+eaNGmSXqZp06ZyH+ssq6dPn8p16dKlWQ9GoNFosGnTJsTHx8suO34OCs+YMWPQrVs3tG/fPsN+1oFp4yS/Ji4yMlL+YStXrlyG/eKx+JInw0m7ntld6zt37qSXsba2hrOzc471IdZi7E5mYh/rLCPR8Ddx4kS88sorMvhkPRSec+fOyUApKSkJJUqUwPbt21GrVq304J6fg4IlgtXg4GCcPHkyy3P8W2TaGDiZCTFwMPMXTuZ9ZLxrnblMduVZZ1m9//77OHv2LA4fPsx6KGQ1a9bE6dOn5U0LW7duxeDBg3HgwIH05/k5KDh3797FuHHjsHv3bnnDT05YB6aJXXUmTty5pVQqs7RUhIeHZ/kfIeWPm5ubXL/oWosyovtU3DX3ojKPHj3KcvyIiAjW2XPEnYm///479u/fjwoVKrAeCploOa1WrRr8/f3lXV1+fn745ptv+DkoBKLLX/zNEHdIW1paykUErYsXL5bbaX9L+LfINDFwMoM/buLDtWfPngz7xeNmzZoZ7byKIpHuQQQ9z19rESSJP2hp11rUhZWVVYYyYWFhOH/+fHoZ0f0hxu2cOHEivczx48flPtZZasubaGnatm0b9u3bJ68768E06kWk2uDnoOC1a9dOdpWKFr+0RQSwAwYMkNtVqlTh3yJTZuzR6aR/OoLVq1fLO7bGjx8v0xHcvn2bly8Pd7GEhITIRfz6L1y4UG6npXYQd9SJu+i2bdsm0xH069cv23QEFSpU0O3du1emI2jbtm226QjE7cXibjqx1KlTh+kInhk1apS8xv/++68uLCwsfUlISEi/fqyHgjV16lTdwYMHdbdu3dKdPXtWpiMQd4bu3r2b199Inr+rTuBnwHQxcDITS5cu1Xl5eemsra11DRo0SL91m3Jn//79MmDKvAwePDj9NuDPPvtMpiWwsbHRtWzZUgZQz0tMTNS9//77utKlS+vs7OxkQBQaGpqhTFRUlG7AgAEyJ5RYxPbjx49ZXc/SQGS3iNxOaVgPBWvo0KHpf09cXV117dq1Sw+aeP1NI3DiZ8B0KcQ/xm71IiIiIjIHHONEREREpCcGTkRERER6YuBEREREpCcGTkRERER6YuBEREREpCcGTkRERER6YuBEREREpCcGTkRERER6YuBEREREpCcGTkRERER6YuBERPScCxcuwNLSErt37zbIddmyZQtsbGxw8+ZNXmeiIoBz1RFRsfPDDz/gyZMnGDduXJbnOnfujLi4OBw+fDjD/piYGJQqVUpMjI4GDRogKCgoy2vj4+Ph7e2Ne/fuyWApNjZWBmH169dHtWrVZBBFROaNLU5EVOx8/PHH2bYoHTt2DLt27cL48eOzPBccHCyDJjs7O1y8eBEajSZLmblz5yI8PFxu+/r6wsrKCgqFQh5v69atsjWLiMwbAyciKlZu376NR48eoXHjxlmeW758uWxV6t69e7aBk/DGG28gKSkJ169fz/D8rVu3sGDBArz++uvyccOGDdOf69WrF+zt7eXxici8MXAiomKjZ8+eqFy5styeOXOmbA0Sy4cffoiUlBRs27YN7dq1k91smaV1zQ0dOlSuz507l+H5SZMmoXTp0rKrTxDdeWkcHR3RokUL/PLLL7LViojMl6WxT4CIqLC8++67sovtzz//xJIlS1CyZEm5X7Q+iRYlMbapSZMm2b5WPF+hQgW0bNkStra2OH/+PHr37i2f++eff7B9+3b8+OOPuHTpUpYWJyEgIEB2A4ruOtGNR0TmiS1ORFRsdO3aFRYWFnB2dsaYMWPw9ttvy6VGjRrp44+qVq2a5XUioLp69apsRRKDvWvXrp3e4iRaqsQg86ZNm8pjiZYpMbapTp06GY6RdlyOcyIyb2xxIqJiRbQcibvcMouIiJBr0d2W2enTp6HVatO73+rVq4eDBw/K7WXLlsnB4sePH5fdfuL4IrDK3N1XpkwZuU4bPE5E5oktTkRUbERGRspUAc+PP0ojgh4huzFIaeOb0rrfROB048YN3L17V46VGjJkCBo1aoTQ0FD5HtkdP+24ae9DROaJgRMRFRtpAVB2gY2rq6tcP378OMc76p5vcRItUH379pVjpkQaguePn3l8kxAdHZ3hfYjIPLGrjoiKjZCQkBwDp7QB25nTDKQFROXKlYOHh4d87OfnJ1uOAgMDMW/ePPlcdgHW89KOy4HhROaNgRMRFRtp056kpSR4nhj35OTkhBMnTmTYn5iYiMuXL6NTp04Z0gt89dVXMp/T89nHRYClVCplYJVdck0XFxfUqlXLwD8VERUmBk5EVGxUqVJFrseOHYtmzZrJIKdfv37yTjuxLZJb/vbbb0hOTk4f3C0GhovuuMytSJMnT85yfNHi5OPjI7OLP09MvXLo0CEMHjyYY5yIzBzHOBFRsSECpoEDB8rpT0QQ89FHH8mgKc2oUaPkGCeR5ynNi7rfnnf//n2ZkTy7cuL9EhIS5PGJyLxxkl8ioueIzN9isl7RQmQIaZMCi9YuEUARkXljixMR0XPEfHNi0Hd2kwDnhZjGReR5EmOiiMj8scWJiIiISE9scSIiIiLSEwMnIiIiIj0xcCIiIiLSEwMnIiIiIj0xcCIiIiLSEwMnIiIiIj0xcCIiIiLSEwMnIiIiIj0xcCIiIiLSEwMnIiIiIujn/wHTdAKzrF0X6gAAAABJRU5ErkJggg==", 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", 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", 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ZbGhpbYWfry/UbLKM6og2jLv22KV4KSkpPCjav38/zjnnHLscUwmoxUjhDIUr+LY+do7Tz5065WyYBC1ihCp0NR/jDYIQQpTIw7vvm85w7L/VeR2y79m/4w5QUFAQTjvtNLzzzjtobz96jFJTUxPcEQVGCtbZ3oqhHTv4fsR4538bYLPTDniO5vuG2p1OPz8hhJBje/fdd2G1WjFhwgQsXrwYubm5vAXpzTffxOTJk93y6aPASMGyNy7lSR2rEIqEtLEuKUNnkthSldyx3SXnJ4QQ0rfExERs376d5y269957MXz4cMyePZsnd3zvvffc8qmjMUYK1pX1C98WhUxFhIOTOvYlYcr5QNazyLDloa6mHGHRCS4pByGEkN5FRkbi7bff5jdCLUaKxZbiiK9fx/c9M+a5rBzhMcnI0yRDzdZO2/iDy8pBCCGE9Ad1pSlUXkEu9IIRnYIHhk5yXWDElEedht+to7GtXu/SchBCCCHHQ4GRQv1ersXYrvfxdPS7MHj5uLQsuml34zrz/fhPVTJlwSaEECJpFBgp1F95dbBBjSEZ41xdFIyK9YdeLaCxw4ysyhZXF4cQQgjpEwVGCtRltmBLkZgu/qQhIa4uDnQaNVL8BUSjFvt2rHd1cQghxKGZpIm8n38KjBQob8tyrFTfjqc8v0JquGu70Xpc7rEGfxnuxLA9L7i6KIQQYlcajZjF2mQy0TPrQj3Pf8/1OFE0XV+BWrNWYpiqDsN82iSzBo46KBloBlKNe3niSU9vX1cXiRBC7EKr1cLLywu1tbXQ6XTiMh6DwJYEYR/yRqNx0MeSGpuD6saOy55/dh3Y9VBcYPT888/j+++/x4EDB+Dp6YkpU6bgxRdfxNChQw9rMnvyySfx4YcforGxERMnTuRpzYcNG3bMY7PMno899hjy8/ORnJyMZ599Fueeey6UJKB6A99aE6ZBKvR+EahCCCJUddi9ZTlGzDjf1UUihBC7YF9AWS6gwsJCFBcXD/p47POts7OTf/5J5cutvTiybizQiouLG/RxJRkYrV69GrfeeivGjx8Pi8WCRx55BHPmzEFWVha8vcX1YF566SW89tprWLRoEVJTU/HMM8/wbJ3Z2dnw9e29NWLDhg24+OKL8fTTT/NgaMmSJbjooouwbt06HlgpQVtLI5JN2YAKiBkzF1KhUqtQEjAeEU2/oD17FUCBESFEQTw8PPiirPboTjObzVizZg2mTZvGW6CUxOzAurFrYI9WKEkGRr/++uthPy9cuBBhYWHYtm0bfzJZxPn666/zgOm8887jf/Pxxx8jPDwcX3zxBW666aZej8sew4Knhx56iP/MtiwIY/d/+eWXUIL8rSswUmVFhSocUYlpkBIhbgrQ9AsCa7e4uiiEEGJ37EPZYDhicdgTwMbIsEYBdiylBUYaGdRNkoHRkZqbmw+uBMyw5sqqqireitRDr9dj+vTpWL9+fZ+BEWsxuvvuuw+7j60szAKj3nR1dfFbj5aWloMRL7vZU8/xBnvcjuw/+LbUfxxC7VzGE9VTp9D0acBuINmcg+amenh5+0EJ7HXtpEjJdVN6/ahu8kXXzv4G8j8u+cCItQ7dc889mDp1Kl/cjmFBEcNaiA7Ffj5W/y57XG+P6Tleb2Od2DimIy1fvpwP8HKEFStWDOrxKdWb+bZEFYuqZcsgJbsPFMFTCEakqh6/fP0hPCMyoCSDvXZSpuS6Kb1+VDf5omtnPx0dHcoJjG677Tbs3r2bjwM60pEDrFgQdbxBVwN5DOtqY0HZoS1GsbGxvKXKz8/P7tEs+ydgXX0n2rzY2WXB4q1fQKUyY9IZVyEiPhVS0FO3OafNweLqG7C+pBPp0bNw82ljoAT2uHZSpeS6Kb1+VDf5omtnfz09PrIPjG6//Xb89NNPfKBWTEzMwfsjIiL4lrX0sJkAPWpqao5qEToUe9yRrUPHegzrnmO3I7E3UEe9iQ7m2FtLWvCk+SpE+BmwITlDcrMZWL20Yy7HL0V70FBuwx0K+yBy5OvC1ZRcN6XXj+omX3Tt7Gcg/9+STJDAWnFYSxGbsv/HH38gMTHxsN+zn1mQc2gzI5sJwAZSs6n9fZk8efJRTZOsW+xYj5GTrUUNfDsuIVByQVGPCYniOLEdpU3oslhdXRxCCCFE+i1GbKo+m132448/8qn3Pa08/v7+B3Mf3HXXXXjuuef49Eh2Y/ts3M9ll1128DhXXXUVoqOj+Vgh5s477+Sz2lhOpLPPPpsff+XKlb1208lRRe4OeMAD4xPE4EOKkkK8Md27GJnG7cjZ5YfMsSe5ukiEEEKItAOj9957j29nzJhx1LT9q6++mu8/8MADPEnULbfccjDBI2v9OTSHUUlJyWE5DVjL0FdffYVHH32UJ3lkCR6//vprReQwsrJ8T5V34Am9BSUBrFUsAVLEgto7PX/FGOtqbNgdDFBgRAghREK0cl0Ijn3APvHEE/zWl1WrVh113wUXXMBvSlO0fyuSVZ1oEzyRlCrO3pMqU/REIHs1PGt2uLoohBBCiPTHGJGBq8sSg8BCzwxotNIeRBo0VOw+i+/MgmCzubo4hBBCyEEUGCmEtmwT37aHj4fUJQybhC5Bh0C0oqxgn6uLQwghhBxEgZFCRLaJAYbPEOnPsPPQG1DokcL3q/audXVxCCGEkIMoMFKAhppyRAnVfD8ucyrkoCloJN9ay8RM3YQQQogUUGCkAKV7/+LbYnUM/AKCIQceCeJMQP/GLFcXhRBCCJH2rDQyMNs6QvGz+TKkxYYhXiZPXtSYuThzTRtyVfHYYbLAy4Neio5iNFvR3GmGv6cOBp3GYechhBAloE8jBVhb640/rWfiqZHDIBcR4RGo98tAV7MRu8uaMSlJHi1dcrLz96+QtXMDHq2bA5sA6DQq3BmxBzNGp2P41LNcXTxCCJEk6kqTOZbzaVdZM98fERMAORkdJ5Z3e0mjq4uiKJ3trdj26rkYtfYmnN/yOXyENrAVYjRWIy6pexvDV16JTW9fA1OX0dVFJYQQyaHASOYqykswtXMVkjQ1SI/8O+u3HMwKrMOL2g+Rsv05VxdFMdpbm1D8+hyMbf0DFkGNHZEXYdmd01Dw3Dz8ettE5IfM4n83se57ZL1+NrqMHa4uMiGESAoFRjJXtWsF3vR4G+97vgu9Vl7jRzKCBFysXYURLUdnKCcDZzGbkPfuhUgzZ6EF3siZ+yUm/eNdxERG8kzxCTHRmHj7x9g17QMYBR1GdW7EnnevpCSbhBByCAqMZM5cspVvGwOkvQxIb+KHTYJNUCEc9airKnV1cWRvy6ePYmTnZh70VJ75GTImnd7r342cdQlyT/kPb1Ea17ISm7+mFjtCCOlBgZHM+Tfs5lt1zDjIjbdvAEo10Xy/PGuDq4sja9m7NmB88f/x/b1jn8HQcWKXWV8yp52DrUPv4/sjD7yO0rw9TiknIYRIHQVGMu86STDl8v2wtMmQo1qfNL7tKN7m6qLIltUm4O4/jHjNciE2+c7GuLP+0a/HTbzkIezRj8HPtsl4fEVlvxZvJoQQpaPASMZKc3fBU2VCu2BAbIqYSVpuLOFiuQ21YssXGbivt5Qiq7oDn3tcgJSbPu/341RqNQKu+x6Pq27Fn8VmLNtTRU8/IcTtUWAkY3W5W/i2xCMZao28Bl738EuewLdRHdmuLoostXV04tXfDvD9u05NQZCPfkCPjw0LxI3Tkvj+y78dgMlsdUg5CSFELigwkjFrxS6+bQlIh1yxAdhsEHCbzQO19fWuLo7s7P3+BXxmuRcX+e/HFZNOLO/5DScnYax3Hf7Z8ix2LHnN7mUkhBA5oczXMvYJ5mOhKRLnD5Xn+KKeAdhn+H6BfXU2LKy1YiYlwO43Y2c7huQtQoi6CRcO1UGnObHvOd56Le4fUo5J2VtQvr8EFvOd0Oo8TuhYhBAid9RiJFNsoOy6ah1+s41HVPokyFlKTATf7ikXM3iT/tn109sIQROqEIKRZ9w0qKdtxPxb0QhfRAvV2LX8E7oEhBC3RYGRTJU3daLFaOHrX6WGyyvj9ZGGR/vz7Z6yJlcXRTYEmw1RBxbx/aKh18FDbxjU8bx8/HEg7lK+77/9XUr6SAhxWxQYyVTZnrW4XfM9zg4shodW3pdxnH8rvvZ4Cg8VXePqosjGvg1LEStU8BmJw8+42S7HTJt/D08OOcSaj+ztf9rlmIQQIjfy/kR1Y0Luctyr+w4Xa1dD7lIS4jBelY0koRT11WWuLo4sdG34D9/uDTkdPn6BdjlmYGgkdgecwvdb1onHJ4QQd0OBkUx51u/jW2t4JuTO2y8QFWpxnFFFNiV6PJ66qhKMaF3L90Nm2Ke1qIffSdfzbWbjSjQ31tn12IQQIgcUGMlURIeY8dovcSyUoMYrhW/bS3a4uiiSt/iAETeZ78aPnuchOdO+A++HjjsF67UT8ZrlAizbSwkfCSHuhwIjGWqqq0IExG/zseligkS56wrJ4FtNrdgSRvr2w65q/GEbg/YZT9r9aWLZsPfP+AD/Zz0TX++mWYKEEPdDgZEMlWZt4tsyVQR8/YOgBIZosUswqE1sCSO9y61uxf7KFj4bcV6m2P1ob/NHRkKtAnaWNqGkoYMuBSHErVBgJEPtJdv5tsY7FUoRnjqeb2MtJTB1GV1dHMkq+fV13KP9BucnmhHg5ZgkjGG+BsxM9MTZ6nXI/rP/a68RQogSUGAkQ5oasbupK2QYlCIyLgX5iMZftuEoKq9wdXEkm7sorehT3KH9ARdE1Dj0XDeF7MEbHu8iPed9h56HEEKkhgIjGXpSdTPmdL0Iy3AxIZ8SsLEtD0V8hGvMD2JvEy1H0Zu8Xet4ZuoOQY+MGRc59HoMnXE5TIIWibZiGBtKHXouQgiREgqMZMZstSGnzoQcIRYJSeJMLqVIjxQzeB+oanV1USSpbtsSvj3gO5FnqnYk/6AQ7PMWB/Z71W516LkIIURKKDCSmeL6dpisNnh5aBAd4AklSYv049vCcpom3puIij/41pY61ynXw5winie1g1IoEELcBwVGMlO3ewVe1r6P6/y3Qc2mDinIKK86bNLfghfKF7i6KJJTUXgAibYiWAQ1Uk463ynnTJ5yHmyCCkNRhJqyAqeckxBCXI0CI5kRiv7Chdo1OFmrvHw/CYkpCEEzgtGMuioa13Koko2L+TZbPwz+weFOuR7B4THI9kjvPv/3TjknIYS4GgVGMqNvzOZbW6iYEFFJPL19Ua6J4vuV2VtcXRxJKaysQ5Pgjdb42U49b2NM99pp5fudel5CCHEVCoxkJrQjn2+9Y+W/Rlpv6ryG8G176S5XF0UymjvNeKxuNsZ2vY/IWbc49dyhJ1+HCcZ3cFvjJWg1mp16bkIIcQUKjGTE2NGGaFsl349IGQ0l6goayreaOrFljADr8+pgsQmID/VDfGSoU5+ShNgY2AwBMFsFbMivp8tBCFE8SQZGa9aswfz58xEVFQWVSoUffvjhsN+z+3q7vfzyy30ec9GiRb0+xmiUT5bl0pydUKsENMIPIWExUCJ9pNhF6N8mtowRYPsBtkyKgGkpzg2KeqT7C3y7LsexSSUJIUQKJBkYtbe3Y+TIkXj77bd7/X1lZeVht//+9788yDn//GPP1vHz8zvqsQaDAXLRWCR2L1V4JPCEiEoUnDiSb2PMxbBZra4ujiRcnXU9/tLfgbkhtS45/yTvKnymexbX7L7cJecnhBBn0kKC5s6dy299iYg4fPHMH3/8ETNnzkRSUtIxj8uCpyMfKyftdaV8+nSrv3LWSDtSVNIwvixIni0Sp9Q1IiY8BO6svGA/z3Zthgb+w8Wg0dkiAnwxqXw/tIKNpw2ISkxzSTkIIcRtA6OBqK6uxtKlS/Hxxx8f92/b2toQHx8Pq9WKUaNG4emnn8bo0X2P1enq6uK3Hi0tLXxrNpv5zZ56jnes4y5UnYObuybgyWEJGGvn8ztSf+p2kEqNJwOeQ05NGyJrTQgPMiurfgNUuvVnRAPI9UhDiqePQ85xLOx8Wr0ncnTpyLDsQ8mW/yE0JhlK4chr52pUN/mia2d/A/kfVwmCIA4gkCjWyrNkyRKcc845vf7+pZdewgsvvICKiopjdott3LgReXl5yMzM5AHOG2+8gWXLlmHXrl1ISel9aY0nnngCTz755FH3f/HFF/Dy8oKzPb5Ng2aTCncOsyBJTBKtSB/nqLG9Xo2z4qw4JVrSL0+HC9n9Nk6ybsb/vM6HbejZLiuHOvtHzO9YjPWa8agdcbvLykEIISeio6MDl112GZqbm/mwGkUHRmlpaZg9ezbeeuutAR3XZrNhzJgxmDZtGt58881+txjFxsairq7uuE/siUSzK1as4HXR6XS9Ttke99yffH/7IzPhazj6b6TqeHU70jurCvB/v+/BJRme+Oelc6C0+vWX1WJB14vJ8Ec79s39FqljZsJVdUsK1SNz+cVogRd0D+RBq1PGQr+OunZSQHWTL7p29sc+v0NCQvoVGMm6K23t2rXIzs7G119/PeDHqtVqjB8/Hrm5bMZP7/R6Pb8dib2BOupNtK9j1+xah288nsQ+XSaCfM+AHPX3eZuKnbjLcCPyipOh08mnrvZ+XRRnbcIQtKNV8MTQMTOgdeEHd8ro6WhZ7gU/dCA3extSRk+Dkjjyf9rVqG7yRdfOfgby/y3rqU0fffQRxo4dy2ewDRRrKNu5cyciIyMhB23FOzFBnY1xWuWvWRWeIA7ujbaUuvXMtLp9YgthgVemy1toNFotCj3FpKL13eUihBAlkmSLERskzcYD9SgsLORBTFBQEOLi4g42i3377bd49dVXez3GVVddhejoaDz//PP8ZzZWaNKkSXw8EXss6z5jx3znnXcgB7YaMeFhu59yBr72JSpxGLoEHTxVJpQX5yA6SVyvy92sbk/APstcJMRPhhS0xM7CH/u7sL/FH5NcXRhCCHGnwGjr1q18+n2Pe+65h28XLFjAEzUyX331FW/1ufTSS3s9RklJCe8u69HU1IQbb7wRVVVV8Pf357PRWCLJCRMmQA48W8SEh6pQ5U7VP7R1okgbg2RrIWoLdrplYMRe299URaDeciUWT5ZGYOQz9SacuzsT/rU63GwToFarXF0kQghxj8BoxowZ/IPhWFiQw259WbVq1WE///vf/+Y3uQoxFvOtT/QwuING72SgpRCd5XvhjvJr21HfboJeq0ZmdACkYHi0P7w8NHwiQHZ1K9IjFTw1khDitmQ9xshdGDvbEWkTl2MIS1bm4rFHsnSvmaard8810/J2rcVk9T5MjDHAQyuNf1OdRo2x8YGIQD3279nu6uIQQohDSOMdlxxTRf5evkZaC7wVu0bakfRR4pppAe3KH2zem5A9H+FLj2dxi/Z/kJIbPJZjo+F2JOzqfWwfIYTIHQVGMlBVVYlKIQgV2jjFrpF2pJCUCfjCMgufm6bDZpN0qi2HiG7ZybfeqSdDSkJTxTF5Ce07Idhsri4OIYTYnXt8ysrcZmRgctfbWJTS+6K6ShQVn4InhBux0Hwqyps64U6qSnIRiVpYBDWSRjs/qeOxJI08mc8YDEILygr2ubo4hBBidxQYyUBeTRvfJkYEwl1o1CokhIjLruTXivV3F2W7xTxBhbpkePtKY+B1D73BC4Ue4hI6VXvXuLo4hBBidxQYyWSGEpMc6gN3khasQ7qqGHXFWXAnlpItfNsQOPDEpc7QFCSWy1a62dVFIYQQu6PASOKsViveb7gOn+meRaqvCe7k0q5v8Iv+IcRl/xfuJLBhN99qYsdDijwSJvJtSJNYTkIIURIKjCSuqjgX8apqvhxIVEQE3Ik2TExm6d1aCHdhMnYi0SxmfY/ImAopis4U10lLsBSivbXZ1cUhhBC7osBI4uqKxG/l5Zpoly4i6gr+sWIyy3BTCdzF/uoOnG96As+obkR0kpiyQGrCY5KxUHMB7jLfir0V7jX+ixCifBQYSVxnxX6+bfBKgLuJ7E5mGYImtDbVwx3sLG/FHiEJeXEXSjo1w9akW/E/2xRsrXCvGYOEEOWT7jsv4dT1OXxrDhjids+Ir38QaiHOxKss2AN3sKOkkW9HxUprNtqRRseJ5dtR0uTqohBCiF1RYCRxvm1i5mdtRBrcUY1HHN+2lLpHzpzx+W/hEs0fGBuhgZSNjvHFBNV+pBV9SokeCSGKIslFZMnfIsylfBsQK83xJo7W5psI1O+CuUZsOVOyproqXG5eDOiA5sgHIGXDIrzwmcdz8BCsKC9i46HcY3FjQojyUYuRhDW1tCDHFo0aIQCRScPhjhrjTsML5kuwRiXNqev2VLxnLd+WqqLgHxwOKTN4eqNIl8z3q/ZvcHVxCCHEbqjFSMIKm6y42PQ4wv302CSxDMjO4pk+G+9v8Edqqw8ehLJ1FIqJHat9MxAL6Wv0zwDqc2Au3e7qoshGef5elP3yKqLrN8BPaEa9OhTVUbOQdvaDCAiNdHXxCCHUYiRtRfVixuuEYG+4q6QQse5FdR2wKnwxWUOdOMDcEjEKcqCKEsvp0+ge478Ga9G6AtR+sgAT675HjFAJP3Qg0VaMSWULoX5nLHat/NLVRSSEUGAkbYU9a6R1BwfuKDrAE+naSswUNqKiQhxvpVSRHeI4Kr/EcZCD4BQxA3ZcVw4NwD4GQRDwwi8H8MTP+/GY6Wrs1o/FzpM/QNElf2LruFdQoE6AH9oRuvZh/LQl21mXjxDSB+pKk7CT9z6Ci/Q7ccB6L4ARcEdqtQrveLyFJFsRduWkIzZGnKWmNI21FYhAHd+PzRADDqmLSxuLLkEHP1UHygqyEDPEPcfBHc8Xq3fj/dVlfP+M0+chc9ptUKlU/OeEtDEwzb4Sf/znPjxRNgZl3+fB1y8QM4eGubjUhLgvGnwtYQEdRYhR1SE4wA/urLE7uWVnxQEoVWn2NnGriuL5m+RA56FHsS6R71dnb3R1cSTpwOYVOOvP0zBXvQkPzU3DP6YnHwyKenjoDZhx81uYMGYsWG/x3V/vRHkTJc4kxFUoMJIowWZDhKWc7wfGumcOox6mQDG5pbpBXENMif6yZGCk8UN8HPcM5GR1/O04u+sp/C7Io/vPmYwdbfD+5Q74qjpxXcg+3Dgt6Zgto8+eOxwjov0wyfgXCj68kronCXERCowkqr6mnL+h2gQVIhLS4c503YvJ+rQVQan2VjSjGT4ITpTHwOsevkNnYJcwBDsrja4uiuTs/PwRxAoVPHt7ytXvH9VSdCS9VoN35kfiDd07OLljBbb/8pHTykoI+RsFRhJVW5zFt9WqEJ4zxp35RYstZqEmcZyGEu0rF1epHx4tr27TzBh/vt1T3swHGRNRdVkeRpV9zvdLJz8N/6DQfj01sQlDsD3+Or4ft+VZdLa30lNKiJNRYCRRreXi7JQ6vRwy2jhWeIKY9TsUjWhvVd7aXC1N9Xi65VH8U/sFhkX4QE5Sw31xoW4dHrR8gLJC5Wcn76/i7x6DQWVGlm44Rs++fECPHXPZv1ChCuOv911LXnVYGQkhvaPASKKsdbl82+ErDjx2Z+zbdiN8+X5VkfIGYJdmbcLJmr04R7sRQb6ekBOdRo0b9CtxhfZ31OwXM3e7u4rC/Rjb+AvfV895Cir1wN5m9QYvlI24g+8PzfsP2lrEhYUJIc5BgZFEFZsCsNWWClMoTYFmPvW9HreY7kC+SXkZwFuLxBlplV7iWCq54RmwAcqA3W3rn0vARhPtNoxD2vhTTug5HXPmTXyGYiBasfenN+x3sQghx0WBkUQtsszGBaYnYBl5pauLIglFMedgmW0ScluUl3pLU72bb40hmZClCDHHlleT8lrzBqqx3YQHC8fgVNPLsJzy1AkfR6vzQOXwG/l+fN5nsJhNdiwlIeRYKDCSIJtN+Hs5EDfOen2o+O5lUYrqxOdFSUJaxfFknnGjIUcB3TPpooz5cHdfbC5Bp9kKQ0QaRo+bMqhjjZh7A37HBDxiWoCVB2rtVkZCyLFRYCRB1c1tsJpN0KhViAmU15gTRxniZ8Gp6m2IKlsKJTF1GRFjFWfbRaTKMxdQbNo4nlYiBE2oq1L2si3HYrNa8dumXXz/2qmJx52efzwGLx9sn/wW/rCNwX//KrFTKQkhx0OBkQTV71uFA/oF+MbzeT64lQCpulr8x+NVXNn8gaKejrLcXdCprGiBF8JjkiFHXj7+KFeLK8NX5myFu8pa/zO+77web+g/wBmZ4vMxWFdOSoBaBWwuakBBrbh2IiHEsehTV4LaK7OhUQnQehhcXRTJCEscxresVUJJs3Qqy4vQJHijXJc44NlLUlLrLWYnby7bD3dl3PwxtCobIkIC4emhscsxI/wNODdJwN3a71D488t2OSYh5Njk+06sYEKduPRFJ03VP8g/IBgNEJMfVhWKyS+VYI1tBEZ1fYhv0/4NOduVfj9GGT/AYs08uKPmhlpktqzh+0EnX2/XY18eU4s7td8js/gTWC0Wux6bEHI0CowkSN9azLeqYPFbOBHVaKP5trlcObOfsqtYZmMVEqMjIGcxiUPRBF/sr2yBO8pZ9QX0KjMK1fEYMuIkux572MyL0QQfhKEB+9b9YNdjE0KORoGRBAUZxYGWXpHyzGvjKK3ecXxrqslTWGAEpEeICSzlKj1SLH9+bRtMFhvcjT7nJ76tiptn9y5RlvAxO2QO3+/a8a1dj00IORoFRhKc2RJhreb7wbHuvXjskSwBiXyraSyEUrpfvu68CR/qXkVqmLxnH0YHeOJ+ww/4SP0cSrK3w5001VUho1Osc8xJlzrkHL5jL+Lboc1r+UxGQojjUGAkMXWVxbxJ3ixoEBaT5OriSIouTOxa9O0Quxrlrjx7K+LUtRihKYGfl7wDIzY1faYuC9M0e9CYuwnuJGf1l3zQdb4mCbEpIx1yjqHjZ6MOAfBDO/av/59DzkEIkXBgtGbNGsyfPx9RUVH8DfeHHw7vV7/66qv5/YfeJk2adNzjLl68GBkZGdDr9Xy7ZMkSSE1FUzuWWE/Ceu0Env2W/M17yDTcbroNT9quVcTT0loi5ryp8pTnNP0jtfoP5Vtr5R64k0X1GXjEfC3yUu076PpQGq0WeSGz+H7Xru8ddh5CiEQDo/b2dowcORJvv/12n39z+umno7Ky8uBt2bJlxzzmhg0bcPHFF+PKK6/Erl27+Paiiy7Cpk3S+nab2xWEu8234j9RT7q6KJITE5+E/9mmYGN7FFqNZshetTi7rjNQDCjkThUpLmni7UZLg7QYzVheZMXn1lMx9JSrHXoun9Hno0XwQk4TYLa63zguQpxFkgtPzZ07l9+OhbX6RET0fybP66+/jtmzZ+Ohhx7iP7Pt6tWr+f1ffvklpKK0sYNv44O9XF0UyfE16BDi44G6NhOK6jqQGeMPOfNvzeFbXZQyFgoOSBwN7AGiu9xnaZC1OXWw2AQkh3o7fPme9ElzMfWPj1DZLiChoAFTU0Icej5C3JUkA6P+WLVqFcLCwhAQEIDp06fj2Wef5T8fq8Xo7rvvPuy+0047jQdGfenq6uK3Hi0t4lRks9nMb/bUc7z66nJoYEW0v8Hu53CVnnrYoz6n+hbDq3Mn6rLVMIefCrnWz2a1IcZUxGbqIzB+pGSv9UDqFp4oBnhBaEF1RQmCQu2T/VnKr03zujdxlaYdAYkXOOUaThkaicXbK7AyqxITE/yd9n8nNUqum9LrZ3ZR3QZyPlkGRqw16cILL0R8fDwKCwvx2GOPYdasWdi2bRtvSepNVVUVwsPDD7uP/czu78vzzz+PJ588uktr+fLl8PJyTIvONUUP4Dl9Eb7OuhfLWmS62nofVqxYMehjnN76A2bo1uKnbR1Y1maSbf26WmpxkaoTJkGD3bll2FvQ9+tQTnUbJ4QiWlWLVf/7ki+kquTXps1mw7Tqz3COrgVf10Zh2TLHZ2T3a2Prr6mRtWM9lqHAaf93UqXkuim9fiucXLeODrE3RrGBERsr1GP48OEYN24cD5KWLl2K8847r8/HHbmooyAIx1zokXW33XPPPYe1GMXGxmLOnDnw8xOzMNszmmUvlAjU8hkuE6bORELGBChBT91YV6ZOpxvUsbbUrwdK1iJS24pR8+bJtn6bN6zBztxkeOh0OHP+WZCqgdZt34F34N3RgdCQQJwskevjqNdm7vZVCN7VglbBE/OvuA06j96/lNnTyR1GLHhtHBJQheKhqxGVLC6V4+j/O6lRct2UXj+zi+rW0+Oj2MDoSJGRkTwwys3N7fNv2HikI1uHampqjmpFOhRrfeqtBYpdTEdcUEtXBwIhJvyLTMxQ3D+EPZ43j/AhQAng01kmuednIPXbZYvHS6ancXZ6JN6QWD0GU7ffh72It9aV4wpNPGbJoF6DeW227BEnfOT6jscYbx84Q5C/DhX6CMBUhcrtPyM+bdRxH+Oo9yspUHLdlF4/nZPrNpBzSXJW2kDV19ejtLSUB0h9mTx58lFNd6xLbMqUKZAKS2sN39bDHz5+ga4ujiT5dWcDDzFXQs7yqsWV0lPC5Z3x+kiJUaF8iZOc7vopWUjlar61JItZqZ2lLU6ctu9d/LtTz0uIu5BkYNTW1oadO3fyG8PGEbH9kpIS/rv77ruPD6YuKirig7BZzqOQkBCce+65B49x1VVXHZyBxtx55508EHrxxRdx4MABvl25ciXuuusuSIWqs5Zva7VRri6KZIXFiVPbQ9GIznaxdU2OCmqa+XZImHNaGpwltTvQy61u5V3VSs52PcQqzr5LmnS2U88dOV4831Djbln/DxAiVZIMjLZu3YrRo0fzG8PG+bD9xx9/HBqNBnv27MHZZ5+N1NRULFiwgG9ZoOTr+/e3bxZEsfxGPVjL0FdffYWFCxdixIgRWLRoEb7++mtMnDgRUqHvFFuM2rzExVLJ0fyDwtACceB7dYk43V2Oy758XHcplnvcj6He7VCS5BAvvK/7N5ZYbkN9dRmUqmDrr3xbqI5DSKS4hp+zxKWMQBVC4KGyIG+bcgfnEuIqkhxjNGPGjGN+2/ztt9+OewzWknSkCy64gN+kytckBkZm/wRXF0Wy2AKdNZpI+Fnz0VSeC6SPhdzUlBcgQtUOTxihio6FknjqdRiuLUWMUI29eTsQEqGs+vWoKcnhy/bUBE+AuIKfc/8HSvzHI6L5F7Tv/wOY1veEE0KIQlqM3NU2IQU/WqfAGim/D3tn+iHiDpzd9RR2a+WZzqAmX1wKpEIT5ZSZTM5W6ymu8ddWuhdK9VLrHIzs+j90TPp71qozqZKm821w7UaXnJ8QJaPASEK+NE/HnebboM843dVFkTRzzETsEoagoP+zLyWlo0JcCqTB09ltDc5hDEjhW1WdMpcGqWo2oqC2HUaVAWPSxckAzhY/bh4+tJyBZzrOQ3OH8pIAEuJKFBhJBFv7qLE7yXZ8EC0Hcixx3c9PSUP/E3ZJiboum29NgWIAoTS6iAy+9WvJgxJtyBcnSQyP9oe/l2umUodFx+OrwBux2jYSGwvrXVIGQpSKAiOJqKxtRLSqDux9NtRXed0r9pTkZcQCzW84qXIR5Mi3TcxYrJVRZuiBCEwYwbdR5iIINuUtdhq15p/40eNRXBEsBriuclKyuFba+rw6l5aDEKWhwEgiWnPXYJ3+TvxP98gxs3ETIM7bhid1H+Ny49d8hpecsEAhylzM9wPjxQBCaaJTRsIqqOCPdtRXlUJpYps3Y6S6AGkRrl3EeGqSL6aq9yA66/9cWg5ClEaSs9LcUWeN2IrQwrLakmMKi0mCRVDDoDKjpqoEYdHyGatT19yKDdYRGKKqQFKyuOiq0hg8vZGjSUSnBbBUVCAkKh5KUVteiCihhgd+SWNPcWlZJkUbcJrH80AX0FB9P4LCY1xaHkKUglqMpKKxiG+MPsqc3mxPbCZXtZplWAbqSl3bnTFQeQ0W3GG+HTf7vgGDl7KSOx7q1YQPcbbpGewyKivQL931J98WapNcnp3ePzgchWox6CzuLhchZPAoMJIIQ2uJuBNIOYz6o8FDzA7eVimvAb55NWKm4iGhyg2KmCHdGbDza5W1NIipcD3f1gUef40yZ6gJFJPgduX/5eqiEKIYFBhJhL+xnG8NYWIOGHJsHd5iy5qtXuyClIuyigqoYcOQcGUHRsndgV9BjUxzKvQhuGEH32oTJkMKNPGT+Dawfruri0KIYlBgJJEBuRFWcfkS/yjX5EWRGyFAbFnTtogDmeVifs5DyNJfg2nWTVCydH09X/LktcqroBTtrU1ItIiBeMzImZCCqBHigrJJ5jxaN40QO6HASAIaasvhpeqCTVAhLFaZuW3szSNUbFnz6RBb2uQivKuYDxoPiVTOgOTexMTEIlVdjkjUoaVJGXl2sgrL8YttAvaqUhEROwRSEBmXghoEQaeyonDXGlcXhxBFoMBIAsqauvC25Wx8h1OgN3i6ujiy4Jk6E+d0PYVbhfshF82NdQhFI9+PGjISSubrH4RaiIOTqwr2QAk21HrgdvMd+CD1Q0gFXzfNV3wtNeWI458IIYNDgZEEeAdGoHrc/VgefLWriyIb0dHR2CkMQX67Ae1dFshBVf5uvmXf8FngoHQ1HuKq880l+6AEW4vFoHZcvGtnox2pbPitmNP1Ij60znd1UQhRBAqMJGBImA8ePyMN8+OVlyXYUfwMOgR0L8cgl6VBWsrFtcNqPdwjJUObr5hfylIjr5QKvWGJROuL2Rp3AsZKLDAaMnwCcoRYbCtphtUmuLo4hMgeJXgksnWJ93aEmHaiOVcDREp/4V1LbS7ftvm6R0oGISQVqAcMzfmQu9LcnViquhOV+mCEhovXUSrSInzh7aFBa5cFOdWtSI/0c3WRCJE1ajEisjUbG3G99hegVB4zvDyaxBlNQlAy3IF3VDrfBnXKa+Zgb2oObODbJo8IaLUaSIlWo8bVwVl4Vfcu6rd85+riECJ7FBgR2TL5imNYVN1Zw6VugzUdy6wT4BE7Fu4gNGkEsmzx2GGJh9kirzXtjmQr28a3zUGZkKKphgKcr1kHXRFlwCbE5V1pWVlZ2LdvH2pqavjip6GhoRg+fDjS08Vvi4Q4iiY4EagAPNukv1CpzSbgnbYZ6DSfjD8zZsAdhEUnY6bwEjrNVoxo7ESSjLN9Bzbt5VutRINaffx4oPJThDSL5SSEODkw2r9/P9577z188803qK2t5fcJgjjor2dl+JCQEFx00UW4+eabkZGRMYgiEtI7r4ghwB4gsEv6uYyqW408QNCqVYgJdI+UDGq1Cokh3siqbEFBbbtsAyNTlxEJ5gJABUSknwQpiho2FdgIxFuKeKJHT29xSRZCiIMDo6KiIjzwwANYvHgxPD09cfLJJ2Py5MlITk5GcHAwD44aGhqQl5eHjRs3YuHChXj33Xdx/vnn46WXXkJCgnsMOiXOERwjZgkPt1XDarFAo5XuXILisgpEoB5egbHQadynBzs5zAf7K5tQXFkDZIRDjor3b0GKyoIm+CA6UZot4eHRiTxvVKiqEXl7NyBt4hxXF4kQ2RrQJ0laWhrvIvvvf//Lgx0fn2N/A2xra8N3332HN954gz+us7NzsOUl5LCuGrOggYfKiuqqYoTHSHdQs2rfYmw0PIttwlQA0lhOwhnOtfyCl/T/xp49pwKnfAk5qs/ZCJaPvsQwFCPU0gxqWaLHMq90hHasR1PeRoACI0JO2ID+yz///HPs2LEDCxYsOG5QxLC/ufrqq/lj2GMJsSfWQlSjDuH79WXSmkJ9JKFOLJ/Z1z1yGPXwDwqDp8oEv3Z5DJDvzUZjPN61nIXy6DMgZcYwMQO2rkpc6JYQ4oTAiLUSHerBBx/k3Wb9cd555w2sZIT0w79Dn8E443vYrx0m6efLq6WQb1Uh7rUWXkDccL6NNBfzxZLl6Jf6CLxkuQSaMZdDynySJvL1Fk3tza4uCiGyNqh24ZdffhmbN2+2X2kIGSBNWBrq4I+yJqOkn7ugLnHmnG90GtxJVNIw/mHtj3Y01lVCbjpMFuTWtPL9kbEBkLK40XOQ2fUfXNpxLxrbTa4uDiGy5dAO8y+++AKxse7VdUCcq2eGV2mjdJcFMRk7EWmr5vthCWILirtgs6OqVSGyXUw2JzsLJ6t2IcXXhHA/A6TM39cb4SHic72rrMnVxSHEfQKjN998E/Pnz8fTTz/Nf25paenzb61WKyoqKgZXQkKOIc2jBo9oP8Pk4vcl+zxVFh2ARiWgXTAgOML9vijUGuL5tq2MrTUmL8Y9P+Jjjxfxku5DyEFPq9auUupOI+REDXh+c0BAALZu3YqlS5fynEW33norHn30UYwePRpjxow5eEtMTMT69et5PiNCHCVG34HZ2mWobAuV7JPcULIPLDSo1EZjiERnNTlSJ1sbzrgVtrr+jUeUEk21mDDRGCKPlr5TDdm4wuNlmHfGAdO/hrsqyd6OyrWfQN+Yg1VBF8M/bRrOGx0D/+6Fpwmxa2B01VVX8RvLaZSUlIQzzzwTarUa27dvx++//35YkkfmyiuvHOgpCOm3kBhxMHOYUAeLqQtaD73knr08czA2Wc5EeGgshsD9GCPG4ZeqYlRaYzEJ8hLUls23hhhxxpfUJYT6Ypg6F1Vt7tmVxrqtd350G8bVLEacSkw6fH/D2cjNzcJry3Pw+JlDceG4ePYh5eqiEgk74Yx4LFnj2WefjZtuugmnny6ubF5fX49t27bx6fkFBQU8cLrjjjvsWV5CDhMcHosuQQe9yoyK8kJEJUpvcPM2YzS+slyGO1Pda0baQZkX4OYtcUjp8sG1kFfG61hLiZjxeuh4yEFsxkRgORCBWtTWV8GddLQ1ofiNeZhg3sev2Q7PyTDGT8e5YdPx464a5FQ3Q/PTrdi0PQETbniL534ipDeDShW8ZMmSw35m2a/nzJnDb4Q4g1qjQbU6DHFCORrKcyQZGBXUtfNtUqg33FFisFjv4oYOvmYcWypEDkoObMMQlRXN8EZErDyCWr+AYJSpIhAjVKHiwFa3WSectRbnv30uMs370CJ4o2D66xg96yL+u8kAbpoxFEu+/wLn7V0HVK7Dpo+9MfGal1xdbCJR7vFfQxStSR/Jt501Yq4gqfGt2YZQNCGpO0BwN1EBBug0QIClHhX1DZCLhoJtfFvqMURWrQvVXuJSOR0l2+EuNn36GDKN29Eu6FFx1ucY1R0U9dCoVbjggsuxPu1h/vPE4g+w+/cvXFRaInUD+m9fuXLlCZ9oMI8l5Fg6vWP41togvezKLU31+Mj6CLYYbkGCnzwTHA6WVqPGMv2j2Gy4FY3710EubBW7+bYtUJrro/XFFCoOFNfVigPHlW5LUQPuzRuBddZh2D/heaSN7XvJnSmXPIgNIRfy/Zi1D6KplmZNk0EGRnPnzsX06dN5F5rZbD7u37O/YX/LHjNv3ryBnIqQfrP5x/GtulV6b3LVhfv4tg4B8PUPgrtqN4gLyHZUioOZ5eBLzMH95hvRkXI25MQrbgzfhrbnQOksVhseXbIXVUIglgx/F+POuO64jxl93ZsoVMchCC3I+4zGwJJBjjHauXMn7r33Xr40SGBgIE455RRMnDgRycnJCAoKgiAIaGxs5MuEsIzYf/zxB/+ZjTlijyXEEZrTL8X4rDTEhyXgO4k9xc3lB/i2RhcDd05cYWRT9js2QKiXx5R99l72R40/Wq0zcO0wtvCvfERnTETl6iAcMEegy6LsVsrv1+9FdnUrArx0eGx+Rr8eY/D0gumMt2D76RyMa16B3O2rkDJmhsPLShQaGA0bNgy//vorNm7ciPfee4+3Bn333XeHTc/veVPx8/Pj66PdfPPNGD9eHjM6iDxFREShFoXQSHBZEHNtPt+2eYutWu5KFTwEqAY8W6XX3dmbssZOtHZZ4KFRY0jY8RfMlpKQiDiM9/g/1LZ24a5OC5TKajJi3pqzYdCNhHnmiwjw8uj3Y4eOnYHNq+cguXk9fli7DfeNnn7U5xhxXyc0K23SpEkYO3YsrrvuOnh6emLfvn2ora3lL6zQ0FAMHz6cJ3xk+Y1OxJo1a/g6bGzqf2VlJQ/AzjnnnIPdcyyh5LJly3hKAH9/f5x66ql44YUXEBUV1ecxFy1ahGuuueao+zs7O2EwSDvVP+nfsiDVrUZ0WazQazWSecq0TWIgYAtIgDvzjkoFsoAgo7hmnNSV7luPKzXL0RQyBjqNfAZe9xgW5YdV2bUoa1fuh726+A8EohUTtAWInCQOOB+I2EtexWnvbkJdpSdOyq/HlCHu3KZL7DJdX6vVYtasWXj++edx//33w57a29sxcuRIHsiwbrtDdXR08GSSjz32GP8b1lV311134ayzzuIZuY+FtWJlZx8+xoGCIvkL9vbAgx7fIUEoRU3JEMQmDYVU+HaU8K0uNBnuLCRe7OaIsFXDYjZBq+v/t3tXUGcvw9O6RdisqgGwAPIMjGpQ3ya9VlR76Opow0mtv/J8RZWZ/0DsCbyeIqNiccb4Zny8oRjvrMqjwIgMPjBirUORkZG8xcje2CBvdusNayFasWLFYfe99dZbmDBhAkpKShAXF3fMMkdERNi9vMS12HWdq92CBFspdpdmSSowCjWLA8L9WYuJGwuLSoRR0MGgMqOsOAcxQ6S9xIahQVzXzRaeCTma6pGHa/Q3o641GMC5UJq9v36ISaomVCEEo8646YSPc8O0JHyxqQheBb/hwA4z0kafbNdyEjdM8Mhac9iYo9tuuw2u1NzczD8c2Tpux9LW1ob4+Hi+uO2oUaP4Qrisy68vXV1d/NajZ8Fc1p3Xn1l5A9FzPHsfVwqcUbcmjyjAWIqO6jynP4d91a/daMZr5vMQr6rGBTGpsry29rx2v+pPQ3WHgIzGLoRL5Lnoq34RHbl86x07QpbXLSomDiGqFvgKHehob4OXt7zGSR2LYLMhLPtzvp+fdCWC1ZoTvkbhPjq8G/kLZtd/hm1/7IB5uDQWraHPA/sbyGtEJbCR0ifowIEDfOwP6/J68MEH4Qgs4Dl0jNGRjEYjpk6dirS0NHz22Wd9HocNGGez5TIzM3mA88Ybb/BxSrt27UJKSu9ZbZ944gk8+eSTR93/xRdfwMvLaxC1Ivbmue9TzDGtwDLDfJjTxTwlrlbeDry0WwtvrYDnxlvh7j7KVmN3gxrnJVgxPfKE33YcztzVjguybub732W8B51efok5BZuAU3b+A76qTnyd8BwMgWKuLyUw1uTi4vKn0Sl4YOmwNwZ9fYx1Rbi49HGYBA1+Sn8dOk9/u5WVSAcbhnPZZZfxhhQ2rMZhLUYZGRm8K+3hhx/Gzz//jAULFmDmzJl8+r6zIsBLLrkENpsN77777nEHjLNbj5NOOgljxozh3XBvvvlmr4956KGHcM899xz8mQVUsbGxPP3A8Z7YE6kL6yKcPXs2dDplrQDtjLptadkO5K9AmLYNI52cM6uv+v22rxrYvQtDIgIwb95EuPu126fNwe61RfAOT8C8eemSrV/2lpV8oDjrpjn7XGkE2Scib18C0i37kRCgwhgF5ZHb/tYVfLtRNwnzzjzHLu8pB178HGmWbER37se485+Cq9Hngf319Pj0x6ACIzbomuUnYovG/vXXX/zGWnhY0MC6qljgwWavsSjNES+ciy66CIWFhTxf0kADFTZjjqURyM0Vm8x7o9fr+e1I7B/RUR/wjjy2qzmybp5hSUA+4GuscNnzd2T92iuyMFaVjdTAsbK/pva4dsmhPghBMyxVrdDpRkCq9WsvF5NyVhsSESHj69bslwI07Ie1er/sX389mjvMuLXhApwphCE1NtVu7yktw68Cdj6CxJJvoVE9DbV2UB+NdkOfB/YzkNfJoK7+iy++eHC/vLyczxZjQVLPbfXq1TxQsndg1BMUsaDmzz//5IvXDhTrQWRBHetaI/LnGyG2UgZbqiEVCXmfYrH+J2wwXg9gOtzdcOt+bDXcjIqaMACXQLKqxYHXnQHyHjAvhGYADT/Bq0k+2caP59d9laizeOGvsPMxKrjZbscdcdo1aNn5HCKEWmRt/hUZU86027GJ/NgtLI6Ojua3+fPnH7yPTaVnAdJAsUHSbDxQD9YqxIIYll2b5Sq64IILeBDGuu/YQOqqqir+d+z3Hh7itM2rrrqKl4elE2DYWCHWlcbGE7EmNdZ9xo75zjvv2KH2xNVCY8WZaD5COzo72uHp5fpxId7t3VP1Q5JcXRRJCE8Qp+yH22phMnbCw2D/Ga328KH2ErzSNQzXp0+AnPnEjQCygYhOMcmoEvy4U5zledaISKja7RcYGTy9sStwFiY2/g/tW74AKDByaw7NXMaWDWG5jgaK5SNis8V6ZoyxcT5s//HHH0dZWRl++uknvmXddSxlQM9t/fr1B4/Bpu6z5JA9mpqacOONNyI9PZ2PEWItXCyRJJvmT+TPLyAYM4UPkN61EGWt0lgGIaSrnG99IuXd8mAvweExaBcM0KgEVBaJS6VIDWtJ3l4DbBHSED1EWt19AxWVMgZ/WYdhqWU8Wjr/nl0rV7Xlhbiz9E5cpvkdZ2SKa+/Zk/f4y/lW15gLo0m5GcPJ8UmjI/UIM2bM4G9QfenPRLpVq1Yd9vO///1vfiPKpFKrYQiKhlDZwpdzSAn3dWl5uowdCBPqeAK60HhpDDSWwjWq1EZjiDUfjaX7EZ/Wd6oMV6lrM6Gxwwy2OoTclgI5kl9gCG5VPYwmswojq9sxLuHo8ZJykr/qE0xSH4CfXo3YIG/ssfPxMybOwZV/voq1rRF4P6cWpw+PtPMZiFzIL9c9IcdZGqS0scPlz1FVcQ7UKoG3kASF9r1Ujbtp9ozlW2O1NFd+L8vaiEe0n+EK/90w6KSztMyJivISv0QeqGqF3AUV/sy3LUN6T90yWGqNBhljTmIhPJbsEFt7iXuiwIgoximqbXhX9zoi9i9ydVHQVCZ2FVVpo3hLCRGZ/BP5VtVYIMmnpCt/LW7QLsO5mr+gBJFegC86UF28H3JWU16IVEsObIIKSdMvddh5zhopfolZn1PB19Ek7onesYliJGjrME+zGSH121xdFHRWi5MHmj2Vk1jPHjShYjJVn1ZxcV2pUdeKM9K6gqSzrMxgnIrN2GO4HmfkHZ2oVk6K1n/Htzm6NIRG9L3s02BlRPrhOZ+vsV59Iw6s/tph5yHSRoERUQxDiNga4Wd0fTP4Ns0oPGa+GvlRZ7u6KJLinTge/7HMxbe2GZAi/1YxoPWIlPZabv2l9mWpEYAocxFfSkOuDPm/8W1j7CkOPQ9LL5MY7MUzhtuyxK474n4oMCKK4R8l5jIKsbo+l9GWjjB8ap0D65A5ri6KpEQkj8QzliuxqG0ijGZpLZNis1oRYy7m+yFJI6EEOr8omAUN/NCBmjJ5Tttvb21CWqeY9iVy4vkOP5//mPP4NqXlL5hNRoefj0gPBUZEMUJjxWnxAWhDa3ODS8tSXC8OAI8LpjX1DhXk7QFfgzgZtqTB9YPkD1VVmgdvlREmQYuopGFQAo1WizKN2J1blev6LuYTsTUrF3/ZhqFAFYf41FEOP9/QcaegDgE8mMze+IvDz0ekhwIjohg+foFohDhNv7a076VeHM1iNmFM028Yo8pBQpA0kxi6CuuqyAy08uemssR116g3NfliqwQLJHQe8p7afqg67yF821m2G3L0U7EHrjE/iC/HfOGUiQwajQb5QdP4fsfunxx+PiI9FBgRRanTRPBtS+XfmdOdjXVZvKp9F196PIsIP4PLyiFV91r+D9/rn4D+wA+QkvZyceZWg48YSCiFOTiNb3X10kyqeSwsZ93qnFq+PzPDeXmFPNJP59uY+r9YIZx2XiINFBgRRWn1jESXoEVrY43LylBfKn4AVWoieG4U0teU/UJJPTXf6M7GeOM7yEq/E0piiBYHkge1ue7LwonKLSyGuq0KnjoNxsUHOe28KZPOgEnQIEqoRlmevVNJEqmjwIgoyu9Dn0Ba1yL84em6Qc8dVWIXUZOBpur3RhsiDpL3aRMHOktFdnUbahGIqHhlTNXvEZI8Fp9bTsFC86mwWOU1M61h/SJsNtyKd/0/gYfWeR9XPr4BWOlzFl4xX4j1ZfJfToUMDAVGRFEiQoIhQM2XBXEVoV5MXmj0jXdZGaTMN1oMPIJNrk+r0MNqE1BQ1873U128nIy9hUcn4hnVjfjEfIrkBrwfj3fZGr71jBS7A52pZMJjeNt6Ln4poq40d0OBEVGUmCBxFlipCz8ADK1iS4gqKMllZZCysO614yJQB2NHG6SgqiQXb6texj26JYjuXlpGKdRq1cF133KqpfF894exsx0pneKA8fDRc51+/umpoXy7oaBecqkliGNRYEQUJd7QiXd0r+OZxgdcltAuwFjGt57hyhrEay+BIZFogRjAVhVLY0BwQ+FOzNFswxkeW6FRq6A0GaFaDFMVorFQnHknB/lbV8KgMvPuzYS0sU4/f1qEL1J8TTjFuh77tq93+vmJ61BgRBQlKjQYZ2g2Y5xqP5obnD8AW7BZEW6t4vtBsc5v/pcDNuW6WhPN9xu7B6q7mrFKLEejlzgwXGnOMS3FUv0jSM39P8hFy/6VfFvkP94l6w2y1BLP+HyHdzzehHn7Z04/P3EdCoyIohi8fHhyNqa2zPl5cmpajLjZfBeetCxAeCy1GPVlY+j5eNy8APttsZACbYP4WjEHKvOaeUaJCSsD2qW5eG9vQqq7W2mSZrqsDJpkcema0LrNLisDcT4KjIji1GnFfCetLshlVNRgxCrbKPzhf66ikgTaW23SefjEehr2dDhvCvax+LWJqQM8IpTZyhfavcRJjKUMVosFUtfSVIdki7iEScL4eS4rR/zY0/g22VqA5nqxJZgoHwVGRHHaPMVuGlNdoeuWAukeBE56Fx/szbdF3TPBXEmwCYiylPD9oHhlLB57pIj4VHQKHtCrzKgsEhNZStmOsjbcZb4Fn+ouQGhUgsvKERoZhyK12KpZsHW5y8pBnIsCI6I4Zj8xf5Cqyfl5cmyFq3GW+i+M8ml2+rnlJDFQh5GqPCTWiONIXMnc2czXxbIKKkQlZ0KJ2DIX5VrxA762YBekbmOZET/ZTsLu1DtcXRRUBY3nW1PealcXhTgJBUZEcTRB4jdMQ7vz8+SklHyLNz3ewUmWDU4/t5wk+trwo/5xPGd52eVT9o1tjegQ9KhUR0BvUG5LX5O3mD6iqyILUre5UFwEekKi67tadUPEcUYR9ZtcXRTiJBQYEcXxCkviy4IYzc6fru/fWcq3hrAUp59bTgKCww9O2Xd1184uIQnDuj7CK7FvQ8nMwal8q23IgZSxQHlixSd8oeEJCYGuLg6Sxp0Gm6BCvK0UDTXSSUpKHEfrwGMT4hK+aTORtnQRPKxaHBAEPu3WGVjepDBrJd8PjFHWshIOmbKvjYafJRdNZQeADLG7whWqO1U8W3p4pDg2TbGST8WzOY1oEkZiHKQrf8dqPKD5EjWaIIQGuX7dusCQCDzp/RB+a4jAI1VqnBHm6hIRR6MWI6I4UUE+UKnU6LLYUNvqvHWOmhqq+VgVJlxh6205QrNnHN92VTs/rcKhqrtXjxkSKmaHVqrItIn4P+uZ+Kkxni+BIlUtOeIyICW+o1ySv6hX6WeiAiFYn1/n6pIQJ5DIq44Q+9Fp1Ij0F5d1KHXimml1Jdl8W41gnk+JHJvFXxwLpm50bW6dp43P82zpad7yWS7jRMQGefGFWNkXhnIXriV4PD5V4lgea8wkSMXExGC+3VrU6OqiECegrjSiSHdpFyPVYz269t0FxC9wyjlbK8WWjzqPKIQ75YzypgkdApQB3m3Onz3Yo625HhNV+wAN0BKp7D4SttTJyUHNMNTtQ1luIOKCp0BqzKYuJBuzABUQmum6xI5HGhcfgBs0P2N8QzZa6r+EXzD9hysZtRgRRUrQ1GGkugDWGucN7LXWiy0f7d5iFxE5Nt8osbsxxOS6Aa2V+Xv4lq3H5Rcgtgoo2c3Ct3yJC3X2MkhR4d4N8FJ1oRneSBjq/PXR+hLia8CVHqv5enpFO/90dXGIg1FgRBTJ4i8GJ5pmcZaYM/ymm41rTPejKPESp51TzsKTR+JJ85V40HSty1Yvby0Tp65Xe8TDHZiDxdmSmgax21dqGg6s49siz+FQazSQkip/MXt4R/5fri4KcTAKjIgiabtzGXl1iCvdO8PuVm/8aRsN7wTXzbCSk4DAYHynm49VttEHM4Y7m7VWDBDafMUcP0qnjxQzewe0SXPNNG3lDr7tCBsDyYkTxzz5125zdUmIg1FgRBTJO1z8oAs0idPnnaGkQRzQGh+s3CSB9sTSKCSGiEuDFLpoaRCvZnE9LiHEPfJOhSSJmb2jLaWwWV3TSncsD1puwHldT0A76lJITUSmmOgxyZQDk1G6g9fJ4FFgRBQpKEb8oAuz1Tll0UyLyYhLjN/gTPUGxAWJM+LI8Y3zbcJ89XoYC7pXUneyYKO4Rpp3VDrcQVRCOkyClo/jqSpxbZqEI9W1dSG/0YodSMXQ9AxITWxyJhrgx9ebK9xD3WlKRoERUaTQyASYBA10KitqysVWAUeytFbjft03eNrjY/h5ejj8fEox2/wn3vJ4G1FF3zv93F1mM5qtHjxLemjSCLgDrc4DZRpxLcHagp2Qkp0lTQfzSfkZdJAallOpyEtscWs8IOZaIspEgRFRJI1Wi3J1NApt4aitrXX4+VQdNXxbo41y+LmURMum7LMWmzax5caZiuqNONP0HMZYFiI4TAwW3EFD95ppnRJbM8225SM8rf0vzg523oSJgeqKnIAuQYfGevH/nSgT5TEiivVY1P9hXX49XkY8HN0eoO8U3yhbvdznA9YefKNSgR1syr7zBsn3yKsREzqGeKqlk2HZCQoTLsOH20YiVjMV0kmhCERV/IY52p3Y5CW9/Eo9DJOvQ+a+UfBt9cbpTlxuiDiX+7wbELcT2z0I2hnZr31N1Xxr9k90+LmUJDxxmLhFPTrbW10SGIV7Snd5DEfwTp2K5bbx2NYonbFwbBxgglGcIRiSNhVSlZEQBWj1qG83uWzCAHE8CoyIYsUEioFRWYPjp4IHWsQWI22Ie0z7tpeA4HCezI+pKnJeMk5m/J7H8bPHwzhV7V7Tr4eE+RwMDAVBGkFhae5O+Kg60SHoEZ8mwan63fRaDTKj/fn+jmJaHkSpKDAiijVCyMYPHo/i0oJ/OvxcEbbqv7uGSL+xrohqrbiqfWOZc5MOhrftx3B1EQLcbKx8QrA3TtXswFWWxaipksZ4nposMbFjoT4VWp30Bl4f6lLv7fjF45+I3vgvVxeFuFNgtGbNGsyfPx9RUVH8jfOHH3447PfsW84TTzzBf+/p6YkZM2Zg3759xz3u4sWLkZGRAb1ez7dLlixxYC2Iq4X6eWKUugBxXTkOPU9XZzvC0SCeMy7NoedSohZPMUu5qdqx1+lQNosF0ZbucU2+7jVgni0k+5j+Szyg+xo1uVshCeVb+KYleDSkLiHEC+nqEgQ37nJ1UYg7BUbt7e0YOXIk3n777V5//9JLL+G1117jv9+yZQsiIiIwe/ZstLb2PUZhw4YNuPjii3HllVdi165dfHvRRRdh0yZxJWeiPCHduYxChQZ0GR3XnVbabMEc04u4RXgAQaHu9SFrD7kJl+I60734Uy8m0HOGqtI8GFRmPsNI5xsKd1PvKY6Fay87/hdKZwht2su3+oQJkLqojJP4NsFSCGOHOE6NKIskZ6XNnTuX33rDWotef/11PPLIIzjvvPP4fR9//DHCw8PxxRdf4Kabbur1cewxLHh66KGH+M9su3r1an7/l19+2etjurq6+K1HS0sL35rNZn6zp57j2fu4UuCquvkFhvExCyyZXWVxDqKTxIG+9lZY34E8IQbawDRYWDZhCWYUlvK108aNx+9bPNHW5Om010h1/g6wELZcEwW1Wu12/3dG/yFA+1qoarNdXvf2jk5ore2ACohIm9Sv8rjy/TIkMgF1CECIqgm5u/5CyrhZdj8HfR7Y30BeK5IMjI6lsLAQVVVVmDNnzsH7WNfY9OnTsX79+j4DI9ZidPfddx9232mnncYDo748//zzePLJJ4+6f/ny5fDycsyyDytWrIBSuaJumaoQJKEcm/5cCsOBYoecY1Ulm7KrgcHUgmXLpLlquZSvXQVv6NUiu7zBac+fkLsarNOmQhXhlv93JqOBb72asl3+ms1vAd7segPxuhbcszMLYLd+ctV1C1cnIcS2HTnrf0BujdFh53G316UjdXR0KDcwYkERw1qIDsV+Li4uPubjentMz/F6w1qV7rnnnsNajGJjY3lQ5ufnB3tHs+yFwlq1dBIffCinuu3b/zZgLEd8kAfGzpvnkHMYP3kdkZpcBMTNxbx5Z0JJnHHtGtqMyM56CQnWKsyc/iI8vX3haNvf+ZpvVeFiK6K7/d8V7A0CfnwPcUI5hp5+ukvzOH28oRjYl43kpGTMmzdaFu+Xm+s3ACXbESNUYqQD3ldcXT9HclXdenp8FBkY9TgysZbQj2RbA30Ma4lityOxi+moC+rIY7uaK+rW5RMDGDdDaCp12LnTan/Dxbot2KgZStfuBIQFaPG8x0fwRzsKy29A4jDHjzMpN/kg3xYJbeQwwOZ+/3fxQ0fDJqjgr2pHfVMNgsNjXVa+rCpxnE5mTMCAr4GrrptfymSg5D1EtWc59Pzu9rp0pIGcS5KDr4+FDbRmjmzpqampOapF6MjHDfQxRP6sgckosoWjzuS47wBBXeV86xUuLm9BTnzKflPZAYc/fewL0VPGC3CK6VV4jhTHKbobg5cPKtTie19VnmtnVy3Ivg2f6J7HRN86yEVc5lQeWG+yDEF9Mw3AVhrZBUaJiYk8yDm0f9JkMvGB1FOm9J1KfvLkyUf1abKxQsd6DJG/5hHXYYbp3/hIdb5Djm8xmxDencMoOJam6g92yn5XteNXfG9oN6GpwwzWWJwY4pixgnLwWfiDOK3rBezEUJeVgWU7H27Zi2maPRgSK58Znf4BwbjR/33cbb4VuyopA7bSSLIrra2tDXl5eYcNuN65cyeCgoIQFxeHu+66C8899xxSUlL4je2zwdCXXXbZwcdcddVViI6O5gOomTvvvBPTpk3Diy++iLPPPhs//vgjVq5ciXXrxMRiRJlig7qzXzc6Zrp+TVkBolRWPu07JDLeIedwB+aABKAVUDcVOPxceVVsFXeBZ0Y36DRwV0L8ZGQXFSC77u+Zt85WnLUZaSqBz/IKjRSDY7kYGRuA/Np27Cxtxqw06nlQEkkGRlu3bsXMmTMP/twzAHrBggVYtGgRHnjgAXR2duKWW25BY2MjJk6cyFt/fH3/HrRZUlLCp+H2YC1DX331FR599FE89thjSE5Oxtdff80fS5QrtntZkLo2EzpMFnh52PclX1+azad9V6hCEaNx3w/ZwdKGDAFKAe+2EoefS7XjU+zWv4INGpYS5GS4q5Qw8f0yt9p1XUHNBWJix3LPVITIbCHf0bEB+GF7KUoLWcZ2ynivJJIMjFgm62Ot4cPGJLDM1+zWl1WrVh113wUXXMBvxH34e+nwheEFpApFqM3/AfHpY+16/I4qMVtzjTocMXY9snvxjR4K7ABCTd3ZqB3IVpsDP1Un/L2OnljhToYGCLhesxRDqtiaX5NcUgZVpTi+qSN4OORmgl8TdulvgFChgmArg0pNX4yUQl4hOiEnIFzTihBVC5oq7T9+RWgo5NsmHTWlD0ZEQgbfhqGBjztxJK+WfL5Vh7r3t/zEMF88qvscl9iWobleHCfnbMGt4sLB+lj7fmFxhqSUdGhhhR86UJa3x9XFIXZEgRFRvBa9OKjTWCMGMfb0meEyzOl6EVt9Z9v92O7EPzgcd+AB/lwWN1sceq5Qo5jvzDfWMZnQ5cLHNwBVCOH7lS6YmWbsbEecRew6jUqX35AGnYcexbpkvl+TvdHVxSF2RIERUbwuXzFHi9Bo/8zXeU025AixUPuE2f3Y7oR1jxeHTOfPZVGD4wYDs9aoSNTy/YikEXB31QZxzbSWUnGtMmfKLy7FZlsaihGJ8BgxwJCbpkAxuDaX7XB1UYgdUWBElC9AnC3m0Wbf8StsHFxxvTjbLcTQ95g40j/xwd58W1TvuOnP5Xm7+bYRfggMjXT7S9PB1kxj465qHJ8/6kg7mjxxufkRPBr7sUszbw+GOkrM1O3f6PzAkjiOPF+NhAyAIVT8VuxvFBMx2ktddTkeF97HjdqfEeTe43jtYoxXNf6h+QlBuYsddo6mUnE1+UqdvKaGO4omTMy95d3yd3oUZ9lX0cy3mdH+kKvQoeKg9fiuXNgsju0CJs5DgRFRvMAYMYFduKUSgs1mt+PWFu3Fpdo/cbXud2jpP2nQhqsK8U/dVxhe8xMcpbjdA39YR6EqUH6DfR2hZ5xVmLHI6efOLRNXIhgu48AoNmUkOgQ9vFRdKKUB2Iohyen6hNhTePxQFAthKLaFI6OpCSFBQXY5bnsFy18C1HuIy1mQwfGJGgpsB0JM9m3ZO9QflhFYan4Aj2amY5bDziIfUUNG8m2o0IDWlkb4+gU65bwmYye+qL8UVR6B0AT+AbnS6nT42Xs+ilusSGuwglK8KgN9zyWKZ/D0xmWeH+Aq80Mo6v8Cy8dlqRW7H9p9Eux3UDfmjCn7eTViMsPkMB+HHF9u/IPCcLn2FQzr+gj5zcdehNueSrK3wUNlgb+6A1FR8s4AtjPtHvzbciE21bvv8jJKQ4ERcQsJ3WtiFXUPlrYHQ4u4fIUQTIvH2kNASASaIQ7ArirKgr1ZrVY01VXy/SGhFBj1EMIz0QkDcqsdmz/qUA25m/m2VJ8i24HXPUbEiF2Be8rZUjNECeT9iiRkgDOeSurEAZ/2ENgpTv/3jEih62An1VqxW7KpzP6zpCoL92OT7kas19+OaH+D3Y8vVyndrWd5tc5bGkToznjdGiS/jNdHYoPHQ9GEkIo/YbWYXV0cYgcUGBG3cIp5Nbbo/4Hpex+1y/GsFguirGLrQ1Bcul2OSYAWL3G2WFe1/WdJ1RWJ2Yk7tX5Qa+itr8c4z0o8rf0vRhx43WkvwcBmsUXQI0ac7i5nSSFeWKW/B++pX0JZzk5XF4fYAb07ELcQFBCAUFUL/DpK7XK86rJ8PkbCJGgRFk1dafZi9hdTK6gbxW5KezJWistPNHmJ5yCiJB8zrtSuxOiWP53ylJhNXYg3i1now7unu8uZRqNBsYfYalyTs8nVxSF2QLPSiFsIjBXztYRby/mU/cGOa8gzBeFU438xObgD72vp38hemtMuwul5yQjXp2EC7EtdL66VZw6kQPZQEcliBvAIWw0f9O7p7QtHKs3ZgSSVGS2CF6ISxQH3ctccOByo2QNbubwzYLc3VCKnzQCLTUBiiDdCfNwzQRu9oxO3EBEvBkZswcfG+upBZz0urGtHBwzQRtAEXXsKj03BAaEWTQ32yzfVw69NbKXQRYivBSIKCo1CI3wRqGrlmcGHjDzJoU9Ndp0JGy2zEOrnidkK6dLUxo4Bar5EQJM8M2CX7l2H1mX/QkbHVtxkfAc1ENM2nBfbhn8MF5A67WK2bg/chTJelYQch8HLB9UI5vvVxYOf8cQCIyYxhGY32VNC9yD5qhYjOk1Wux2XtRJGdS9YGhQv/wG/9sRaTyt1YoDfXOL4VeI3NgfhYcv12JRhn/F+UhCeJi6CG2/Kh8VsgmwIAnZ8+S9EfjufB0U2QYWpPmWICfTkvz6r6h2k/nkTdr91CSxGxy3VIzUUGBG3UdediLGtImfQxxqb+wae0X6EUfoKO5SM9Aj09sD1hj/wnPY/qCi037fv+ppy+KGdv/FHJlFgdKRWX3ERV1OV49dM21PevRRI9zR3JYhJGo5WwRMGlRllOTLpThMEbPvP7Rid/Tq0Khs2eU1H/qXr8Nqj/8S6B2dh/QPTIUSMgEVQY0TDr8h/fS7MRufNXHQlCoyI22j3Eb8Vm7sTMw7G2LY/cYX2d8R70/Rce7tIuwaXaf9AS5H9ZvgU1bZhoeU0rNBN5wk/yeGEEHHZHEOTOA7LUVhrirpyBzxgxrAo5QRGajYAW5/K92uzN0IOtn39HMaWf8r31yTdgwn3/YCUtL+/NEQF+WDmrW9j+/T/8qBvqHEXct86D4JV+WvCUWBE3EZn6Aj8ZR2GQvPglgQxdrYjwlbL98MSxLWmiP2n7BvtOGV/f5snnrQswDcxyum+sSfvGHEQtMbY4NDzlOXtxneaR7BZfyuSgpWVKXp/3GW423QzVlszIXXZG5di1P6X+f7qxLsw7ap/9TkhZcKsc5E7exE6BQ9ktG/Czk8ehNJRYETcRmfmlbjc/Ai+ts4Y1HGqivZDrRLQAi8+cJU4Zsq+xo5T9rO7szqnRjh2xpVchQ6bhRHG/8O5nY/BZLH/wPcetdnidPYKjwTF5ZLSZ56FJbaT8VettGdydZgsuG+1GT/bJmOj72mYduW/jvuYMVNPx9YRT/L90cX/Qe7Gn6FkynplEnIMCSFiF0px/eAGETaUiPlwqrQxsl/OQIq0oeJ0eu92MbO4PXSW7YUvOpAaToPlexMe5Aeb3h9Wm4CiQf5/HIu1QuwebQlUxjT9IzNgM1kVLTBbHRdcDtYrv+VgT6MOL3rdh2E3f9Lv97Cp592Mtf5nIdcWjdfXVqHLYr/JEVJD7+rEbcQFiU33XR2taGo78TXTuroHqPZ0+RD78osWx7uEmsrtNiPtsbr7sMdwPYZr7XNMpVGpVBjSvTRIbrXjBtj6N+7jW03UKChNfJAXphgKcbmwFEW5Yj2lZl9xNRauF9NWPHteJny9DAN6jQy/+k0s0L2MpfWRePfPfCgVBUbEbXh5aPGL52PYb7gWVQc2nPBxtA3irDZLkPgBTuwrPF5sTQhDAzrbWgZ9vPrqUgSgDVZBhdghNCOtLxfoN+ET3fPw3vEhHIEtoxNvEseNhQ4Vp7criVqtwsP6b/G47lM07VsJqWFfENSfn4/3tP/GlekqzBwaNuBjBAYG4qGzxGVcPliTj8rmTigRBUbErVg8/Pi2pfTEv9GpOur5Vh+lvO4AKQgICUcTxNaLiuLsQR+vMlfsvqlQR9KMtGNINrRgmmYPfGu3wxHK8vfAS9WFDkGP2BTltRgxrUHiwGuhQnpT9vf++TXSTXswQ70TN8848YWvzxwRiclx3rjG9gPK/ns1lIgCI+JWOvzEfC226hPL12KzCbjCeD8fqBo44nQ7l470eDjwNQwzfoRcIWbQT0p72W6+rfNKoif4GLyixBmWQR1iV4ujBl6XeCRDo9BldDzixvJtYPM+ybUW+ax/ke9vi7wEUfEnHhipVCo8fpIX7tN+jfHNv6Jo1yooDQVGxL2Eit1fns0nNhW8vKkTRrMNRq0vYkIHN+2f9E0Xnop2eKKw7sTHgvVQ14lBsDFQzDNDeheaJK6ZFm0tc0j25s2meDxnvhQHos5V7CWITJ/Ct2yR3C7j4F+79rJvzWIkWgvRLhiQfv7gU1akj5yATf6n8f225c9DaSgwIm7FJ1r8VhxiLDqhx+d0T/tOCvGGVmHTjaWELWDJ5NcOfiCwX4s4SFQXSTmnjiUidgjv5vJQWVFZOPhlc460qj4AH1rnwzLiMihVZFwKX3eOPYcl+7dAKjTrX+fbXeHnIig0wi7HDJ37MB+3N7x9I0qzTnzMphTROztxKxFDxG/FkbYaGDsG/qGr3bEIn+qew+WG9Q4oHekx0rsJT2kX4tT85wfdhRBtFoPg4MSR9AQfJ3tzuTaW79cV2XcxVNYFzaaxHzqtXYnY1PcSg7hIcUOuNDJg52xdiXTTXpgELRLPsl9yxpT0kdjiM4vvN/7yLJSEAiPiVoJCo/nAXpagsTxv4AtmelVvw8mavUjRNzqkfESUFOSBq7QrMK3zD9isJ54vpbKxDa9YLsIX1lMQnSz9jMSu1uQjjsPqqrBvi1FJSSFmmtcgVVeD5FBlL8nSESJ++VJLZAB285r3+XZH4GmIjBGTp9pLwGn/5NvhLetQW+z4dfachQIj4lbYN7qNnjPwhWUmipoH/oEb0C5mY6YZaY4VnZTBv+GyWUxVJSe+fldOnREfW0/DwsA74aHvf84Wd2UJSkWj4IPGQeT56k3dnhV4y+NtvKV/X/Fd0NYRl+L8rn/hGeFaVxcFdW1duKH+UjxivhZBp95t9+OnjZiA7R7j+RfNkl//DaVQ9iuUkF6sGvIgHrbcgF2doQN6fljLRbS5hO9Tt4xjaXUeKNWI3To1+Sf+zbtnTFhqOC0F0h+tY27G6K4P8I5wPuzJUiZew6YA5ae4SEnLxDZhKPbUmPnyG6709ZZSNFkN2Bd1AVKGj3fIOSwT/oFvLNPxSvVYGM3KyIZNgRFxOz0ZfvMGOLC3qjSPt2CYBA2iEpX/Bu9qjT5iaoXO8oF3efboyluDYapCDAuX9vpVUjEkIoC1qyKvpo0vD2Ivfo3imCVVtJgcUMnC/QwI99ODPX37usdVuYLVasMXG8Vlda6YFO+w84yZeR7+7X0nNnREY+nuSigBBUbELQMjPUzoqhxYn3hNnpj4rlwTy1s0iGOZg8XUCrr6E0/yOL/sVSzVP4KT1PYdTKxU8cHeMOjUPCVFUZ19lgZhLa1xXWJ6jJAU5WW87s2FwYV4UrsQbVu/dlkZ9v35Jf7TeRcu89zIkzI6ilajPhh4LVp/YrN9pYYCI+J2hnq3Y7/+GrzfevuAco10lojdAXW+tBSIMxiixcHSge0ntiYTm3UYay3j+1Fp7vGBPFgatQrP+CzGWo870bTFPh/q5YVZ8FF1wijoEDdU+S1GzEke+VigXQG/4uUuK4N151dIV5fgzLA6GHQah57r0glxGKMtxKXVr6Jg52rIHQVGxO1ERMWjVeUFncqK0uz+j1+pa+3kA1OtYbTeljOEJYkfoh6WdlhOYCXvkgNboVEJqIc/QiJowd/+ivU0IVZdC1P5LthDdbY4bb1El+g2La3eSRP4Nrxtv0vO39HaiPRWMbdQyOTLHX6+IG8PPBC4Bpdp/0DT2v+D3FFgRNxyZlqpXkyJ35i/td+Pe9F4Lh+Yigk3OrB0pEdkfAomW/+Dk7teR1HDwBerbMzfxrflhiH8mpP+ESLEljqvBvt8qFtKxS8fjf7uMy4vbthJfBsjVKK5sdbp5z+w+hsYVGaUqqKQMkLMxu1o+glX8e3Q+hXo6miGnMn23SIhIYGv2XLk7dZbb+3171etWtXr3x84oJzcC6T/2gLS+dZWKa6jdTzNnWaUNbIPZxXSo4PpqXZSwsGwcHFsRHaVOLtsQKrEa9seKF5r0j8BiWP4Nsp4YsvmHOljYR5uMt2F5rRL3Goh5DKVmGG6dO9fTj+/Jut78dxRpzvtS8GIKfNQikh4w4gDv38GOZNtYLRlyxZUVlYevK1YsYLff+GFFx7zcdnZ2Yc9LiXlxBfTI/KliRKTsPk29S8wzioXvwHFBHrC30vn0LKRv2VE+fHtvoqBfwMNaBavrS5amSu5O0ps2jjYBBVC0Yi6qtJBHYvNbFtdqcVvtgmIz5wKd1LlIwbkbQWbnXrelqZapLeLy5FEnOT4brQeGo0ahXHiOniGvV9CzmS7xHFo6OE5aF544QUkJydj+vTpx3xcWFgYAgLYlNTj6+rq4rceLS3i1Euz2cxv9tRzPHsfVwqkWLeAhFHADiDOlI8uo5G3ThyLsPFdrNN/jE2Gs2E2nyz5+tmLq+s21VCE03SvQLcrCOZTvu3346wWC2LNhayBD0FJo/ssv6vr50gnWjcPgxfK1JGIEypQtn8j/INPfG2tA1Wt6DBZ4e2hQXyg3m7Psxyumyl0JND6Jzyqdw24nIOpH2utmaCyokAdj5ghI5z6HMVOuxrWT9/D0K49qCzYi5DYoZK5dgM5n0oQBPslq3ARk8mEqKgo3HPPPXj44Yf77EqbOXMm74IzGo3IyMjAo48+yu/ryxNPPIEnn3zyqPu/+OILeHl52bUOxLlsVgvm7boJepUZ3yS/DL1f+DH/PmDP+5huWY//eZ0H29BznFZOd9dZV4BLSp9Ag+CLNaPehkqt6tfjajqsWLMnG5nqIqSMPR1qGmM0IMG738FU6yb84H0JVKnzTuziAagt3oum6kIUemZiTmYC3Eln1QFcUvkcdgtDUDjmcaedd92+HMzsXI5Ov2RoU+fC2aJ3voRxwl4s9bkQlpT5kIqOjg5cdtllaG5uhp+f2BKtuBajQ/3www9oamrC1Vdf3effREZG4sMPP8TYsWN5K9Cnn36KU045hQdM06ZN6/UxDz30EA+2Dm0xio2NxZw5c477xJ5INMu6A2fPng2dTlldNVKt2085PyOr1RPjhkzCqeOOPQ6lZuf9fBs7fh6GTZ0ni/rZg6vr1tXZDvOrTyNI1Yrxo9IQFiMmfTyeH3ZWYO0uPdpjpuHuM8UZQlKsnyMNpm4bmrZjZ34d2n1icNG8Ew+Mtr/zNSbqlmJDqAHj5t0Ce5HDdWtrn47xL8aiVvDHxmnTEeyjd3j9GtpNuHeTDt/aMrD8ipOQGOL8dek2GPNQllWFMiEI1/by2nHVtevp8ekPRQRGH330EebOnctbjfoydOhQfusxefJklJaW4pVXXukzMNLr9fx2JHYxHXVBHXlsV5Na3Xal34tPNhTDVqfB3GOUq766DNFCNR93kTR6Vp91kFr97MlVddPpApCvjUOytRA1uVsRnSiuXH48eyrEwdpj4gL7VW66doezTrwV5+yfgJROH1w+iOse1iIm1jQkTnLI60fK1y0wIAD+odGorWlDVnU7ZgWKGfcdWb/fsythsQkYFuWH1Mj+DRmxt/TTbsKknamw1qsxt8WMuGAvSVy7gZxLtoOvexQXF2PlypW4/vrrB/zYSZMmITf3xBeoJPI2Nj6Qb7cXNx7z70p2rRK3mlj4BdCMNGer9xVb87pKxMzj/RGXvRBz1ZswNkqaH5pSxz5YmfzaNrR3ndh6X20tDYi3imsLxo7o/cun0o2I8efbXaXOmb7esmEhElWVmD+y70YCRwsN8MHk5DC+/7/dFZAj2QdGCxcu5AOqzzjjjAE/dseOHbyLjbinMbEBiFNVI6VqKTrb+54ObiwUE6XVBIx0YulIDyFSfN496/u3ZpqxoxUL2hfiPY83MDqEnscTEeZnQKS/AR5CF/YXitnDB6po9zq+6noVQt02weYsnxIs0r2IiTv/6fBz1ZUX4sbGf+NP/b2Yn+Taj/azRkZBCwvKtiwFZDiMWdZdaTabjQdGCxYsgFarPWp8UHl5OT755BP+8+uvv84HXg8bNowP1v7ss8+wePFifiPuiU29/07/NMLQgKzd05AxufeBigF13S0VsX2PVSGO4580DtgPxHYegGCzHTcvS9Ge9UhTWVGLQIT3c0wSOdpThi8x07gYW7fcDqQ9NeCnqDVfzHhd4ZOBE5/XJm8pEf4YqtmFpnYfCDYrVGrHLc2Rv/ozhKgE7NdlID02Ea50WkYopvx8D2I66lC8exTiRx57trjUyLrFiHWhlZSU4Nprrz3qdyxHEftdDxYM3XfffRgxYgROPvlkrFu3DkuXLsV5553n5FITSWXA9haX92jO7n19nzajGauNQ7DfFoeYUbOdXELCJGZOQaPgi33WOBRXVh/3SWnKFVv4yrwyKOP1IPgER0KrssGjeucJPd6zWvxCYYoYC3eVOGwSOgUPBKANpXn9a/E8UYGF/+PbpqSz4Gr+3gaU+4i54mo3uW4hXbcMjNjsMJZtIDU19ajfLVq0iM846/HAAw8gLy8PnZ2daGhowNq1azFvELMtiDJY4sScRP4V63r9/caCBrxgvgQ3+76JqETKoOwKeoMXbor4CleaH8bmyuOPd9FXiAn1jG78gWwPfsmT+DaqLWvAj7VZbQjtEDNnB2XMgLvy0OtR4CFO+qne+/fnkb1VFh1AqjkbVkGFITOcl9TxWIR0MUCLrlwpu+40WQdGhAxWzHgxz0ZKVxZamuqP+v3aXHGdo6kpNFjFlcYmis//1qKG4yZ2TO4QWziCh5/ilLIpVXzmFD4TMwK1qKv6u/W9P7Jr2jDN+BousD2PpMzJcGfNoeISKyh1XAbs4jWf8+1+/UiERkpjPFf61HNhFHSIFKpRni1m4pYLCoyIW4tOSucLLepUVuRv+vmw37HxLO17l0EPE05OOTzTOnGu8QniDMKcwuJj/l3B3g3wQztaBU8kZTpn8Uyl8vELRLFG/JAt29N7i2pfNhc2wAoNPOPHQqvzgDvzShJfh+HNJ9Yl2R+hxeJ7V1uq67vRevgHBGCv13i+X7nxG8gJBUbE7ZWFiQMDbXvEhRd75O5YjVfMz2Kt/m5MHxLk9s+TK42N9sFKj/vwQ/uVqK0o6vPvqvet5dt871Fu/4FsD7V+ww6bmTmQwIiZmEj/N/GjxK7EOFs5musqYW/FJcUIs1TCLGgwVCLdaD26hoizxUNLl0NOKDAibi90ypX8OUhu3YLmlr+n7TduEGc0lviNgUHv3t96Xc3f1xs2DzFBXtHGH/v8uw+7TsXUrteRP+JeJ5ZOweLF1o6A2q39fghrab0x9x94SfsBplA2FASGROCAegj+sg7D/oJjt3ieiJ9yTRjX9R5eDH+Fn0tKUk++gAds8dZi1Bbvh1xQYETcXnLmZDzr9QCmdr2Bb3eJY4qaG+uQWbuU7+vHL3D750gK6qPEb96a/BW9/r7VaMbG/HqUCWEYMVocOEwGJ2b0HCy1TsDnxsnoMPUv0WNp3m6MRDbO1qxHRkI0XQK2OkPaR7jc/AjWNto/GzVLotgFDwydIL1Zs6FhEfjA/07M7Xoev5QbIBcUGBG3x6btJ824Em3wwrur8tHUYcL+rx6Fl6oLhep4DJ8qnYUQ3VnwqDP5NrVtK0xdxqN+vzq7BiarDUkh3hgSNvDlF8jRIuNT8azXP/Gp+RRsL27q11NUsW0Z3+bpM2DwpMW2mbEJYpfituNk2R+onPI65FS3wkOjxpxh0mot6qEfdyX2C/H4dd/xU21IBQVGhAC4cGwMkkK90dbejoKXpmFS9Zf8eWmZ+jDlwpGIISOnoh7+8FF14sD6wwfKMyG/34P/072Cq+LroVKpXFJGpWHP46QkcRmcjQVHz9rsjWfxH3zbGuu+0/T7Wn6ouLQUZrPJbsdtWPY0fve4D/dG74W/pzSXvzmtO2DbVNiAxnb71d2RKDAihKWA16jx9qVjcLvnrxjD0iyzVaIjr8DIWZfQ8yMRao0GuSGn8n3z9s8O+11zQy1GN/+O2ZrtmJJMqRXsaWJiIF9/y5r103H/1tjRhtTOXXw/fMzAl2lSquRQHywxPIkNmhtQsGuNXY7JxnLFVvyCZHUlXyxZquKCvXBxcAFe0ryDvD/+CzmgwIiQbhlRfrjsH49iy6hnceCMxZh80zv03EhMyMlilvvMlrVoqhfHgzH7f/0AepUZBeoEpIwSk3YS+zg5zMjX37q36TkegB5Lzubf4KkyoQZBSEgXp2oTQK1WQfAWF1Zt3Pu7XZ6S/F3rEC1Uo0PQI2P6hZJ+ms8MKsP5mnUwHOh74oSUUGBEyCGCw2Mw/pzbkDZebJkg0pKcOQUr9KfiWvP9+GBz3cFWiuSc//D92rQrqOvTzqIShqJQHceXB8n96/CUFkfq2PcL3xYFTqbrcARTrBiw+1aut8t1qd/0Bd9m+U2Bt68/pCx4tDhOc0j7NliM7ZA6CowIIbLBF5A9+12ss2Xio7+KsL24Abv+ewdC0YhKhGLUWbe6uoiKVBUujhdS5YiBT2+sNgEb6r1RbAuDR4Y4UJ78LXL0HL4dYtzHg/nBsFmtSKgScwOph18g+ad56MgpqEYQPNGFgm3Sz2lEgREhRFZOTQ/jN5PFhl8+fAQT6xbz+6tPeoqvq0bsL3C0mFE5pXVTrzMCmS1FDXijYw7mq99C+gxpd+24QlzKSN7FyLp887f/OahjZW9ejnDU8wzvGdPOhdRpNGrkB5zE9zv2/Qqpo8CIECK7mVKvXTwKZybrcId2CdoFPTZlPIZRsy9zddEUK2XMTNQiEH7owL7V3/b6N//bVcG3pw+PhF4nzRlSrm7tLPYTFzZu3b9yUMdq3SJ2o+0PnAmDpzfkQJs2l29j6tZKflFZCowIIbLjZ9Dh7RvmwHjzZqgfyMfEi+5zdZEUTaPVIi9SnGWm2ikuWHqotpZGmHZ+Cy0sOGskJXXsi5AgLj8UVD2wJVYOxVpK328ci28t06AfdwXkYujkM9DFF5WtgbWlHFJGgREhRLZCI+Lg6e3r6mK4hagZ1/NteHs2ymoOz2m0d+l7eFn1Br7xfgVTksW8R+RocePn4nvrVLzeOfeEc/qsyanFH50peMnzTgyfMk82T7O/fwCyDKOw3xaHyiZpD8CmwIgQQshxxQ8djbeCH8HMrlfxzrq/v/F3trciMVucFWhOPZNPTSe9i4gdgg+DH8Qy6wSsyqk5oafph53icz9/RBQ0Mnuut056C3NNL+DnjnRIGQVGhBBC+mXy/OthhB7fbC07uLzFrk/u4wOB2azAkWfdRs/kcZyaHs63K7MGHhi1tTRgzIFXMFxVgHNGyW+F3hkZMXyb26yC0WyFVFFgRAghpF/GJQTh7FFRsNmsKFh4A3a8Mh+Tqr/iv6s66UnZDAR2JTajMl1VjKE5H6DL2DGgx+5fsQjXqpfiXcN7yIyWdu6i3rA1DCP89NAIZuzMzoNUUWBECCGk354+ZzgeCVqFC7ECo9vE5S02xP8Do2dfTs9iP4yI9sMn+pdwh+or5GzsOy9UbwL2i7PRypMulGUCTZVKhQeC1mC3/gZ4/fUSpErr6gIQQgiR14zAK25/Cpt+joKtoRB+mfMwWUaDgKWw5l9B0DSENvwI487FwIzz+/W4/N3rkGLJhUnQIvW0GyFXEXEp0FeZEdmwCVJFgREhhJABYV1mEy+8l561E+Q97lJg+Y9Ib/wDxo5WGLyOP7OyfvWHSAaw228axoXJNyVC8tjZMG/SIMpWhfrSbATHDoXUyK8tjhBCCJGxjIlzUK4Kh4+qE1l/fHncv2+qrURmndjtZpgoLqQsV0FBwdinGsL3S7cuhRRRYEQIIYQ484NXo0FJtLiwqsfe4wdGB356FZ4qE/I0yRg2RUy0KWeFnsP5Vl0wuKVRHIUCI0IIIcTJYmdeB5ugwnDjdhRnbenz7zpMFqwps6Be8EXTmFtlOej6SG0BYmCU0LoNgtUCqZH/M0wIIYTITExyBnb4TEWD4IM/NvQ9EPnDNQV4t3M2LvH6D0bNuQpK4BWagBbBC35oR+m+vyA1NPiaEEIIcQGP+a/gpEX7YM73xMk1bTzPz6Eqm414f3U+379r7khoFbI4r1ajwS/+FyGn3oK0Bi/EQVqoxYgQQghxgcy0NExJi4PFJuDxH/fCZvt71XmbzYbK/1yMqdYtGJ8QiHmZEYq6RhXDb8ZH1nlYXaGB1FBgRAghhLjIv+YPg16rRnTRYmx+/x+wWa2wmLoQkLUQE41/4Q3dO3h5XgxPjqgk4xMC+XZzYQME4e+AUAqoK40QQghxkbhgL7w324BZf34I1AClz66FVjBjplADq6DC/okvYFyc1DqbBm9kjD8SNXUY056F8sJoxCRJZ2FZajEihBBCXGjW9JnYNPI5dAh6xNrKESnUoEnwwbaJr2PcvGsUeW0MOg1e9f4Er3q8j9qtSyAl1GJECCGEuNjEc29F07QLsW/TUggqDUpb9Thr9nlQstbw8SzLIzRlGyEl1GJECCGESEBAcBjGz7sGo0+9FFq9AUrnO3Qa38a27AQkNM6IAiNCCCGEOF3KqGnoEnQIRDPqS/ZJ5gpQYEQIIYQQp/P18UGuVlw3rSpLOokeKTAihBBCiEvU+4vLg3SVbJPMFZBlYPTEE0/wnA6H3iIijp38avXq1Rg7diwMBgOSkpLw/vvvO628hBBCCDmaKno03/o27IFUyHZW2rBhw7By5cqDP2s0fWfPLCwsxLx583DDDTfgs88+w19//YVbbrkFoaGhOP/8851UYkIIIYQcKnD4bFy/rR4l6qH4TRAkkchStoGRVqs9bitRD9Y6FBcXh9dff53/nJ6ejq1bt+KVV145ZmDU1dXFbz1aWlr41mw285s99RzP3seVAiXXTen1U3LdlF4/qpt8udO1S4yNwyqMg6VDQFFtK2ICPR163v5QCVLLxd3PrrSXX34Z/v7+0Ov1mDhxIp577jneRdabadOmYfTo0XjjjTcO3rdkyRJcdNFF6OjogK6PhfnYeZ588smj7v/iiy/g5eVlxxoRQggh7umlXRqUd6hw/VArMoMcE5Kwz/rLLrsMzc3N8PPzU16LEQuEPvnkE6SmpqK6uhrPPPMMpkyZgn379iE4OPiov6+qqkJ4ePhh97GfLRYL6urqEBkZ2et5HnroIdxzzz2HtRjFxsZizpw5x31iTySaXbFiBWbPnt1noCZXSq6b0uun5LopvX5UN/lyt2uXXf89PAp+RYZ+BGbOu80h5+3p8ekPWQZGc+fOPbifmZmJyZMnIzk5GR9//PFhgcyhjuy37GkoO1Z/JmuNYrcjsYvpqBerI4/takqum9Lrp+S6Kb1+VDf5cpdrN15fjJna75FVXg6d7m6HnU/Rs9KO5O3tzQOk3NzcXn/PxiKxVqND1dTU8HFKvbUwEUIIIcQ5fGKG8W1wZyGkQBGBERsgvX///j67xFiLEmu6O9Ty5csxbtw4xUbjhBBCiBxEJI/g23BbDazGVlcXR56B0X333cfzErFp+Js2bcIFF1zA+w8XLFhwcGzQVVdddfDv//GPf6C4uJh3s7EA6r///S8++ugjfhxCCCGEuE50VAxqBX++X5Xv+nxGsgyMysrKcOmll2Lo0KE477zz4OHhgY0bNyI+Pp7/vrKyEiUlJQf/PjExEcuWLcOqVaswatQoPP3003jzzTcphxEhhBDiYmq1ChW6OL7fUur6wEiWg6+/+uqrY/5+0aJFR903ffp0bN++3YGlIoQQQsiJaPKMA1r3wFxbAFeTZYsRIYQQQpTD4hvDt6rmv3t7XEWWLUaEEEIIUY6qpAswvXAoRgUMw9+pmF2DAiNCCCGEuFRIZByKhVoENFtdfiWoK40QQgghLhXTvUZaWUOHy68EtRgRQgghxKWi/D1xt/ZbhHc1oqt1NPS+IS4rCwVGhBBCCHEpf08drtD8jmBVC+qqi10aGFFXGiGEEEJcnsuoURXA91vrK1xbFpeenRBCCCEEQKs2kD8PXU2Hr23qbBQYEUIIIcTlOnRBfGtuocCIEEIIIW7OZBDHFQmtNS4tB7UYEUIIIcTlbJ5iixE6G1xaDgqMCCGEEOJyGm9xjJHa2OzSctB0fUIIIYS43PA516F08rlICAl3aTkoMCKEEEKIy4WEhgHs5mLUlUYIIYQQ0o0CI0IIIYSQbhQYEUIIIYR0o8CIEEIIIaQbBUaEEEIIId0oMCKEEEII6UaBESGEEEJINwqMCCGEEEK6UWBECCGEENKNAiNCCCGEkG4UGBFCCCGEdKPAiBBCCCGkGwVGhBBCCCHdKDAihBBCCOlGgREhhBBCSDdtzw45PkEQ+LalpcXuT5fZbEZHRwc/tk6nU9TlUHLdlF4/JddN6fWjuskXXTv76/nc7vkcPxYKjAagtbWVb2NjY0/02hBCCCHEhZ/j/v7+x/wbldCf8IlwNpsNFRUV8PX1hUqlsns0ywKu0tJS+Pn5KeoZV3LdlF4/JddN6fWjuskXXTv7Y6EOC4qioqKgVh97FBG1GA0AezJjYmLgSOzNWWlv0O5QN6XXT8l1U3r9qG7yRdfOvo7XUtSDBl8TQgghhHSjwIgQQgghpBsFRhKh1+vxr3/9i2+VRsl1U3r9lFw3pdeP6iZfdO1ciwZfE0IIIYR0oxYjQgghhJBuFBgRQgghhHSjwIgQQgghpBsFRoQQQggh3SgwkoB3330XiYmJMBgMGDt2LNauXQupe/755zF+/HieBTwsLAznnHMOsrOzD/ubq6++mmcIP/Q2adKkw/6mq6sLt99+O0JCQuDt7Y2zzjoLZWVlcKUnnnjiqHJHREQclkGV/Q3LoOrp6YkZM2Zg3759kq9Xj4SEhKPqx2633nqr7K7bmjVrMH/+fH4tWDl/+OGHw35vr2vV2NiIK6+8kieIYze239TU5NL6sfW0HnzwQWRmZvJys7+56qqreHb+Q7E6H3k9L7nkEpfX73jXzl6vQyleO6a3/0F2e/nllyV/7Z7vx/u/nP/3KDBysa+//hp33XUXHnnkEezYsQMnn3wy5s6di5KSEkjZ6tWr+Qfpxo0bsWLFClgsFsyZMwft7e2H/d3pp5+OysrKg7dly5Yd9ntW9yVLluCrr77CunXr0NbWhjPPPBNWqxWuNGzYsMPKvWfPnoO/e+mll/Daa6/h7bffxpYtW3jQNHv27INr6Um5Xgwr86F1Y9ePufDCC2V33djrbeTIkfxa9MZe1+qyyy7Dzp078euvv/Ib22dv0K6sH1v8dvv27Xjsscf49vvvv0dOTg7/cDnSDTfccNj1/OCDDw77vSvqd7xrZ6/XoRSvHXNovdjtv//9Lw98zj//fMlfu9X9eP+X9f8eWyuNuM6ECROEf/zjH4fdl5aWJvzzn/8U5KSmpoatuSesXr364H0LFiwQzj777D4f09TUJOh0OuGrr746eF95ebmgVquFX3/9VXCVf/3rX8LIkSN7/Z3NZhMiIiKEF1544eB9RqNR8Pf3F95//31J16svd955p5CcnMzrJufrxl5/S5Yssfu1ysrK4sfeuHHjwb/ZsGEDv+/AgQMuq19vNm/ezP+uuLj44H3Tp0/n17gvUqhfb3Wzx+tQCnXr77VjdZ01a9Zh98nh2vX2/i/3/z1qMXIhk8mEbdu28Uj7UOzn9evXQ06am5v5Nigo6LD7V61axZtaU1NT+Tefmpqag79jdWfdAYfWnzW7Dh8+3OX1z83N5WVhXZys6bqgoIDfX1hYiKqqqsPKzJKxTZ8+/WCZpVyv3l6Dn332Ga699trDFkaW63U7lL2u1YYNG3gT/sSJEw/+DevSYfdJqb49/4fsOgYEBBx2/+eff867K1hL6H333XfYt3Yp12+wr0Mp1+1Q1dXVWLp0Ka677rqjfieHa9d8xPu/3P/3aBFZF6qrq+NNhuHh4Yfdz35mLyq5YF+I7rnnHkydOpW/qHuwLkHWPRMfH8//UViT/6xZs/g/BPsnYXX08PBAYGCgpOrP/gk/+eQT/mbM3rCeeeYZTJkyhfeP95Srt2tWXFzM96Var96wcQ+sv56N55D7dTuSva4V27IP5yOx+6RUX6PRiH/+85+86+HQBXEvv/xyHuCzroy9e/fioYcewq5duw52oUq1fvZ4HUq1bkf6+OOP+Xid884777D75XDthF7e/+X+v0eBkQQc+k2954V25H1Sdtttt2H37t28j/hQF1988cF99g8zbtw4/ibHvhkd+QYgpfqzN+QebGDr5MmTkZyczN+8egZ/nsg1c3W9evPRRx/x+rJvanK/bn2xx7Xq7e+lVF/2zZu1bNpsNj6Z41CspeXQ65mSksKvKRuXNGbMGMnWz16vQynW7UhsfBELgtgEHLldu9v6eP+X8/8edaW5EGse1Wg0R0W+rLn4yEhbqtiMgp9++gl//vknYmJijvm3kZGR/I2NdVMx7FsQ68phsw6kXH82W4IFSKzcPbPTjnXN5FIv9s1t5cqVuP766xV53ex1rdjfsJbDI9XW1kqiviwouuiii3irCmtJOLS1qDfsA1Wn0x12PaVcv8G8DuVQNzYLmc3oOt7/oRSv3e19vP/L/X+PAiMXYs2IbHp+T7NoD/Yz67qRMhaxs28KbCbMH3/8wZt7j6e+vh6lpaX8DY5hdWf/5IfWn826YE3GUqo/m1K6f/9+Xu6eZu1Dy8z+udksjZ4yy6VeCxcu5E3SZ5xxhiKvm72uFWsxZGMoNm/efPBvNm3axO9zdX17giL2QcmC3ODg4OM+hnUJs8f1XE8p12+wr0M51I212rK6sBlscrl2wnHe/2X/v+ewYd2kX9iIfDYy/6OPPuIj8O+66y7B29tbKCoqkvQzePPNN/MZBqtWrRIqKysP3jo6OvjvW1tbhXvvvVdYv369UFhYKPz555/C5MmThejoaKGlpeXgcdiMvJiYGGHlypXC9u3b+awMNiPMYrG4rG6s3KxeBQUFfDbEmWeeKfj6+h68JmymBav7999/L+zZs0e49NJLhcjISMnX61BWq1WIi4sTHnzwwcPul9t1Y+XdsWMHv7G3s9dee43v98zKste1Ov3004URI0bwGTHslpmZyV8Xrqyf2WwWzjrrLF72nTt3HvZ/2NXVxR+fl5cnPPnkk8KWLVv49Vy6dCmf9Tp69GiX1+9YdbPn61CK165Hc3Oz4OXlJbz33ntHPV7K1+7m47z/y/1/jwIjCXjnnXeE+Ph4wcPDQxgzZsxhU96liv2j93ZbuHAh/z37B5kzZ44QGhrKAz/2Icym35aUlBx2nM7OTuG2224TgoKCBE9PT/6CP/JvnO3iiy/m/8Cs3FFRUcJ5550n7Nu37+Dv2VRUNqWfTUfV6/XCtGnT+D++1Ot1qN9++41fr+zs7MPul9t1Yx+Yvb0OWZntea3q6+uFyy+/nAfI7Mb2GxsbXVo/9mHZ1/8hexzD6sHqzOrG3l9YWoY77riD18fV9TtW3ez5OpTitevxwQcf8HKzqetHkvK1w3He/+X+v6fqriQhhBBCiNujMUaEEEIIId0oMCKEEEII6UaBESGEEEJINwqMCCGEEEK6UWBECCGEENKNAiNCCCGEkG4UGBFCCCGEUGBECCGEEHI4ajEihBBCCOlGgREhhBBCSDcKjAghboWtUK7VarF8+XK7HO+7776DXq9HQUGBXY5HCHEtWiuNEKI4H3/8MZqamnDnnXce9bvTTz8dbW1tWLdu3WH3t7S0ICAggC2sjTFjxmDbtm1HPba9vR1paWkoKyvjwVBraysPskaPHo0hQ4bwIIkQIm/UYkQIUZwHH3yw1xahjRs34rfffsNdd9111O+2b9/OgyJPT09kZWXBarUe9TfPP/88ampq+P7w4cOh0+mgUqn48RYvXsxbowgh8kaBESFEUYqKilBdXY0JEyYc9bv33nuPtwrNnz+/18CIOe+882A0GpGXl3fY7wsLC/Hqq6/i3HPP5T+PHTv24O/OP/98eHl58eMTQuSNAiNCiGKcc845SExM5PtPPPEEb81ht/vvvx8WiwXff/89TjnlFN4NdqSerrNrr72Wb/fs2XPY7++9914EBQXxrjiGdbf18PX1xcknn4xvvvmGtzoRQuRL6+oCEEKIvdx44428C+znn3/G22+/DX9/f34/az1iLUJsbNHEiRN7fSz7fUxMDKZNmwaDwYC9e/figgsu4L/7/fffsWTJEnzyySfYv3//US1GzOTJk3k3HetOY91shBB5ohYjQohizJs3D2q1GoGBgbj11ltxxRVX8FtqaurB8T/JyclHPY4FTDk5ObwViA2mHjZs2MEWI9bSxAZxT5o0iR+LtSyxsUWZmZmHHaPnuDTOiBB5oxYjQoiisJYfNkvsSLW1tXzLusOOtHPnTthstoPdY6NGjcKaNWv4/rvvvssHY2/atIl3y7Hjs8DpyO644OBgvu0ZnE0IkSdqMSKEKEZdXR2fSn/o+J8eLKhhehsD1DO+qKd7jAVG+fn5KC0t5WOVrr76aowfPx4lJSX8HL0dv+e4PechhMgTBUaEEMXoCXB6C1xCQ0P5trGxsc8ZaYe2GLEWpIsvvpiPWWLT9A89/pHji5iGhobDzkMIkSfqSiOEKMaOHTv6DIx6BkQfOQ2/J+AJDw9HVFQU/3nkyJG85WfDhg14+eWX+e96C6AO1XNcGnhNiLxRYEQIUYyeZTl6puwfio078vPzw+bNmw+7v7OzEwcOHMBpp5122PT7l156ieczOjR7NgugNBoND5x6Sx4ZEhKCjIwMO9eKEOJMFBgRQhQjKSmJb++44w5MmTKFBzGXXnopn6nG9lnyxh9//BFdXV0HB0+zgdesu+zIVqD77rvvqOOzFqP09HSeHftQbGmQtWvXYsGCBTTGiBCZozFGhBDFYAHRlVdeyZfnYEHKAw88wIOiHjfffDMfY8TyHPU4VvfYocrLy3lG7d7+jp2vo6ODH58QIm+0iCwhxK2wzNVsMVjWwmMPPYvOstYqFiARQuSNWowIIW6FrXfGBlX3tsjsiWDLjLA8R2xMEiFE/qjFiBBCCCGkG7UYEUIIIYR0o8CIEEIIIaQbBUaEEEIIId0oMCKEEEII6UaBESGEEEJINwqMCCGEEEK6UWBECCGEENKNAiNCCCGEkG4UGBFCCCGEdKPAiBBCCCEEov8H3VEehSICDo8AAAAASUVORK5CYII=", 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", + "image/png": 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" ] @@ -821,351 +1038,6 @@ "plt.legend(), plt.grid()\n", "plt.show()" ] - }, - { - "cell_type": "markdown", - "id": "004f84c0-2685-4117-9f84-199a29574321", - "metadata": {}, - "source": [ - "## Check with SEOBNRv5EHM" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c0dcc211-7ab8-489d-a0a6-9e2945e2267a", - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No version information file '.version' found\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import esigmapy\n", - "from pyseobnr.generate_waveform import GenerateWaveform, generate_modes_opt\n", - "import lal\n", - "from esigmapy.python_codes import generator_python as gp\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "34165747-ac2d-4245-9f6c-c7b1a78d4f0a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0007736943088216417\n" - ] - } - ], - "source": [ - "m1 = 20.0 # masses (in solar masses)\n", - "m2 = 30.0\n", - "spin1z = 0.5 # dimensionless spins\n", - "spin2z = -0.3\n", - "eccentricity = 0.05 # starting eccentricity\n", - "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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 = 50.0 # starting frequency (in Hz)\n", - "delta_t = 1 / 2048. # time grid-spacing (in s)\n", - "\n", - "# Input parameters for SEOB\n", - "q = m2/m1\n", - "chi_1, chi_2 = spin1z, spin2z\n", - "omega_start = np.pi*f_low*lal.MTSUN_SI #0.0137 # This is the orbital frequency in geometric units with M=1\n", - "# eccentricity = 0.4\n", - "rel_anomaly = mean_anomaly #2.3\n", - "print(omega_start)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "71035b84-3c81-4de0-890e-6fd94772e528", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================ Python ================\n", - "Orbital evolution took: 0.6134227930015186 seconds\n", - "Modes generation took: 0.05245385800662916 seconds\n", - "Generating MR waveform from 50.11548092913955Hz...\n", - "Hybridizing the following modes: [(2, 2), (2, -2)]\n", - "By aligning (2, 2) mode\n", - "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", - "INSPIRAL mode (2, 2) goes from 47.79145657063043Hz to 122.77938006168975Hz\n", - "MERGER mode (2, 2) goes from 50.149759320137925Hz to 349.405367681844Hz\n", - "INSPIRAL mode (2, -2) goes from -122.77938006168975Hz to -47.79145657063043Hz\n", - "MERGER mode (2, -2) goes from -350.04376353886676Hz to -50.14885885724892Hz\n", - "blended.\n", - "================= C =================\n", - "Orbital evolution took: 0.003511044997139834 seconds\n", - "Modes generation took: 0.0018358320085098967 seconds\n", - "Generating MR waveform from 49.83655249209011Hz...\n", - "Hybridizing the following modes: [(2, 2), (2, -2)]\n", - "By aligning (2, 2) mode\n", - "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, 2)] modes\n", - "INSPIRAL mode (2, 2) goes from 47.92635547090774Hz to 119.87538703628631Hz\n", - "MERGER mode (2, 2) goes from 49.87962380222865Hz to 349.00018011199046Hz\n", - "INSPIRAL mode (2, -2) goes from -119.87538703628631Hz to -47.92635547090788Hz\n", - "MERGER mode (2, -2) goes from -349.6894142266106Hz to -49.878717424127885Hz\n", - "blended.\n" - ] - } - ], - "source": [ - "print('================ Python ================')\n", - "imr_modes_py = gp.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", - " 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", - " verbose=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d46a691b-65d2-4d07-9128-bb5a4a563b54", - "metadata": {}, - "outputs": [], - "source": [ - "settings = dict(\n", - " # \"EccIC\" determines the prescription for the starting frequency.\n", - " # EccIC=0 means that omega_start corresponds to the *instantaneous* angular frequency, and\n", - " # EccIC=1 means that omega_start corresponds to the *orbit-averaged* angular frequency [default option]\n", - " EccIC=1,\n", - " # Specify which modes are to be returned,\n", - " return_modes=[(2, 2)],\n", - ")\n", - "# See the pyseobnr/generate_waveform.py file for a description of more settings\n", - "\n", - "\n", - "t_seob, modes_seob = generate_modes_opt(\n", - " q,\n", - " chi_1,\n", - " chi_2,\n", - " omega_start,\n", - " eccentricity=eccentricity,\n", - " rel_anomaly=rel_anomaly,\n", - " approximant=\"SEOBNRv5EHM\",\n", - " settings=settings,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "59da8c7e-c67e-4af3-bf3d-1885da3a4564", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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iSn3uokWLxDZROqu5US8c9bS89dZbJtt/Y9ZpTPtXZFuNYUy7l7SsMfti7OeDejM7dOgg0lWL9nRTewQGBoqeUELLVQX+PSn+e1KR3xRDfk/s7OzEtYeHR6H7PT09IZfLYW9vb9bvSGV+iyvz3avs/pT1nTak3Y3Z78q2EWUvKJUlJ7Yasq3W+BvBmC3ioJsxVmk0JvDu3bsife3555/HmjVrxEHco48+ildeeQU//fQT3nnnHaxcuVKME3zkkUcq/FqULqtWqw26lObixYto3rx5sQORNm3a5D9uDErJoxS8li1bVnCvgNjYWDFmj9IH6SCmNDTGllIai9LfV1KKsH4b//nnH5FmHBISUuIyM2bMEG1CqcY0JpjGO1Z0OUpRpTGFV69eFQdrtDydsKA0zdTU1Artv7HrNJSx6zW0nQxt94osa4rPB6Ur0/dWf0BNB/CJiYniMRqnSgfklHJ8/vx5EWC0b98eVYF/T6ru92Tq1KkiwH722Wdx+/ZtMW5806ZN+Pnnn8XnnMaZV/S7Z8j3xNjfYlN99yrzW1LWeg1td2P229g2opRz+vePhipQME3DFEo6uWDItlrrbwRjNslEPeaMsRoqNDRUpL517txZysnJyb9/+fLl4v5hw4blp0SSefPmFUpbJPPnz5fat28vKZXKctMl9WmGhlzu3LlT4joaN25cLJVWn05Lz/vkk0+MaoOHH35YbPvJkydLfNyQVNDx48eL9F99W5WW6teuXTupSZMmkkajyb8vNzdXatCgQYkpiHoLFiwQj69cubLYY6dPn5Zeeuklae3atdL+/ful3377TWrevLmkUCikbdu2Gb0cadq0qeTo6Ci5ubmJ9qT37bPPPpOcnJyknj17FvpMGLr/xq7T0PY3dL3G7L8h7V6RZcvbF2M+Hzt27BD3/fPPP1JYWJi4TWmztL/0faaUU9K7d2/RRrbwe5KdnS1NmzZNCgwMFO8npdYeOnSoWv2eGPI5MPT3hFy5ckVq1qxZoX198cUXC7WzMd89Y74nhradqb97Ff0tKW+9hra7MZ8ZYz9fTz/9dP77aG9vL/59LYkh22qNvxGM2SoOuhljlbJ69WrxD3HRAx/6h57uP3fuXKH7aQycnZ2dCAT06EBqw4YN0oQJE8oNuml86okTJwy6FDxoL4gOYoYOHVrqQczcuXMN3n/aH3rO999/X+EDZDqgoYOjS5cu5d9X2sHaokWLxLpoLCKNS7x7964Yc0sHn3T/n3/+WeJrdOrUSfLx8RFBiSGSkpJE4NKmTZsKLUdtXFJbfvPNN+L+nTt3Gr3/xqzTmPav6HrL2v+KtLshy5a3L8Z8Pmh/6b7bt2+Lv729vcXnmAIaej9u3LghDq4pMJk0aVKJrxcVFSW98MIL4qRZnz59pB9//LFQwF/Vvyfp6enShx9+KAIE2o4lS5aINi04xt3Wf0/K+xwY83tCJxIaNWokAk163r59+0Tw6e7uLj322GMm+Y6U9ztR0barzHevMvtT2nqNaXdj9tvYNqLPPn1eaSz4M888I8nlcunzzz+v0Laa4jfCHL8TjNkiDroZY5VChXgcHBwklUpV6H460KciL0WNGDFC/MNbEnpOeUE3/QNPB9iGXErTrVs3cZa+qIsXL4oDjJ9//lkyxKxZs8TyH3/8cYUPkPVFk1577TVxEKm/0AEMHQDRbQokCqLePSpwpe/N6N69u/TWW2+J2wcOHCj2GhSo0GPUU2QMOmCj52VmZhq9HLUx3Uc9VAVdu3ZN3P/pp58avf+GrtPYQLWi6y2vnYxpd0OXNSRrwtDPxwMPPCB5eXnl/92/f3/poYceEu8HLV+wDb744otir0NBPfU2UqBPPc10YE6BGvXUWsPviR7t49mzZ6vN70lZnwNjf08mTpwo+fv7F/uNoaBK36tpiu9IWb8TlWm7in73Kro/pa3X2HY3Zr9N0UaUOREbG2v0tlb2N8JcvxOM2SIe080YqxQqskJT2OgL8uidPHlSFE8rafnKTD1ERV/otQy50DQpJWndujWuXLlSbNy3frxrSdPaFPXhhx+KOXLpQuPVKyo+Ph4xMTFimiCaK1Z/ofHvVOiHbj/88MOFnkPj8+h5tL20j1QgKykpSYy/LKltqegPeeKJJ4zaNjoxS8or3lTScvrxhqUtS0WajN1/Q9dprMqut7R2MqbdK/oelcTQzweNGy5Y+Eg/9y/tx7vvvivu088XXdLnil6HPvtUTIreJxrfSvtB40BpHKk1/J7QmN2srCw0bNiwxMdr+u8Jze1MRdv0Y7f19NNg6ccLm+K7V9L3pLJtV9HvXkX3p7T1Gtvuxux3ZduIpg+k59KYfWO3tbK/Eeb6nWDMFvE83YyxSqF/lCdMmFDovuzsbFy6dAnjx48vdD9VTqY5fysTdNNzab5SQ9C8oyUZO3YsfvnlF6xevRoTJ07Mv3/p0qXiOV27di1zvR999JE4OKbCcTNnzkRl1K5du8Rq7lTchgKCrVu3iuqzJRXG0h9sUaEbqhD/5JNPwsnJqdByOTk5otgNHXgZcvCvR0EaFVSigyxHR0ejl6P3nuYUpu0vWFxny5Yt4prmITZ2/w1dp7Eqs97S9t+Ydq/oe1SW8j4fVAyKDsILfkeHDRsm7qPiWlTxXP/9Lq1A0t69e8V3hmzbtk3MEUzFv+j51JZU6MqSvydUtJHmKKbvKVV/LklN/z2h7aPAOj09vVAbHTlyRFzrC2xV9rtX2vekMm1Xme9eRfanrPUa2+7G7HdlP1+0XXQSoUGDBkZtqyl+I8z1O8GYLeKgmzFWYeHh4aICatEeqHPnzokz60Xv158RL6nHylD0D31lnq8/cBg0aJCo2EuVahs1aiTO8tMBAR1UKRQKsRwdgFCF2g8++EBcCPUO0O2hQ4dixIgRooJrQQUP1uiAgnoOqCIwuXz5sqh6S4YPHy6mdaKDRZriqaglS5aI7Sj6GB0g08EXtQEFVtTWdLDUuHFjcfBe1Lp160S12bJ6UCdPnoygoCCxTjrYunHjhthP6g2h7TB2Of20T6NGjcLs2bNFNV1qF+qtpB69kSNHiqnliDH7b+g6jWl/Y9ZrzP4b0u7GLGvovhj6+aADZerVK9iLRd8JuhREy1EvcdEppfT0PYL0GtRWdDBN72lubm6p+0IH6H379hUH4+b6PaHXf/DBB0Uvblk9x7b0e2Lo58CY3xOqXj1mzBix/TTTBH2uaRvmzp0r2o72zdjvnjHfE0PbztTfPWN/S8pbr7G/44butzHLPvXUU6KqO50UqFWrlujRXrVqlTjh9sYbb8DPz8+obaXA3BS/ERX5nSjtN4Ixm2bp/HbGmO1at26dGMt1/vz5QvdTkRS6Xz+GrOCYRSp6VFqhKEPGdJsKjWujCr21a9cWBWGoGE/RarT6ysYFt6lv375lVjguKDg42OhKyOUV4KHxc1SIhgra0HZTESQqvlR0TKbeoEGDxHqoYFRpqFgOVb328PAQBbf8/PyksWPHSsePH6/Qcno0zpLG/dWrV0+MKQwKCpLefvttg4qKlbb/xqzTmPY3ZL3G7L8h7W7Msobui6GfDxp/Sc+9fv16mdtG4zkffPDBEh8bOXKktH379vzvk76tHn30Uemvv/4q8Tm0HL0ujQs11+8JFWii9d9///1ljsW2td+TyvymlFW9fPfu3dLgwYPFtlP1bqp+T+N94+PjK/TdM/Z3wpC2M8d3z9jfJ2O+04a0uyH7bcyyNA6fqoj7+vqK/fH09BSfr2XLllVoW03xG1GR34myfiMYs2Uy+p+lA3/GWM2mn1ebzuTXrVtXpFnSGMqCZ/sZY9ZFn/JNqa+9e/cWvVZffPEFduzYgX///bfE7y+l71JPF/Ve01hVc6A0euoJpZ7AsoZGMMas73eiKn4jGLMEDroZYxZH4xkpra+gxYsXY9q0aRbbJsZY+c6fP4/XXntNFHqiFFIaf0qpyfp096IozTUiIgIrVqwwS/OGhYWhfv36ItgueDBPadl0wM8Ys+7fCXP/RjBmKRx0M8YYY4wxxhhjZsJThjHGGGOMMcYYY2bCQTdjjDHGGGOMMWYmHHQzxhhjjDHGGGNmwkE3Y4wxxhhjjDFmJhx0M8YYY4wxxhhjZsJBN2OMMcYYY4wxZiYcdDPGGGPVTHp6Ol5++WUEBASIOavbtWuHP//806Dn7t27FzKZrMTL0aNHzb7tNbHNz549ixEjRiAoKAhOTk7w9vZG9+7dsXz5crNvN2OMMfNTVsFrMMYYY6wKjRs3DidOnMC8efPQpEkTrFixApMmTYJWq8XkyZMNWscnn3yC/v37F7qvVatWZtrimt3mycnJqFevnli+bt26yMjIwB9//IEpU6YgNDQU7733XpXtB2OMMdOTSZIkmWG9jDHGGLOALVu2iF5TfdCnN3jwYFy6dAl3796FQqEos6ebgu1Vq1ZhwoQJVbTVNbvNS9OtWzdERkaK5zPGGLNdnF7OGGOMleLnn3+Gg4MDOnXqJIInPbVajd69e4teyZiYGKtqv7Vr18LV1RUPPPBAofunT58uArhjx45V2bbYYvtZU5v7+vpCqeSkRMYYs3UcdDPGGGOlGD58OJYsWYIbN26IdGu9119/XQRS1Btcq1Ytk7QfJZ5RMGrIpSwXL15E8+bNiwVrbdq0yX/cEDNmzBDrcHd3x5AhQ3Dw4MEa0X4VYao2p1R02r64uDjMnz8f27dvx1tvvWXy7WWMMVa1OOhmjDHGSqEfZztmzBgxXpdQcaxvv/0WX375JXr06CHuW7BgATp06AA7OzvMmjWrQu25b98+8XxDLjTOtzQJCQmiEFdR+vvo8bJ4eHjgpZdeEr3Ue/bsEft679499OvXTwSBpm6/nJwc0SNMy1KATynVhw8fhqXaryIq2+Z6zz33nNg+f39/vPLKK/juu+/w9NNPm3RbGWOMVT3OWWKMMcbKQenRy5Ytw5EjR/DEE0+IwlgvvPBC/uN16tTBhx9+iN9//73CbdmxY8f8wLQ8VCG7LFRpvCKPkfbt24uLHqWBjx07Fq1bt8abb74per1N2X7UsxsSEoJDhw4hMDBQLHf//feLcczOzs5V2n768eyGOHPmjKhQboo213vnnXdE+8TGxmLjxo14/vnnRVE1ygxgjDFmuzjoZowxxgwI6Ch9eejQoSJA/OWXXwo9Tj25ZP369SU+f/To0di1a1f+3xRIHThwAL169cq/j8YEFwziyvzHu4xxvj4+PiX2rCYmJorrknpky+Pp6YmRI0fip59+QlZWlpjWylTt5+Ligg8++CD/76lTp4peXkpJb9u2rUFtZ6r2a9q0abH3tjQ0vZep25zWqV8vpeaTt99+W7SJn5+fQetgjDFmfTjoZowxxspBwZ++t3LNmjVG9cAWDcYpdTs8PFzMw1w0PdrQXtY7d+6gfv36JT5GPdIrV64UPcgFg8sLFy5Uatov/WQnhvbaVrT9rl69KgL7hg0bGtx2pmo/ylignmZjmavNu3TpIk503L59m4NuxhizYRx0M8YYY+WgMdsUdA4bNgyNGzeucHtRryX14K5bt67YFFKmSi+nVHDqrV29ejUmTpyYf//SpUvF87p27Wr0diclJWHTpk2iJ9nR0dFs7ZeZmSnmpqZ5qann2tC2M3V6vrHM0eaExtTL5XI0aNDAhFvLGGOsqnHQzRhjjJWBelAp4KMiX6dPn65wW82ePVtU7KY5ne3t7Ys97ubmJsY+VxYFtoMGDcKzzz6L1NRUNGrUSPTCbtu2DcuXL88PWGm/Bg4cKFK7C6Z303hrSnGmbaEpqyjQpaJnNLUXVSIviHqv+/btK8ZCV7b9cnNz8eCDD6JFixZibLMxbWfK9jNnm5fW7k899ZRoH+rZpmru8fHxorL7X3/9hTfeeKNQL7chbc4YY8y6cNDNGGOMlYLmWKaeSwpyRowYgddeew3JyclijLMxvvrqKxGA7dixo0I9xcaiFO53331XBHU0rrhZs2YiCHzooYfyl6GeZ41GI6apKjrNFQV7lNacnp4uxiPT+GkqcNa5c+f85egxfUp2ZduPtuHRRx8VwemiRYsKpbBXdduZs81La3dKl1+8eLHoGaf2oV5+SsmnNn/kkUeManPGGGPWRybpB2kxxhhjrFDPK40RDgsLEz20169fF8Hnzp07cd999xVqKf38z9TTWbduXZEeTVM/URBJqdW//vordu/eLabjqi6o15mKq507d06Maa5M+z355JOiR52C64KBdXVtO3O1OWOMMevEQTdjjDFWAiraRb29lA5Mc0dT1WwvLy/07NlT9GZS76Q+QKS5uWnKsIKo53LatGmiVzc7O7tQgS0aq1uw19gWUdpzREQEVqxYUan2o6CciprR7YJp2Fu3bsWoUaOqZduZq80ZY4xZJw66GWOMsSL+/PNPTJo0CT/88ANmzJiRf/93332HefPmiTG3KSkpRk+dVVNw+zHGGGP/4aCbMcYYY4wxxhgzE7m5VswYY4wxxhhjjNV0HHQzxhhjjDHGGGNmwkE3Y4wxxhhjjDFmJhx0M8YYY4wxxhhjZsJBN2OMMcYYY4wxZiYcdDPGGGNmkJ6ejpdffhkBAQFiDup27dqJqbSq6vkF0fRmcrkcX3/9NSwhLS0Nb775JgYPHgw/Pz/IZDIxt7kxDG2PvXv3ivWXdDl69GiFtp9e+8knn0TdunXFnOENGjSANdu9ezcee+wxNGvWDC4uLmK7R48ejVOnTlWqbZcsWSLa8eTJkyWuZ+TIkWLOdcYYY4Upi/zNGGOMMRMYN24cTpw4Ieb1btKkCVasWCHm/tZqtZg8ebLZn18QBUmSJKFz586whISEBCxcuBBt27bFmDFj8Ouvvxq9DmPb45NPPkH//v0L3deqVasKbf+rr76K1atXY/78+QgODoaHhwes2YIFC0Sbv/TSS2jRogXi4uLw5Zdfolu3bti+fTsGDBhgts8aY4yxEkiMMcYYM6nNmzdL9E/sihUrCt0/aNAgKSAgQFKr1WZ9flHz5s2TFAqFlJ6eLlmCVqsVFxIXFyf2bebMmQY/35j22LNnj1h21apVJtn2nJwcydXVVXrjjTckWxETE1PsvrS0NKlWrVrSwIEDK9y2ixcvFsueOHGixNcdMWKEFBwcbLL9YIyx6oLTyxljjNm0gwcPirRl6n308vLCiBEjcOPGDYtu09q1a+Hq6ooHHnig0P3Tp09HZGQkjh07ZtbnF0W9mM2bNxepxpagT++uKFO3h6Fo/Q4ODiL9+vPPPxf7QL3F1s7f37/YfdR+1Ot97969Kmvb0tL86RIaGlrh9TLGmK3hoJsxxpjNonHBffv2Rb169bBy5UqRtkxBxcCBA0WgVBGUhq1Wqw26lObixYsiyKXxvwW1adMm//GyVPb5JaWXVzS13BTtUVkVaY8ZM2aI5d3d3TFkyBBxcsZYb731Ft5++21xe8OGDThy5AiWLVsGczJXe9O4/tOnT6Nly5aVbluNRlPi9tC2F0TtVfBCY81pfHnt2rXh7e1t1PYzxpgt46CbMcaYTdq0aRM+/PBDMQ510aJFGD58OMaPHy/G3lLgvX79+vxlaUwr9YBTTy+NWd25c2ep6923bx/s7OwMupTWW0fjaUsKKvT30eNlqezzC4qPj0dYWFihoLuq26OyjGkPynigscw///wz9uzZg2+//VZ8Hvr16yfGMxuDCpHRyRvKoBg1apTo5W7cuLEoFlaRIN4Q5mpvOgmRkZGBd999t9KfNWqHkrZny5YtxZbTX+jzR+8FBf+bN28WJ0MYY6ym4EJqjDHGbNIHH3yAhg0bigCrYK9fSEgInJyccPv27UIBB/WuUbD577//4sEHH8TNmzfh4+NTbL0dO3YU6diGoGrPpSkrndqQVOvKPl9Pvy+dOnWyaHtUlqHt0b59e3HR6927N8aOHYvWrVuLCurU620MqvhNbVBVzNHe77//Pv744w98//33Je6LsZ+133//XfSOF/XKK68US1/Xe/7550WwvXHjRnTo0MGg7WaMseqCg27GGGM2Jzo6GmfOnBG3acxtSTw9PcU19VSuW7cOt27dgrOzM+6//35RRZt6wmlapaJofCtNmWSIoim5ehS8ltRDmJiYKK7LS62t7POLppbb29vnpwtboj0qq7LtQZ8Fms7qp59+QlZWljgpYwhKoz579ixeeOGFUpe5dOkSnnnmGVy4cEGcBPruu+/Qs2dPUa2dpi+jSuC0HtoGqoJO2Rl3794VwW9sbGyxoNbU7U2vN2fOHHz88cci8DVF21LAXfAkTsEsg5KCbnp9anvKSBk6dGi528wYY9UNp5czxhizOfoDe5p3mnoFS7o88sgjYhkqqkaBDI371qNeTwqWzJXeS+u/cuVKsXG3FJgZMnVVZZ9fNOimgFt/csIS7VFZpmgP/XhjY7IE6DUzMzNL7elWqVQi7XzChAkia4B60unvpKQk9OnTBwcOHBDL0Qkimp9c//f+/fvRq1evErfFlO1NATfVPaDLO++8Y/bPWklobm/qaadtKOmkDmOM1QTc080YY8zm6HvfKGgpqcetIOrZLTp+lP6msc7mSu+ldOZffvlFjC+fOHFi/v1Lly4Vz+natWuZ663s8wuifRk9erRF26OyKtseFARTDQDqQXZ0dDTqhAUpLeimyt40lzUNcSC0bd988w22bdsm5rnOycnBnTt3RJD95JNP4scff0Rubq74m9LezdneH330kQh033vvPcycObNKPmtFUTvQflOwXdY2MMZYdcdBN2OMMZtDabz9+/cXAQUFkRQYUE9mVFSUKJ41depUUTiLUK9uampqoefT33R/Sdzc3MoN5MszbNgwDBo0CM8++6x4rUaNGonq6hSELF++HAqFolDPJlVbpzHqdDH2+fqTD1TFndKZC6L2oEvBImqWaA+ydetWUcgrLS1N/H358mX8888/4jYVwaNUd1O0x+TJkxEUFCS22dfXV/Tsf/nll4iJiRG9roa0W8Hx3JQW3qBBgxIfpym1CmYMkODgYHE/od5s6t2myxtvvIHDhw+LddLfFIyaq71pf6ntKJWbCuYdPXq00OMFpz0z9rNmKDrZQNOQUdvR9GNFt4HG3Zc2NIQxxqobDroZY4zZJBqXTJXLqagTjRmlcboUbFFab8ExsVRtmgLz8PBwBAYG5k+DNGXKFLNu35o1a0SlaAp+aHwsVcKmYOahhx4qtBydLKAxv9RjWpHn66dGq1OnjkFF1CzVHhTUURV1vVWrVomLPkCjiuCmaA9Kpf/rr7/EGGLaT8qKoOCXpvoqePKhrHbTowC5rKJf1BNcdAwzjdfWZxZQbzadRKD10HtAf9N+UNsXLPZmalSsjFDgTJeiik7tZWjbGoPea2rj69evl9irX/A9Z4yx6k4mFf3lZYwxxqoZ6nGjIk9UvXnXrl0iwKQeUOoJtXU0TRMVCTt37pwYn1vT28Oc7aZHwSL1Anfp0gVNmzbFa6+9JoqprV27Fk899ZSonE/TjFF6+oABA0TKOGVgUG/v4MGDRWZGWdO0McYYq164kBpjjLFqb/78+SLllyo107RG1BNaXQJMCuaoR9KYwLE6t4c5260oqgpPVd+pV5jacu7cudiwYYMIuIm+N1vf00vBN/XilzaemzHGWPXEPd2MMcYYY4wxxpiZcE83Y4wxxhhjjDFmJhx0M8YYY4wxxhhjZsJBN2OMMcYYY4wxZiYcdDPGGGOMMcYYY2bCQTdjjDHGGGOMMWYmHHQzxhhjjDHGGGNmwkE3Y4wxxhhjjDFmJhx0M8YYYxWg1Wrh6uqK1157zeLt9+uvv0Imk4ntKUl6ejpefvllBAQEwNHREe3atcOff/5Z6WUr+vwlS5aI7aXL3r17i61DkiQ0atRIPN6vXz/UFJVpe2pHfZsWvRw9ejR/ud27d+Oxxx5Ds2bN4OLigrp162L06NE4deqUGfeMMcZqNqWlN4AxxhizRZcuXUJGRgY6d+5s0e2IiIjA66+/LgK1lJSUEpcZN24cTpw4gXnz5qFJkyZYsWIFJk2aJE4cTJ48ucLLVva13NzcsGjRomKB9b59+3Dr1i3xeE1S2bYnn3zyCfr371/ovlatWuXfXrBgARISEvDSSy+hRYsWiIuLw5dffolu3bph+/btGDBggMn3izHGajyJMcYYY0b79ddfJeqUvXnzpkVbb+TIkdKoUaOkqVOnSi4uLsUe37x5s9jOFStWFLp/0KBBUkBAgKRWqyu0bEkMff7ixYvFck888YTk5OQkpaSkFFr+kUcekbp37y61bNlS6tu3r1QTVLbt9+zZI56/atWqMpeLiYkpdl9aWppUq1YtaeDAgRXcesYYY2Xh9HLGGGOsHL/88gtat24tUn6p15B6BI8fPw4vLy80bNjQYu23fPly0Ss8f/78UpdZu3atSDt/4IEHCt0/ffp0REZG4tixYxVatrKvRagXl6xcuTL/PuqtX716tUiBLsmsWbNEyvSZM2dEz7C7uzs8PDzwyCOPiF7bgq5evSpeo1atWnBwcEBQUBAeffRR5OTkwFDTpk0rNW3flCrb9oby9/cvdh+9LvV637t3zySvwRhjrDAOuhljjLEy0BjbF198EWPGjMHWrVsxY8YMTJ06Vdzu1KmT0W1H45XVarVBl7LExsaKbaNU5MDAwFKXu3jxIpo3bw6lsvCIsjZt2uQ/XpFlK/tahALmCRMm4Lfffsu/jwJwuVyOiRMnlvlaY8eOFeO+//nnHxGIr1u3DkOGDEFubq54/Ny5cyL1n8Yzz549W7xfc+fOFQG3SqWCKZniPa1s2+vR55PWQW1L7XHw4MFyn0MnOk6fPo2WLVsa9BqMMcaMw2O6GWOMsVJQj+u3334rilnpg0AaL5ucnIx33nkHU6ZMyV/25s2bojgVFcOiHvHSUM900TG3pblz5w7q169f4mPPPfccmjZtimeffbbMddD43QYNGhS739vbO//xiixb2dfSox5tag8aI09BHwXg1Ntb3nhu6uX+7LPPxO3BgweL3uyHH34Yf//9t7h+9dVXRfBJGQl+fn75z6PHTM0U72ll2556+2mcNo2P9/HxEZ/Hzz//XPy9efNmEYCXFahTfYJ3333XoH1gjDFmHA66GWOMsVJ89NFHore0aK8rpeKSgj3d58+fF0FwWQE36dixoyiWZQgqjlbayYCNGzeKFGtKtS5PWcsUfcyYZSv7WqRv374iRZ+CbUrlprahwl7lKRo8P/jggyIDYc+ePaIXnALhxx9/vFDAbS6meE8r2/bt27cXF73evXuLdqBhEW+++WapQff777+PP/74A99//73YD8YYY6bHQTdjjDFWgujoaJGi/PXXXxd7LDw8XFwXrFxOy7Zt27bctqTxszQVlEH/SBdJNSbUk049ky+88III4KjXnehTpulvOzs7MR0UoV7PknpJExMTC/WkGrtsSSryfAomadzyd999h+zsbFG1mwLG8tSuXbtYW+lfPykpCRqNpsy0e1Oq7HtqirYviaenJ0aOHImffvoJWVlZcHJyKvT4hx9+iDlz5uDjjz/G888/b/T6GWOMGYbHdDPGGGMl0AfWderUKfYYTeVEQV/BoI6Cbv3427JQDywFxYZcQkNDiz0/Pj4eMTExojeYCrnpLzQWmlKE6XbBXmDq6bxy5Uqx8cQXLlwoNp2UMcuWpKLPpx5u2i8KDikAN/SkSEH0mhS0UvBKAapCoch/DyvDkN79yr6npmj7ssabl7QfFHDTWHi60FAJxhhj5sM93YwxxlgJ9GnJVMCqYHo5Fe46fPiw6EEsiNLLn376abOnIlOwTynURVFBNQr+qGCYr69v/v2UYkzV1yklveB+LF26VKy/a9euFVq2JBV9ft26dfHGG2+IauOUIm4ISokumA5NY7kpYKUxzNSjS2nrq1atEr24BdujIp8D6iWmubKpwJu50ssr2/YloR7/TZs2iV74gsMeaNgEBdvvvfceZs6cafR6GWOMGYeDbsYYY6wENL0UpY9TejkFXtSLvX//flFYrWhqeWpqqujBNCS9nAqEVaTquR4FTxRYFrVkyRLRu1v0sWHDhmHQoEGi4BptJ1X8pl7xbdu2iSnH6DnGLkvB/cCBA/HBBx+IS0Veq6STBsZYs2aNSNWm16MibDQ2mdqfxnaTr776Cr169RLB6v/+9z+xLZQhsGHDBvz888/5hdqoB5gC9L1795b4Oj179hQFySh4p32m9RXdj8q+p8a2XUntP3nyZPGZpe2gkww3btwQ2RC0z/TZ0KP76DlDhw7FiBEjRHX3grp161ap/WCMMVaCMmfxZowxxmqwO3fuSEOHDpVcXV0lT09PadSoUdKiRYsoX1favHlz/nIHDhyQfHx8LLqtU6dOlVxcXEp8LC0tTXrxxRel2rVrS/b29lKbNm2klStXVnjZPXv2iDaYOXNmhZ6/ePFi8fwTJ06UuU8tW7aU+vbtW+g+ek167qlTp8T7Qe+Nm5ubNGnSJCkmJqbQspcvX5YeeOAB8d7QtgQFBUnTpk2TsrOz87eV1vXQQw+Vug1arVZ66623pDp16ohlk5KSJHMx9H0qqf3nzp0rtWvXTvLw8JAUCoXk5+cnjR07Vjp+/Hih51J70nNLuzDGGDM9Gf2vpGCcMcYYY4aZP3++SG+mXkk9SkW2t7fnJjQxSoum8chxcXGVShsnW7ZsEcMEaDw+jalmjDHGzIELqTHGGGOVREEbpfzSWGL9Zfz48dyuVo7Gxj/00EMccDPGGDMr7ulmjDHGWI3s6WaMMcaqAgfdjDHGGGOMMcaYmXB6OWOMMcYYY4wxZiYcdDPGGGOMMcYYY2bCQTdjjDHGGGOMMWYmSnOt2NZptVpERkbCzc0NMpnM0pvDGGOMMcYYY8yK0OzbaWlpCAgIEFOFloaD7lJQwF2vXj1zvT+MMcYYY4wxxqqBe/fuITAwsNTHOeguBfVw6xvQ3d0dNUVubi527NiBwYMHw87OztKbw6o5/rwx/syx6ox/4xh/3lh1xr9xQGpqquio1ceOpeGguxT6lHIKuGta0O3s7Cz2mYNuxp83Vt3wbxzjzxurrvj3jfFnznLKG47MhdQYY4wxxhhjjDEz4aCbMcYYY4wxxhgzEw66GWOMMcYYY4wxM6kWY7rnzp2Ld955By+99BK++eab/PLtH374IRYuXIikpCR07doVP/74I1q2bGnpzWWMMcYYY4yZYIpflUrF7WjBOgJKpRLZ2dnQaDTV8n2ws7ODQqGo9HpsPug+ceKECKzbtGlT6P7PPvsMX331FZYsWYImTZpgzpw5GDRoEK5du1ZudTnGGGOMMcaY9aJg+86dOyLwZpZBnZy1a9cWsz2VV0jMlnl6eor9rMw+2nTQnZ6ejocffhi//PKLCKoLfgCox/vdd9/FuHHjxH1Lly5FrVq1sGLFCjz99NMW3GrGGGOMMcbyaDVAagTgGST+vBKVijoejvB0tucmKgUd60dFRYkeSJquSS7nEbOWQCc8KB5zdXWtlu+BJEnIzMxEbGys+LtOnTo1M+ieMWMGRowYgfvuu69Q0E1nvaKjo8Vc03oODg7o27cvDh8+zEE3Y4wxxhizDv/OBA5/Dwz+GL9qhmPO5ivo1sAbfz7V3dJbZrXUarUIhgICAsRUt8yy6f2Ojo7VMugmTk5O4poCb39//wqnmtts0P3nn3/i9OnTIr28KAq4CfVsF0R/h4WFlbi+nJwccSk40bl+rAJdagr9vtakfWaWw583xp85Vp3xbxwzhPLKJlDS6sFwFeacviLuO3cvGTk5Ksjlhqez1qTPGx2zUy8kjSfm9HLLofdAf12d3wdHR0exj1lZWaIjtyBDv282GXTTuAEqmrZjxw7RCKUpmndPjVVaLj4VY6PCa0XRa9TEM2g7d+609CawGoQ/b4w/c6w64984VipJixHJEeKA/L0z7vl3a3OzsXL9VngVPr7nz1seCrZpjG1GRkaNOMlg7dLS0lCdqVQqEXDv27dPZFkURBkXhpBJ+lMUNmTdunUYO3Zsoe59qphHATWlNlCxtEaNGome8Pbt2+cvM3r0aDEQnsZ3G9LTTWNE4uPj4e7+349gdUc/XHRwQEXnqFofY/x5Y9UJ/8Yx/rwxq5J4G3YLuiBbskOLnMWY2iMEdhf/xqjs9UictA09GvkavKqa9PtG1bKpE65+/fpldsAx86IwkgJuKlJdnQupZWdnIzQ0VMSGRT9vFDP6+voiJSWlzJjRJnu6Bw4ciAsXLhS6b/r06WjWrBneeustNGjQQJz9oh8efdBNZyjo7MSnn35a4jopVaBougChH63q/sNVkpq638wy+PPG+DPHqjP+jWOlSroprm5KdTG5W318MKwhZKe/BeTAqphI2DU3vnBTTfi8Fexsq65jiQ01a9Ys0SF59uzZKn9tfUq5/r2oruRyudjHkr5bhn7XbLJ16GxKq1atCl1cXFzg4+MjblOjvPzyy/jkk0+wdu1aXLx4EdOmTRNp4pMnT7b05jPGGGOMMQZ19GXRCtelQLw0sAlkdk5Is9P1bidFh3ILVTMUj1Ccog/gqKPw9ddfF2nyhqDnUYDNbI9N9nQb4s033xS598899xySkpLQtWtXMT6b5+hmjDHGGGPWICviItwAhMqCMNZVN0VYjnMduKXEIzu+5OK/zLYNHToUixcvFsMBDhw4gCeeeEIE3QsWLLD0pjEzssme7pLs3btXzM1d8EwQpVvQHH6Uh0+p5dQLzhhjjDHGmDUI9e2LP9QDEeHRPn9MrMyjrriWUiIsvHXMHGg4Kw2DpfHBlIH78MMPi95rqkf1xRdfFFqWsnUptfnWrVti/Dqhulb0WdH/rbds2TJxn4eHBx566KFCxc2obtWLL74opryiMcm9evUqNAMUxVG0zl27dqFTp04iO7hHjx6iThYzjWoTdDPGGGOMMWZLjrv0x7vqx5FVu2P+fU6+QbrrrCjkqDXm34jwk8DC/sCmV2HLBb0yVWqLXCpbk5rmgaZe78cee0z0gBf022+/oXfv3mjYsGF+kEzLUKdiwaCZgnIK3Ddt2iQu1Nk4b968QhnAq1evFsWkqdA0BfhDhgxBYmJiodd799138eWXX+LkyZOiQjxtEzONaptezhhjjDHGmDULS9CN5Q32ccm/z8k3WFzXkSXgbkImGteiBHQzSg4DIk8Ddk6wVVm5GrT4YLtFXvvy7CFwtq9YSHX8+HGsWLFCFImmotAffPCBuK9Lly4iEF++fDk+//xzsayfn5+4ppmYqKe8aEGzJUuW5A+jnTJliui1/vjjj/NT1+nxYcOGicd/+eUXUXB60aJFeOONN/LXQ8v37dtX3P7f//6HESNGiIxhrhBfedzTzRhjjDHGWFVLvgtEnYUjclDfxzn/bplHoLiuLUvErTjDCmxVSnqc7trF8OnJWMVRT7Srq6sIZLt3744+ffrg+++/R506dUSQS73b+uUo4H3ggQfKXSellResW0Xrio2Nze8FpwC+Z8+e+Y9TETcK7K9cuVJoPW3atCm0DqJfD6sc7ulmjDHGGGOsqp1dgdnRc9Fa2Qf1fHS9i0Kj+/BBw1VYfikHb8RXQdCdkRdUXV4PXFwDtBoHW+NkpxA9zpZ6bWP0799f9DxT4BsQEFBoyikqqka91F9//bVII584caIYX12eotNW0fhs/XRe+vT3ovNo0/1F7yu4Hv1j+vWwyuGgmzHGGGOMsSqmjbksUk6vSfXQu0B6ORxc4VMnBNpL13E7Lt38G5JeoCfz1m6bDLopQKxoindVo2mOaUx1SYYPHy4ep6B869at2L9/f7GgmOYoNwa9lr29PQ4ePJg/dTL1fNO4bZpimVUNTi9njDHGGGPMQnN0h8qD4O/mUOixED9dEH6nCnq6s5Kj82/nRBVON2ZVS6FQiLm83377bREsU/p50TRyGqsdHR0tpkQ2BAXxzz77rBi7vW3bNly+fBlPPvkkMjMz8fjjj5tpT1hRHHQzxhhjjDFWldQ5sEu+LW5meTaBXF44zbdLxO/43u47KOIumX1TMhIi82+roi7hmd9P4lSYYQEdMz0KhFUqVYmVw6myOBVAo+nG2rdvb/A6qZL5+PHjRep6hw4dcPPmTWzfvh1eXl4m3npWGg66GWOMMcYYq0rxNyCTNEiVnOHqV6/Yw35RezFKcRT+2aFIzlSZdVOy4YAcSZea7SbLwtnLlzF+wWFcikwx6+vWRFRBnKb2KgtNB0bTdT366KPFHhs1ahRu3Lgh0sNDQ0PFfbNmzcLZs2cLLUdp4/rHCRVt++677xAXFyeKs1GqeefOnfMf79evnxjjTZXR9dq1ayfuKzofOKsYDroZY4wxxhirSnFXxdV1KRD1fV2LPazwrJc/bdhtM6eYL27yI5rm/I54R91UZX0846sstZ39JycnR/RAv//++3jwwQdRq1Ytbp5qhINuxhhjjDHGqlKsbjz3dW1goTm683nUFVd1ZIm4Y+Zpw1KycnXXrrriXp2cY8R1apbarK/LClu5ciWaNm2KlJQUfPbZZ9w81QwH3YwxxhhjjFWl5qPwk8M0bNF2LTRHdz53XdBdVxaP2/HpVRJ0Z3jqgu5assRC97OqQQXUqDL5qVOnULeu7v1n1QcH3YwxxhhjzOqFJ2Vi3/U43EvMhFarm3vYVmlqt8OX6UNwUNsawb4l9XQH5qeXmzXNO+I0/nfvGcxV/oKoZtOBt8Oxr/4r4iEOuhkzHduY0I4xxhhjjNVYGbcOY//yH/Bx1jhkwAkB9hl42O0cGg99BoNbFy9EZu0ik7OQq5Fgr5SjjrtjGUF3Im6bM7085R4a5t5AshzIcPcFHNzg4WQnHkrN5p5uxkyFe7oZY4wxxpj10qiRufpFTJY240371bBTyPCYdg1mZPwA9zUPI1ejhU1JjUT66b/RQBaJIG/nYtOFFUwv90AGwuOTzdeznx4rruIlj/xg291J1yfHPd2MmQ4H3YwxxhhjzGplHfwRfpk3kCS5ovbI93B59lDcP7AvcqFEN+kcjh7ZD5ty7ziaH3wJn9v9XPJ4buLkBfVLl9BK/TvS1QpEpmSZZ1sy4goH3Qe/xogzT6G7/BJSeUw3YybDQTdjjDHGGLNOKRGQ75snbi5xno5BnVrATiGHf/9nEerVU9yfeHQFbEq6rjp4tORVcuVyIpNB6RWIej5u4k9zjevWpum2JR55QXfUOfjHH0cr2R0OuhkzIQ66GWOMMcaYVcrZ9CYctJk4qW2CViNnFErF9u7+iLjumLYLN2NSYTPSosRVrORVek93npC8ImuhZgq61am69PI4yQNujkrAr7n4u4ksnNPLGTMhDroZY4wxxpj1CT0EhxuboJbk+N3nJdzXonahh33aj0KWzBmBsnjs370JNiOvdzlO8iy9p5tcWosXEj7GBMU+xKerzLIp2rxe9wylF5QKOeDfTPzdWB6O1Gyep5sxU+GgmzHGGGOMWZ2MM6vE9RpNbzwwfAhksiIFx+yckFJ/iLjpfG0tMlW2ESRKeT3dMaKnu4ygO/Yq2qTsRgfZdSRnmifozpU5IE1yQo6Dj+4Ov7ygWxaB1KwcSJJtT81mraKjo/HCCy+gQYMGcHBwQL169TBq1Cjs2rXL0pvGzISDbsYYY4wxZnX+0gzA17njcdl/JHo18i1xGf8eU5AFB1Cn7MZzkbAF6hRd0J0g80KAZwnThel56CqYB8gSkZRpnum7zvT/Ha1zFuG2U2vdHd4NIMnt4CLLQW0pHpkqjVletyYLDQ1Fx44dsXv3bnz22We4cOECtm3bhv79+2PGjBmW3jxmJjxPN2OMMcYYszp/h3viqmY8vu3Trngvdx55w35Y0XsXPtp5F62OhuHBTvVKXdZqpEWLK5l7HV1Kd2ny5+pOQJKZerr104J5ONvr7lDYAb6NgdjLaJw3rtvFgcMFU3ruuefEZ/T48eNwcfkv06Fly5Z47LHHTPpazHpwTzdjjDHGGLMqsWnZuBqdRkW80buxX+kLyhUY260p7JVyXIxIxfnwFFi7Yy3fw4e5U+DgG1z2gu66oDtAloBkM/V05wfdeXN0E1ntNkiEO/xkybZXTE2VUfolN9uIZYtM0VbackZKTEwUvdrUo10w4Nbz9PQ0ep3MNvCpK8YYY4wxZlVitn2OoXI14mv3hrdLXi9sKejxgc38cevSCRy9dg9t61l34HLMqQ8WawLwiE/eOOpy0svdZFlQZSSZfkOizmHwwadhr/THCaeP/rt/3M+YcGcvbsdlYLytBd2fBJT+WOPBwMO6OgHC542A3MySlw3uBUzf/N/f37QGMhOKLzfLuJM8N2/eFOPkmzXTjZ1nNQcH3YwxxhhjVSAlLgJfHUpAZKpK9MzW1USif9Z2NLv/dXjVLqfXsybJSUPzS9/gJ3s1FtbTzcVdnreSZ6O+w14svPYOcF/e+GQrFZeWI6793coYz03sXaBx8IQiJxlOWbqUdJNKCYd/+hU0kefgWoGebuLuqPvb5nq6rZy+MJ3VD4FgJsdBN2OMMcaYmaWEnUfukjGok9sdS9WTxX3D5MfR3f53RCz8F8oX9sHNy5/fBwpMbu+FEmrc0dZCqzYdDGoTh4AWQPxe1I4/IgIbqw1qEm8jMHoXGsuc4edW/skByb0ucmPT4JSbBJVaK07WmEy6bo7ueMmjUHo50f9tc0H3O2UU05MpCv/9xs0yli3Szi9fgCk0btxYfDavXLmCMWPGmGSdzDbwmG7GGGOMMTNKvXEI8iXD4CslYLDyLD4ZUR+zRrXAoO4dkQR31NVG4u6C8cjOLjKOtIZKPrdFXB9AB3QM9jLoOT5NeojrxpqbCE+y4na8sRPPx83CK8p/4O/mUO7ismmb0Uy1FEe0LZGcZeJiahnxJQfdN3fhvbjX8b5yme3N1W3vUvrFztGIZZ0MW9ZI3t7eGDJkCH788UdkZBQfE56cnGz0Oplt4KCbMcYYY8xMnBMvwuXvB+AmpeOCrAnw2FZM7t0S03qGYNyo0Ygf/w/SJSe0VJ3HiR+mQa2u4VM0SRLs7vwrbsbV7g0HZZHeyVLY1+uQP7/0uVAzpGKbuHI5zdHtZ0DQrXDxgpuTbjmTF1PLyOvpRpGgOzsFjbPOoaU81PZ6um3A/PnzodFo0KVLF6xevRo3btwQPd/fffcdunfvbunNY2bCQTdjjDHGmBloVVloE7YIjlDhkKwDXJ/cjAZBQYWWady6K8Lv+xEaSYbe6duw45d3avZ7EXMRrjmxyJQc4NtyoOHPcw9AmtIbSpkWMddOwlpJabo5uuMkT4OCbuKVN51XUoaJe7oLpJe7OxUYcergLq7ckIlUDrpNLiQkBKdPnxbzcr/22mto1aoVBg0ahF27dmHBggWmf0FmFTjoZowxxhgzgxubv0YtJCBK8kHg06sQElDymO1mvcfjRod3xe2h0T/j2tlDNfb9yL26XVwf0rZEj2a66t0GkcmQ4dNK3NREnIa1yk2Jyu/p9nU1IOgOO4w5uZ/jFeUqJJs6AM6IE1cJknvhnm5HDrrNrU6dOvjhhx8QGhqKnJwchIeHY/369ejXr5/ZX5tZBgfdjDHGGGMmlpuZguDLP4vb5xrPQHBt3zKXbzb6DZxz74d0OGHf4cM19v1IDNMVrDpt3xmN/F2Neq5jkC7F3Dv1CnKsNE1fm6oLurMcfWGnMOAwPD0GPXMOorv8MpIzTdzTrXQUn7di6eUObuLKVZbF6eWMmQhXL2eMMcaqudz0BISd+Rc5kZdQp2VveLcYCMj5vLs5/XMxBRtUL2OSch963/+0Qc9xH/sVeiw8g/S7TugXk4YmtXTBT02yyO9/2HK1Hwa01VV5NoZH62FYeuIWdqlboFFUGtpZ4Xzdirxx1BqX2oY9wVG3Dx7IQJKJx3RrH16NNu9ugVaS8qcIK5xenoVUUxdvY6yG4qCbMcYYq4bUuSpcXPkefO9uQ6A6DI30D1z5FjHKOkhs/ADqj/ofnJyNr8DLypadq8G3/95AtLYlagc0w7C8QljlCQlpiN4tk7H1YjR+2nsLX01sV+Oaev/1OIRL/ujUvIHRz5UFdcPeYDn2X4tD/7tJ1hd0q3Ngl5MkbsrdDAy6nfKCbhkF3aYNgNNVamjFtNEyuJfQ020n0yA7q3iFbcaY8fg0N2OMMVbNpGbn4vHfTyPyxhkRcJPbqItDdj2QKjmhljoKzpf+xKBvj+BOPB9Um9qfBy4jOjUbdTwc0bOWiGoM9lw/Oj0iIe78DoRHWXEVbjOIS8vB1eg0Gp6NXo3KTscvTfsg3RRjZ+9Z59RL21t8hlm5j8LJw9fonu7kDNP2dKfk9Zw7KOVwtCtQJd7eFVqFA+Ild+RmpZv0NRmrqbinmzHGGKtG7iVm4rElJ3AjNh0X7GbAofn9COoyEg2D66OBXIawqDgc2rUcR+4kIzwlBw8tPIIVT3ZDQz/jxs+ykmXcu4AJ+wchWTkM/v0+gF3seaOaqnWgB1Z6/4LumXuxbWMyAp/6pMY0dfb6V/Gb3UVsdn8Q3i66it3G6lhLjh7yi0gX04a1h1VROuCoUy8s0QTiafcic0aXxkl3EsFJpkJahgkD4OiL8F31NL6088Snjq8UfkwuR/hzoejz+R44aQ2bso0xVg17uufOnYvOnTvDzc0N/v7+GDNmDK5du1ZoGUmSMGvWLAQEBMDJyUlUA7x06ZLFtpkxxhgzt2sn/8XR76fhRmwaark74Pdn+mPgxBfQOCQEcrlufGxwHT8Me+QVvPjKO2hSyxUxqTlY9NOXiDy5id8gE7i17Ue4IgudHCMwtn1Ahdbh13aouG4XsRKxyak15n1xDd+HAYqzaOpbsYCbdIz8AyvsP8GQ9PVISM+BNfbmE383A4NuB3dI0H13c9N1qekmkRoBp4SLaCoLL5xankdfWC0rVwOVWmu612WshrLJoHvfvn2YMWMGjh49ip07d0KtVmPw4MHIyPgvRe6zzz7DV199JcrxnzhxArVr1xZz4KWlpVl02xljzCZoNdCkxyM1/Apy4kMtvTXMALHR4fDe9DgekLbjVe8jWD+jF1rV9Sh1eZquaOWT3fCgTyg+Un8Nn03TEHFqC7d1JUi52QiO0J280LafBqUh1alL0HDAdCTJvVBblogTmxbVjPckKwle2ffETfcGXSq8GsegjuK6tfyO9aWYx1xCSOwuNJRFGDxHN/U6q+3doZVkUGcmm2WO7kKVy/O4OSpFmr9+uApjrAYG3du2bcO0adPQsmVLtG3bFosXL8bdu3dx6tSp/F7ub775Bu+++y7GjRsnJp1funQpMjMzsWLFCktvPmOMWaXEDBU+23YV/b/Yi6dmfw3FFw3h/ms3OPzQFnfndcHNDZ9Dkxpj6c1kJdBqtAhf8hj8kIwwRRAef/ZN1PYovyfNx9UB/3t6Ko7bdYEDcuG66Snkpunm7mXGu33oH3ggDdGSNzoMGF/hJpTZOSKm2VRxO+TWcnFcU91JEWfEdai2FpqGBFV8RXV0xecay8JxUaSYW5GLq/Fa8hxMUeyEv6FBN4Cbkw+jYc4yXMipZbptyauiXmy6sDzy3R/ib4c56C6/xNOGMWYC1WJMd0pKirj29vYW13fu3EF0dLTo/dZzcHBA3759cfjwYTz9dPGpO2hierropabq0rlyc3PFpabQ72tN2mdmOfx5sw4JcVG4teFzbIh0x1+qHuI+Z5kT4ABRdMsZOQjKvgacngP16U9wq+79CHr4O8DOGbamun7mjvz1GfpkH0OOZAdpzELYOzobvI9uTg4IeeZPXP+hH5pIYbiy/GU0emKJ2be5Oso9+bu4vuA7DP2U8kp93ur2exzaS9+iBW7i4qULaNq0OaqzjJtHQCXDLkgN0M/XqeLfUSc/qO294aRKRMLtU8jNbQFroUiJFL1dsZIXujsqDN5HF1cPSJCLebpVKlWpU6kZ83mTp8VAkdfT7eZQfFsUURfQGZcRKOuFxLQsBHkafpKgKtD20skorVYrLswy9CcE9e9FdaXVasU+0udOoShc58DQ77HNB93UAK+++ip69eolerQJBdykVq3CZwTp77AwXRXXksaJf/jhh8Xu37FjB5ydbe/AsrIobZ/VPBr6UdGoIWnV0GrUUCoUUDiYfzoh/rxZTlL0Ldwf+RV6ytIQBD8cc+6M/oFyBDgFYLniNzjZKRGfmgrHqONol3UIbWW30DBiHb78PhjBTTrCzibzparXZy4jIRzjw76mWX+wzWMicDsW524bnyYe5TcNjWJno3nMJmxe9iXUPtU7yDM1RVYihqUdF+9DmHM7bNmypdKft0aKJmipvYYLWxbi1q1BqM6aXv1XBN13lA2g3bm9UutqpwxEsCoRyuiz2LzZJz9N2tK63L6IOhR0wxNnj+zDVQOPwlUa+r8Saq2EtRu3wrGc5xnyeesYeg6BNMZcckdiTAS2bLlX+PGEdPE41SfYtf8IIr2sK9tCqVSKoaPp6eniRASzrOo+fFelUiErKwv79+8Xw5oLokzqGhF0P//88zh//jwOHjxY7LGiZwIpQC/t7ODbb78tgveCPd316tUTveXu7u6oKehsDf1Y0/h3O7vi6Uas+slUqXH20FYoz/2BtumH4CrLKvQ4HQAdb/cJOnbqgWAf056A4s+bZR3augKDIufBWZYjUpKTeryJHX2HQSYvKZJ+SBTTWb15NSLO7sD3SV3ROtwdPzzUFgGeTrAV1e0zl5mZjoRv+sBBlosLTl0wbMZXpbx/5aN/I3d9cwqDMjehfeQy+D50VKQ5M8Nc+2c2FDIJZ2UtMOWRaaJwXWU/b1dVl4BLnyIg5wa6D/uq1GOY6iD7ou4YTBncDcOHD6/UuiSns8Dh82gmhaJT7wGoZWilcDPTLtBVok+We2PsqGEGv5+y00vhZv831qh7oHPvV1DPq+R/i435vCn++AVI0vV0t2nWCMMH0lR1/5Fv+Rc4cxRuyEL91u0wvA2dLrAe2dnZuHfvHlxdXeHoaB3vr6FiY2PxwQcfiOGyMTEx8PLyQps2bTBz5kx0794dDRo0KLGT8JNPPsFbb72V/zcNnV2wYIEoFC2Xy9G+fXu8/vrrGDlyZP4ye/fuxcCBA/P/prai9b/wwgt46qmn8u+fPn06fv/992KvsW7dOowfPx4ajabE9VGWMQ33nTNnjugANVRoaCgaNmxY7P7Nmzdj6FBdIcklS5bg8ccfL7YMZS/rA13a7uTkZKxdu7bQMvrtTEhIgKenZ/7fdDsiIqLQZ+b48eOi3Yl+P0v6vFFh7j59+hT7vOmzo6t10E0fmA0bNoizDoGBdD5Oh8586Xu869SpU+hDXrT3u+AbSJei6EfLVg/MVKpcQKuGvaPxB8S2vN/MMEk0fnf7Vaw7E4lXpVV4Uvmv6KEpKjA3DKMPpiH14EH0bOSDj0Y2QYPauqEcpsKft6pFwdW/K75C/+tzoJRpccWlKxo9/w+Cnco+wUg/CeMnTMb+NoPg+ecZXIhIxaQF+7BqpBJ1OwyDLakun7mt2zZivDYCCTJP1Ju+BPYl/DtmjKYPf47YhQcRoA7H8V0r0WXUfwdlrGxLU9qhoXoUAlv2RDsHe5N83hoMnI4Hz7niRHYINsVnoWVA6YXxbJoqAzflAagrZcK7cefKfzeDOgGHdcXU7iblINDHDdYgN28ctdqlFuztjajQHnsBQ+XHcEUWiHSVVG77GPR5U9ghQ+6OOHiitYtD8eWddPOD04n4jNzyX7OqUXBEJy0o2KSLLXnggQfECRIKmikApsB7165dInjU78vs2bPx5JNPFnoezdqkf5yCayoWTcEuzeJE61u+fDnGjh2Lb7/9VnRKEv3yNMsTdSJSb+3GjRtFQerGjRvnB9DUlhRMUiHqZ555RpwIKPj8otf69dG2U6bw/fffj+vXr4tZpQwhz1vPv//+K4L2gkF8wdei1yg6Q5X+fdffLvh30fXrPx/6v6kN169fj0mTJuUvS8F9UFCQqA9W2meJ7qfXKem7Zeh3Q2mrB4wUcNNZDTpzERISUuhx+psCbzrbR2d99GkBVPX8008/RXVEbXLpwmkkHVsJx+Tr8M4KRaAmEm/kPo1DTv3EWd5ejrcxzPU6Ggx8HB61G1h6k5kFHd6/E5/si8PFDN0B3FHP+9DJ0wU+XSfDt3EH2Nk7Qqm0R2RUBC6f3IfWcUE4djsRh27GI2b+q8gKaIzGD38NezfTBt+samz7bTaG3ftKnGS56DccLZ9eCpnS8APAPk38sPH5Xnht6T58kPQO/DeEI9VlDdyb9jXrdrPCYlKzMeuiH+arv8CXQ3zR2b9upZsoqG4ANreaibVnwnHqTH3sGaiCp3PFp2+qKSKTs/BPmCMkaRL2D+5vsvW6eNeBd9OekC5FY+uF6GobdEt2zpiU+z5ScnKwIbhi06wVUq8rfvd5GX9H+mJiXDp6NPKFxalVsMtJ1N1203UOGcxRFwB7yDKQnGmiehRT1mLG4uM4fC0OY0oopEZTlRE3ZCIxq3rVwLAkCqwpO5fiF6o1RYKDg9GlS+GK/RQc6jsRi6LZm7788kt89913Ih7S+/jjj0WPLGXujh49WmTs6lEwTL285MUXXxSB+enTpwv1Wt933324efOmGHJLwXdZ9Ouj69dee03EZMeOHcOoUaOwfft28frU+al/Tf3rnjt3TsRjej4+PqXuJ6FAt6zHjTV16lT89ttv+UE3nYT4888/xbZ99NFHMCfbOjWUh87O0NkcqkROH0p6U+lCDad/g15++WWRIkEfgosXL4pq5zQ2e/LkyahOsrJVOLBpKc583B+t1gxA74hf0DljHxpqw0S6YZTkjYQMFS5HpSLz7hm0v/ED3BZ0wNVP++HK9oWQNPxDWpMkpaZj+3cz0GXXg/if6gc09nPBn091w69vPYb2z/yKoPYD4OzqKYJuSlGtW7ceBo1+BH880Q17Xu+Hx4NjRSXTltHrkP5Ve0TwvL42Z+WxMCD0gLh9MWQ6Wj23wqiAW6+etzN+erI/4uzqwA5q4M9HoIq7bYYtZqX55t8byM7Vwi+oGTr1HWWyhho8bhru+vVFUmYufjvE08UZYu2ZCFA9oa4h3ggy8TCcYa11B5xbzkdW2yrm4UlZIpi0UyjRpLZr5Vfo4ouIhg/hotQAt+L+m07WotJ1Mz+oJAWc3P2Me67Tf0F3Uqbpxi+n5AXTJVUvh2Ne0C3Lsqnq5Zm5mUZf1Nr/xujSbbovW51t0HqNRenwdKG07YIFnI2xcuVKsY6SCkNTAEy93qtXry7xufQbQmntlJrftWvXQo9RgTCKnb7//nuEh4cbtC0FZ4bS9/hS8E7BdsFt0Gg0+Pvvv/Hwww8Xej71kFPg3rNnT/zzzz8wtylTpuDAgQOiV5vQNtavXx8dOnQw+2vbZE83jV8g/fr1K3Q/TR1GwTV58803RRD+3HPPISkpSXywqCgaBenVxebzUdi85nfMh26MEM3heMWtG7ICusM5oAX8G7TGz971EZ2qEj0iyddzcO78SbRVX0CzrDPAkTMIO/UzHMf9gFrNdGMZWPUVk5CE0AXjMUR9SvRw+vgHYOPjHeHo7GZwkPXeM9NxYJcPAg++jRApArmbpuJu1ncI6l34R5RZpzN3kzBzw2Xkal7Cj21iMHzC43SWssLr83Z1RL3pv+PSL0PQErcR8+sY+L+8H7K8A0RmPmGht3HhFJ08CcZbQ5uZdKyvnUKOlwY2wYwVp7H60CU81ckDrl4mnKqoGs7NHXToHQySt8TgDrppvkxpYGMPfGr/K3qln8ONu/vRJPi/4XTVxcUwXUDarI4bHJSFKwNXVEM/XfB+Ky4dVsHJE+ubzMPBS7fhZ+wY87yebndkINJUPd3lBd0O7tDIKEyQkGpDQXfXFYUDSUN80fcLDKk/RNzedXcXXt/3OjrV6oTFQxfnLzN09VAk5SQVe+6FqReMLgBH6cyUOv7TTz+JYI96vB966CExrluPxlW/9957hZ67adMmEftQGjeNhy5piEJAQAA8PDzEMgXph+FSoE+VuCl9ncYnF0Xp6e3atRPjyxctWlTqfujXR0E3BfIdO3bM7zWn4H3ixIkiGNePyd61a5eIxyi1ntBJg6+++koE25S6TcOF6TmUcv/II48UmqGKli2oR48eIqYr2C5FlyltbDYF+MOGDRPvAY2rp17vxx57DFXBJoNuQ8700gHIrFmzxKW6CvJ2xrbsFrjq3BDZgb0QMuQFtKzbuMQD4xYB7kCzccD943D92kVE7F2M9pErEay6Cc3KYbgQNBnNp35vkf1g5hceHYfYhWPRVXsB2bBHzMBv0LwCgTJ9r3rfNxoJnfpj//yH0Ue1H3X/nYE72WkIGfSMWbadmUZschqeXXYKKo0WQ1sGYNiEkZUKuPUaBfrj8JiliF53P2rnhOHuL5MQ9PxmGgBlku1mJbu75gNsstuINX7T0SVkhMmbaWir2njW4yiezf4FoatHo9UTP/NbUYqwU9sxSr0dXeyOw7X12yZvJ1cXV/RxuIk6uQnYfHANmgS/WL3eC0lCv019sMveBWv8THcc0tI+CpMUu5AZTcMujA/ETM7BDYfse2CVJgivGzFHd9Ge7kum6OlWq4DFQzEvXYWpeKXkoLvNRPyR2Q0fbLiMYTYUdNsCKkw2YsQI0eN65MgR0fNM6dy//vprfufhG2+8kX9br25dw4YQlVQ4ml6LOh4p6KbCYTTmm8ZPP/vss8WeT0NxBwwYIHrNS0Prc3FxwalTp8QJAgpeC45tph5tKk4WGRkpTgT88ccfokCifqy4r68vXnnllfzlO3XqJIJyaoeCQTdtM6XBF0QFzQrq379/foesHqW6F1xPQRRkv/TSS+Jxav9Vq1aJ/TE3mwy6mU7rQA+seKoHGgefhEJh+AFuk6at0KTpl7h9ZwYu/PUKemfvxZU74fho0XH8+FBbbt5qJjQ8AmmLxqKDdA0ZcEL6+BUIbj2gUuv08XRH+5dXYdf3UzEwaxtCDr2F6yoVmoyoZgeD1YQqV4PLP03Dm9nZWOT3Er54sK1Je0Z7tG+DDdHzMejoVAQlHkbU3p9RZ0Dxf8iZaVy9dBrdUzaLjJWOff+rUmtKCrkMXdu1gvuxLDQKX4OclNlw8ODe7pKknFmne188eqGvo3nGv6fWH4o6NxbC9c5WGhmJaiXxNpw0qQiUZSG4vunqzYQk7Mdcu0VYm90TGTkvwcXB8oe8cWm6dGI/o4NuXaDiARON6ValAxGnQKOIc6GEe0lBt1wOj7x6DqnZlX9NCgS/3XUDIb4uGN2u8vUnSnNs8jGjn2Ov+O97OzBooFiHXFb4uHrb+G0wJSpaRlXm6UI9rk888YToXdYH2hSUNmpUuKK8XpMmTcS4cKpXVbS3m4JcqqZNRdKK1rvSj6+mwmUUlNIY8JKCbuoBHzJkCN55551igX/R9dE20jh1OpFAw3n1RalpjDr1xtN4aXqNtWvXiozksnTr1k2ceCiIesFLawc9Cv6LLlNWejwF/5SaT73wNAadxpVXBe6KsHHdGvgYFXAX1CCkAXq+uQ57O/+E7xSP4vidREz85TgSCg9jYTbsbnwGYhY9hNbSNaTSbJuT1qJWJQNuPTdnR/R4+Q9scxuPVMkJbx5R4ty9ZJOsm5nWzr9/RL/sfzFafggL71PC1QwHn6OGDsMG3yfEbeXBL6HJ5XlTzSV50yxRdf6Sa3cEt7/PbK/T/b7xuCJrCEeocH3jV2Z7HZum1SIwdq+4KW9u+owDvbo9dCmZnXNP4VakrgJ2daENPyWuL0nBaFXPyLHOZXCu01RcN5BF4U68FYzrvncCjRN2IUgWY3zQnZde7ibLNM2Ybgq6KdVYooocypJ7uimd3VF3vynGdJ++myzqULy1+jzUGi3MxdnO2eiLUv7fv4l0m+5zVBYeAlDac02lRYsWyMgw7HNKqeg0P/nPPxfPQPriiy9EjzMFwWWhFHB9LaySzJs3T1Q5P3z4cLnbQ2nhlLI+f/78QvdTHS3q4ab1yOVy0btfljNnzhSadcpcaN9pbDcVs6uq1HLCQXcNR/OI9hsxCYueHYIAD0fciU+D76WFuHdopaU3jVVSeo4aTy47hbezH8VFRQtopm6Eb1PTjt13clBiwIu/YHa9X3FWHYynlp1EbCqftbEmN0Lvotv1z8XtWy1moG5b05x0KYp6zvs+8h4WSaMxMnMmfj8eYZbXqekuXTyLLpn7xW2f++eY9bUc7JSIbKkr1BN8czk02WlmfT1bFHP1CHykRKRJTmjZwzxZB8S1fmfEK/zgLMvBnaObUJ2k3T4uri+iERrXMkERNT0fXc9XiCwKt2Kt4LN7agneSZ+HkfIj8Hczckx3rVZYP/QIeuZ8JwocVlqOLuhOh6PIanGxL2EcfXoc2h9+DovtPjVN0B2mGw9NxR+t4iSIhdC80ZS6TQWhz58/jzt37oj0ZkqrporfemlpafmFovUX/XzQlLZN6dGUgk5VzG/duoWrV6+KMeBUlZzuK1i5XD9tMq2D5v+m11u2bFmh1yuqdevWIkWciqqVhwJq2h4K1PXzZxN6PqWGU4/6hAkTCs1vTWO3acz3lStXxJRgdLKgaDV2fYZE0XagCwX5lUGVyuPi4kSPflXhoJsJTWu7Ye2MnnjB6wTulx9Eg70v4NZWHuNtq7RaCa/8dRbXYtKQ7hoC3xf3wCvEPJUZ7e0UmPnIEDTyd0VMag4+XrIGOUkccFnL5yDsz9fgI0tFpF0wGo//wKyvV9vLBY7DP0IMvPH59msITzK+sisrW+y/30Iuk8Tc6rWbdDJ7c3UbMQ13URvuSMeVzT/w21NE9HFdtd0LTp3h7WHGQq0yGaJr66Yik93R9axXF6qI8+I6xaOZKOJnMl4h0EIGd1kWoiLvwdKktChxTfNiG93TrVDCxd0HEuRINmFPd4bkCHdHZcnDjWQyeN7dif6Kc0jPrFiV7YJO302CEmo4QIVLkbrgsSaigl9U3Pnrr78WadytWrXC+++/Lwqr0bzbepRyTr2+BS9UJFrvm2++ET3LlL5NATIVMqOpuKgqetHAlTRt2lSsg9KwaQw2pVeXF1BTYGrojAnTp08XVdML7gOluHfu3FmcXHi4SNVyQnOM01huWob2g8aFFxznTehEQ9F2oAudRKgMSsunFH5TDrUrj+UHuDCrQXN5T3/2Taz/5jJGa3ci5Oj7uOfmg3q9qtc0azXBxr8WIuNqMuyVbfDzlI6o7WHkWXUjuTna4ZdHO2HeDz/i44QvEb2wIYJe3QWZXeFiF6xq7dz6D4Zk6yp82o/9HjKlkQd6FTCpcxDWn4nE8dBErFy5BK9PnwRZ3nhEVjmRMTHonLRFjOV27vN8lTSni5MDjjScjqBbc1Hr0q/AmNcBRcmpqDWRT/i/4jq74VCzv5Zb8/uAiL9RP/UkcjVa0waoliJJcEm+Km4qA1qbdt12jshwrAO37EhkRl2j/kFYkiYlUhx0x0pe8HExfuy/l4vue2fK9PIMOJaaWk6F3/Q0OWniJC5lR1YEBW5RYdew2/4DMZ3tkntrgfbmG9dtzWjMM82DTZfShIYaNlUjpUaXlx5N1c4NCZypmndRNH84zfttyPpoXHViYt489AVQ0bbS5sueOrXs2R5oPHlpY8rL2u6StrO8dhgzZozZp2SsBr/YzJRcnRyQ2+oR7HAaLnpTav37AmLO7eRGtiH79u/GoKvv43e7eVjYR4X2QVUT8FBxlMdH9ocGcgRnXcLNpTOq5HVZyaISktDk+Pvi9rV6D8C3Rd8qaSo6KJs7vjXesvsbb8S+jdBV7/BbZCL/7t0nZh8IVwYhuIvp5uUuT4f7n0OC5A4fTQJun91XZa9r7VKSEpCVq0GupECjnmPN/nr12g/GVdTHHk0bnL+bgGohPQbO6hRoJBn8G5i+kGuup64wmyzhJiwuXdczl+PkB2UFTpg0PD0XP9p9A6eMSJOll1Nx1VKDbqUDJIXuRK2rlIV01X9zWRsrMiUbV9KcECSPQy1ZMtLuGTfNFmPVAQfdrBg7hQydnlmIg3Y9YA81XNdOQdLNk9xSNiA8Jh5Bu54T4/7uenRCv4HmK+xTki6dOuNgu8/FnPGNw1cj5oSuqi+regvX7IArMpAo90ajSbox3VWF5sf1b6sbJ1Xv9l9Qxd6q0tevjjJy1Pj8iocY03n3voUmme7NUN4e7lgZ+B76qL7B4nu1q+x1rd3esGwMyvkcU9x+RVBAgNlfT+7ihe8a/4Y56ik4dLt6FK2UNCqsQ39s13ZGs3r+Jl+/nb+umJpbRig0WvP2YpVJq4EiW9cLKHOt2CwAbqE7MEJxHG6qWJHpUCmSBio7d6RILiVXLs8jc3TXvbYsEymVGEtO47lzYI8rUn3xtyb+ptl7FRmzNhx0s1IrUzd5diXOyFvCBVnQ/vEAsjNSuLWsGB1QXFj6iigakyj3QfDTf4lxYFVt2OiHsdVtnLhtt/UVaDKKpxsx8zp4Ix6Lb7liSO6XSBvzOxTOVZ/ePez+B3FI1h5KaBCx9r0qf/3q5p9T4UjLViPA1wvdunSr8tdv1288wiU/rDsbgSyVpspf3xrtuBwjrju0alFlr9mzka+4PngzHtVBjMwfL2c/iRc0r4i6IKbm3OMJPKz+AD+qRiAyufRKzWaXmQAZJHFC2sG9YhXa5c7/zdVd6WnDWo7FH/324/HcN8oMuvUp5q7IqlgxtcizwN5PxXhuku7dUlz7qyJE7zdjNQkH3axU/t6e8Hp8NU6gFV7LeQKztoVxa1mxbRtWYFjmBnFbM+p7KFy8LbIdlF7cdurnuCUFwFubiNvLqmbsKdOhcXdzt14Rt+/v1hLBbXpbpGmc7ZVI6vaWuB0StQVZ987yW1SJ9/TU/k2QQYvpPetXeFxlZfRo6INALycR+G87z/8W5GSl4+g1XcHIQS2qbv7yng19RSEq5b3DyKxEuq+1uBqdmj88yUFZQgXtSlLUboEE385IgjtuxulSqi2ZWp4IN/i6V2yaKZmTZ4G5uis/rlsfRJeaXk4c/uvprtBc3aeWAHs/Qe1ry8SfLnWaiOv68mhciuCOHFazcNDNylS/bh3kTF6HfVI7/HniHladtHwFUFbc9bC76HhG15t4I3gi/NpXbVp5UYF+PrjV83MxTq9x9GZEntxo0e2pSfYe3Ieg6J1wdVDghQG6KXMsZfDAIfhXoQv6Y9a+a9FtsWUnDu3Ed9nvYrvjO5jQ3jLp3RToP95KgUV2n6PTtjGiAFZNFrp3KQ7gccxyXoW2gbpgqCoEeyhwxvEZrFDOxvkLtn8iK+r2JdBM0TSDijmHu5BbsRYMuj3q4u+QOZidO8X4yuVF5uqmnm5TTBtmWNDtBjUUcEQuUivS050WLa7uJeue619flxVSXxaDy1E1t4I5q5k46Gbl6tXED6/cpzs7+dP63bh7nAMoa6JSa7Hnz29RW5aIaGVdNJr8FazBoEEjsNFjEn5Qj8aLR92gruwYNFauHLUGzntnYYH9t1gctB0+ruavVl4We6Uc6P+OKDRVP/Eg0q5xEa6KUB1ZKK5zfFvBucA8p1VtaOcW6C6/jHrqMERerF7TVhkr9+pOUTsj0M+rSjMPZHaOiHFuLG4nXNBVTrdZmlw8cGwCLjlMR0dP86V+j5T24X/KlYiNtGCGhpMX9il7YoO2J/wrGnSbsqf7yI+YdPVFjJYfLDvonrIWTwVvwzZtF6RmVSCzIl03BCNK6wFfVwf41Gsm/q4vizbZtGE8NpxVhcrOC054yjBmkOf7N0LUjdP4X9T/oNwiIT1wD1wDmnPrWYH5e2/im6T+iHayw4sPjILMwfTj4iqC5j7s8thXeP/r/UgLz8CK43fxaHddERVmHru2/IPh2jOiZ6LViGetopkH9OiOrfsHoW3OSRw6fRMTm1ZNFfXqIjwqGp0y9olpwvz7W/Y9rePvhwOufdE7Ywfi9y9CQGvdvNE1jlaDoJQT4qZHS13BwKqUU68XcO0CXCIOw6bF3xDzNqfBCYFBuirj5tAz9g8MU97Ax9FdAVjuMxuXppvr2hQ93ZUe0x1zGU0yTiJA1gjujmUE3Qq7/DHfFRrTnRd0x0me6BDkCZlPQ8Q0mYyFl2S4GqEb511RdnZ24jgjLi4Ofn5+VTrfMiscjKpUKjG1mFxe/fpyJUkS+0efM9o/mt+7ojjoZgahM/lvPDwKt7/6Bu2lywhf+ghc3zgCKCv+4WOVFxqfgfl7qTK0DB3GvAivpuavoGuMAE8nvDm0Kd5ffwlfb7+CUQ2V8PIPtPRmVUupWTkIPv2puH07+EE0qa3LTrGG3w73kR9jwIqLkF1yQP+0bPi7Wa631tZc2fkbBslUCFcGI7CFZcbnF2TXeSqwdwcaxe2EOjMFSmcP1DRRV4+iDtKRKjmjeed+Vf76tWlmgGsL0FJ1Fglp2fCx0e+TOuqCOAi9KtVDszrm+xxJ3o2A1BuwT7HgLAr3jqN50m7EyGpX/PdP39Mty0B8ZXu6VWniKr2sebrzeFQ06Kaewbyx7BR0Dw/2Auxd4Dj2Oyw6vwNIUYkee0/nih1HKhQKBAYGIjw83OB5rZl5gtKsrCw4OTlV6xMfzs7OCAoKqtSJBQ66mcG83Zxx78ElSPxzAAJzbuLm6ploNHEut6AFf+jW/PkrHNQB6No4CCPb1LHK92JSlyAcOHwQL6R8jrQlzvB6/RBFYpberGpnz9pfMRq3kAlHNBj/IaxJr9YN0TIoBmfuJmPJoVC8OVSXYsjKRkMyAu/8I26ntJiMQCs4oOnQcxhC9wagPiJxafdytBw5AzVN9JmtoF/bq47t0MWp6gNezyY9xPRLfrIU7D13HP169YEtSg07Cyr3eVMWjI5eTmZ7HScq3hW6Ff6q8EoFeZVycjE+zFmBz+QT4ef2UMXW0flJfBrfEz8djcHTle3pzpunO1MqJ+i+vB5TwhYBivpIzQ427jWykwGtbjsT4I4OQbpZNOj16nk74V5iFi5HpqJHXkX+inB1dUXjxo2Rm1v5Me6sYqjt9+/fjz59+ojsg+qITvAolcpKn1QwKujesEFXGdkYgwYNEmc/WPXQtnlTrG/yNkbfeBf1r/yE5Jtj4NmIUrZYVdt/6CBeiPsQUxxckX3fXqs9w6hUyPH0kI4IWRUN18xshO/7DYH9n7D0ZlUryRnZaHFtvkhBjmjxJBq7V101ZUPQZ/PZvg0xY9kxpBxdioxmE+BSv6OlN8vqnTq2D12lW1SrGo3uewzWwN5OgVt1x6B+xHzYXfoLqIFBt/O9A+I6M9BCmQdKB0S4tUWDtBNIvbwLsNGgOzfyorhOcWti1nHx9v66rJ8QWTRuxWWgY3DVB93qtFhxwB0Hj4qnlzu4wtnNCxLiKj+mW5WR39Pt7lRGKJAUikaJ+9BGrsF+Y3u681LLkyRXaOX2aBOYl82gysBQ71jsTUrBpcjmlQq69QERXZhlUNur1Wo4OjpW26DbVIwKuseMGWP0gdaNGzfQoIH5xuqwqjd04rPY9+lW9M09iMy/n4THG8cgs+MTK1UpPTsXrrvegp1Mg1SvNmgYbN3fsY6tmmPdvmkYE/cTXA7MgdRtQv70J6zy9m9Ygvtl4ciAMxqNes0qm/S+5rUwz30Vxqs24s6mKwh5fp2lN8nqxR5fLa5vePVDS3d/WIu6vacAf85Ho8zzyIy/B2ffeqgpNNnpaJB9Qdz2bzfUYtshhfQGzp+AZ8xR2CqnpKu6G7XMPM+5j24WhwbyKByOS0dHSnOuYpq0GHHAnS73hKtDxZNMPZ11QU1SpYPuvJ7u8tLL8+bpdqvIPN2utXCq02f488hNtAhwh6NdXmB8fCHeDZ+Flsoe2BvZpeL7wJiNMTrHMzo6WgyaN+RC+e+s+qG5NP0f+gFxkgcCVGG4vO5zS29SjfPvX9+jo3QJ2bBH4OTvYQu6PPQO7kh14KVNwq3VMy29OdUGHQitu5aBC9r6iGr2KGROVX9AaQjqyXLprstwCI7fC1VM3gE3K1FsajZejh2G8Tkz4TZYN9+5tWjatDnWKIfjA/U07LmTiZrkYngCvs6dgK3ogaYt2llsO/w7T8Ac9cP4OHNMfoEum5KVBHeVbryvW702VRJ0ByABYTHxsARZRpy4VjtXolc3NQp9rs3BJ8pfKj1lmJSjG9OdITmWXUhNP083soyfMszZGxu1vbBK0y8/tVzwbiiueNowVtMYFXRPnTrVqFTxRx55BO7uui8sq16aNwzBkRbvY6F6BKZfao/YtGxLb1KNcfNeFHre/kbcjmzzAhz8QmALAnw8cKH1/8Tt+jeXISfmhqU3qVpYejgUu7Ob4TXPb9FgnHWN5S5qQO8+OCDvBDkk3NuoK/rGSrbqVDholj1ZUDcENbeu3iDKYrvdeRaWawZh3RXdwXtNsT9Mhfma0djQaA4UCsvVpnCr1xIHfR/CNSkIp8ISYXMkCQvtHsbv6kFoUK+ueV/L2Qc5SnfIZRJyYm6iykkSlFkJutuulchY0eQgOHQVxioOVTq9XAs5NJIM6XCCS1k97466lHA3WWaFqpfTmG3Srl6BzDYffdCtS/fPztUYvV7GbJFR/2IsXrwYbm66VBNDLFiwAL6+lRurwazXsAmPY73/s4jNluPjzVcsvTk1xpVVs0UBnRhlXTS4XxfE2orBo6fgqLw9lNDg3up3Lb05Ni89R43fDt0Rt58f2ARye+uuYkzzdie1140Brhe+AZqUSEtvklXSaiWsOXFb3J7Y2TpTt0e11c2UsO9aXMWmErJRB27oekp7Nbb8sY0+TfpEaOWmXrKEdIU7PkkbgQ/U09GstuHHlRUik+Fcv9/QLft7HEu3wDCN7GTIJd13ROlWiXobeVOGOclUSM+oXIZJzLQjaJizHKHyIPG7XF56uSv1dGcbOU93+Ck0TD6EAMSjjkeBf5u8dB0FnrIMuGlTcTW6Zp24YzWXSU7TUqn4iIiIYvdfunTJFKtnVspOIce8cW1A9U82ng3HmeO64jLMfI6dOYdBKat0fwyebXNTttGYrrSe74jbWbG3kJrG/9hWdl7uSTmr0dJXhhGtrbN6fVEDBt+P02gGe6gRtvkLS2+OVTp37jTWZEzFHIdlGNG6NqxR09pu6OynwYPYjss7l6ImSE+KRkD4JngjFb0b+Vl6c9CrthYTFPvgcfUv2JpreYGWv5sDvFzM/++YV+NuiIYPwhItkJWXrkstpynmPN0rcYLBwR0SVcukzvOsJDGDSUVl5FAALSu7lzvvNfU93WnZRp5cO/4z5mZ/hJGKI/BxLfAe2zsDbgH5xe30veGMVXeVDrr/+ecfNGnSBMOHD0ebNm1w7Nix/MemTJlS2dUzK9c60APPdPbEGvuZaLZlPLLjwyy9SdWWRivh692h2KTtjjtuHVCr83jYogH9B+EF588xKvtD/Ho02tKbY7Myc3IRdO5rvGX3J76qvRMKM1b/NSUqIhTW7Elxu9aNlflT17D/xB3+HR6yTHRxS4Czg/VWg33O/xLm2C1GrYs/oyYIO7oe3yh/wArnzxHkY/maNZ0cw/GF3c8Yk7YCWSrbStFNvvQvgmXRaFbLpUpeL9BL935Rb21KZafbMpZbLawInoMPcqfBr2DwaSyaajMv3dtZm46MSrznlCVFyg26HXVBtxNUyM7VQqXWGvwa2rSY/Dm6vYpO01Ygxfx6DJ98ZzVDpYPuOXPm4PTp0zh37hx+++03PPbYY1ixYoV4rDJn4ZjteGZYJ0gKezghB+ErXrD05lRbf5+8h6NxSnykfAFeT20SKXO2iILDUSPuF2fZFx8JQ0oli7DWVHv/3Yj2uIoc2KHhyDdgS3oOn4zbUh2cU9fHlZu6NGqmk5WjRrPYreK2ov0kq26WBn0ni3GhDVTXkRRe/QvjqW/sFtfRvt1hDXyb9YQWMgTJYnHpug3VyNBq0fvk89jn8Cp6eFZNarxT+l3MdP4HryhX4V5SFRf/c/TAPmV3rNP2gm9FpwvTy5v1wwMZxvc866XHov6WR/Cd3fdwsS8n6HYLgObtKLTI+U38acxr6oPueHgWnxvdWzfjSn15NMKTsozdA8ZqZtBNk6L7+enSrDp16iQmSP/5558xe/Zsq503mJmWu5MDUgd+hlxJIeZzjD6mm+aGmQ6dlf5yx3Vx+8WBjeHpVjW9A+YyqEUtdAjyhCI3A+pbe0ShGWZc1oPr6Z/E7buB90PpqUvVsxX+7s5Y2HQRJue+h18uGt5zUhOcPLQdQbIYMZVPSM8HYc2Cg4Jx3r6tuB22/w9Ua5KEuknHxU3nZvfBGtC0i1H2uvGxsZf3wWYk3YG9NhvZkh38g808XZheVhKma9fgIcUe3E2s+or78em6s8u+rpULumV547o9ZBR0GznGWi8zEV7RB9FLfgEuDuXMby2XQ+HgnB+cG/WaefN0Zzv4FM/EajEaN9u9hV2aDgiv6pMgjNlq0O3v74/z58/n/+3j44OdO3fiypUrhe5n1Vufnr2x3eMBcVu54y1IqgxLb1K1smvNr/goZx56eqVgSrdg2Do6IffW4EbYav8/PJa9GHEn+USNMQ4cPYpeal0AUG+4bfVy603q3VJcbzofZZtTHplJ7umV4vqOX3/IHV1h7VIbjBLXnrc3oTqLu30GvlIisiR7NO08CNYizb+juJaF634PbIEUe1lc35TqoklAFU1x6FVfXNWSJSMiroqrvYefROvkXagvi4KfCXu6jZ7Cq4Q5ustNL8/jljetmMFBtyYXymxdO2ucSyhe12gg0OMFnJcaIiIpq1KZsTlqDTafj+Iq6Kz6B93Lli0TgXdB9vb2WLlyJfbts6Ezr6zSQVTbyR8jXPKFryYOt9bP5RY1kdiUdLS++i2GKU7gw/oXyq40akO6NqqFM555B697PxUph6x8dHCSuf97Mf3NLa9ecAxobpPN1raeJ9oHecJdk4RTmxZaenOsQlxyGtqn7RG3vbs/ClvQqN8kkeVUX30bCaEXUF1FnNKl/F+xbw0PN+s5GeLaqIe4Dkg9J6re24KMe7rPyXWpHhr5V1FbOnkhR6HLEEuPqeIhLWeWYVbOFxgtP1zpnm6MX4SJXn9jvbZHxXu684LudMlJ1Ngo1/Z38YX2MzSQRRqeXq6fl1ySQ+nqU+IidT114+zTctSVmgHhk00XsfzPZZi96nCF18FYVaj00XtgYCBq1y65umrPnj0ru3pmQ+rV9sWpJq+I23UvLURO4l1Lb1K1cHj19+Ifu1SZOxqOtq0pwsoTMuINpEpOCFDdQczxfyy9OTbh7LWbGJC1Q9z2HfQabNlTHd1wyOFFDL72PlRxPLb73O5V8JKlI1HujTrthsAW1K1TF+ft24nb9w7bXhVtQ9mF6ToR0gKs67imdqt+4rq5dBs3ImNhCzLDdUF3nHMDMaNFlZDJkOUSKG5qEkJRlTRpuvclAe7wrUwhNeLiC4WzByTIkVrRMd15xSsz4QDn8sZ0k5v/opf6KGrLEg2fNiwvtTweHvByLXkqS6ekq3jQ+TRckFXhcd1xt05j3OnpWGn/MUZceQtn7yVXaD2MVQWeMoyZ1MDxT+OMrDmuaetiw7Fr3LqVdC82EV3DdL2Aie1nQJZXubS6aN6gHjYpdcGFZs887u02wMrDN7BN2xl3nVvAo3l/2LKBHVvhtLwl5JBwd9s3qOn+vOuOH9SjcbvhVEBeRcGICaSFDBW93bGR91AdaXNVCMk4J257t7GukyFKnxAky71gL9Pg1sUTsAXKeF3RPbVP1WbpaD10Q7PsUqt2lhV1qi7opvfJoJ7lcrjnpXobPW92iT3dBvzO6KcNE3N1GxjoewRhW5M5+DT3IXi7lNK7/8eD+Ez7BZrK7hk/rlurAfZ8Au9l96Gt/Ja4q6fiEhav28pFnJnV4inDmEm5OtohbNBCjFXNxuyjWsSn81jNyjj1zxeoI0tEosIX9Ye9hOooLWgw0qi3O+cWYk+usfTmWLXbcelYdUPCy7nPQzVli81WsNejoRKxzaeJ23Vu/1Ojpw+7GZuGf6Od8I32ITQYo5vL3lYE9Z2KjjkLMCNpYsV736zY5dgs9Mv+Em9qn0eztt1gVWQybGn1Ndpn/4QdyXVh9dQ58MjUBb1Oga2r9KXt/XRF51yzIkQxyiqToQu61U6+lS8wHHoQ0xK/xhTFjkqP6c4wdEy3g1uBuboNDPRdfHDEpT/WanvD26WUaQ+9Q/KnDTO6p1smR/bd01BAg+2aToj11X0v28asE3VCGLNGPGUYM7n7u7VGy7qeYpzOVzt1FbeZ8a7fjULvmN/F7YxurwN2TtWyGWt7umKP+2hxO3f3PK5kXoZFB++IQu/3NfdHozpVVIDIzHoMfQihUm24SBkI378ENdWa0xHiul9TP3i7VDIFtYo1qFsL/v61kauRsOeqbaQ4G+PgzXjEwgsJDcbATln5nkpTq9e6F5LgjpNhVTP9VmV95vI6vs4dj7pBurmaq4qzv+71AhCHqJSqm6ZKkRmvu+Gim+mnUhJuolvSRvSRn6/4mG51jphqzuBCanlzdbsiy6gpwxLz5kMvtadbP1d3RaYNk8nwo/NzeE71IhYFfAS/Ia+Lu8coDuLzLRe5qBqzSjxlGDP9h0ouwwcjW8IZ2ahz6guEH1jOrVwBF9Z9AR9ZGqLtAlFvwJPVug0Dh70mxnafzPBHeIyuAAsrLCEtC4FnvkRDWQSe7K2b47Q68HN3wpla48Vt+anFNfKkCxXA8jnxJQbKT2Fc21qwRUNa6rZ7/3kbmi/aQAdv6IKmXo19YY3aB3mBZmSiwCU6JRvWTC2zw+KU9vhWMx7N6uiCuaoib/MAJjn/gudzX8S9xCoKulUZUGp0qdNK9xKqeBurwJRhFc4q6T4DLzb+F//LfRIu9grDe7phRE/33WMIjt+HOkgoo6dbF3SHyKJxz8hp3GJSs/HzuRxs0XbDS4OaQNZwIHL7z8JjDl/ibkquOEHNmLXhKcOYWXQJ8cacwBN4QbkOTntmQqrBaaMVcfpuEt6L7IHP1A9Buu9DQGF9vSum1LpxCN4IXI6Xcmfgx8O6AiyssINbV+BZ+Vqsd5yFLvV0VV+ri/oDnxBz9gZk30T6zSOoac6cP4vHNX/jF/uvMKCepbemYoY2ccca+w8w79Y4ZKfm9exVA7QvT919A08oNqN3I29YIxonPNNzG/6w+xhXLp2GNQtNyIBKrYWzvQL1vKr4d8zZG/a+9aGBwuggr8LSdZkf9Pvm6maC7KQCU4ZVuKebNkulRS6UBqaX5/V0y4zo6T76I15PmInBipPl93Qbm14edgSrt+5ArlqNzvW90KOhj5hP3K7vK5g6rJdYZP6em0jO1M2Nzli1CbqXL1+OWrUKn5nnKcMY6fLgG2IKMR9tPG6tn8eNYsSUUJ9vu4YsOCK27XOo03VCjWi7JwZ1ENf/nLqHiOSqS/2zBdm5GtS9/Ku4Hd1oImTVbKhBuyYh2O/QRxyYnj1d86Z9iTu8TFzfdmkPR58g2KJW9evAXZELO5kGNw9Wn5kI7pzYgj7yc5hsvx8N/au2Z9YY/eRnRSGplGsHYc2ST61GX/k5tPOTi6y4qhbkrQv071ZV0O3ii5UhH+Od3Mfh61ZyFe8K93RXYpqtjBxdwG5QYbe8Aq6ikFqW2qiTDXGSB7yd7cvs6Q6WxSA8KcPgAmiazW/guStTMFx+HC8NbFJonPzotnXRtJYbMlRqbLsYbdi2MmbtQfeJEycwcOBADBs2DM8++yxmz56NDRs24O7d/6aJ4inDarZAfx+cavqq7vbln3kKMQMdvhKGo7fjYK+Q4+X7GqOm6FzfW5yxrquNxNWV79TINOPS7N2zA51wGWooUH+47jtVndBBU1qPt9A150fMDO9Uo6rPZuWo0TRmi7gta/sQbPk9DK81UNyWrmxCdZF9dZe4jvDqWvkiWGakCugirp1jTsKaNTr7KZbaf4q+7pYpdjUifRW+tfsBqqjLVfOCDm7Yr+iONdo+lZ8urFhPdwWD7gNf4qWEj9BTfgHOhgTd3Z/HxhGn8I76caTlGPaaUt6UYXGSJ7xL22+v+pAgg7ssC46qJCTnjQEvU3Yq5HGXxM0oj3bo2ajwHODyyFNYKJ+LD5TLKlVQLSlDhbeW7sayec/i8CVddXTGLBZ0T5kyBQqFAs888wwaNGiAffv2Yfr06ahfvz58fAp/CVjNNXDcU2IKMUeocPevNy29OVaPgo309W9is/27eKtVCgKrOv3Owl7uE4CN9u9hYMxvSDi31dKbYzXjfe2P/yhuh9YeBqWXjeYfl2NI945Q23vgVlwGjtxOQE1x4sguhMiikA17hPSeBFvm2XGsuG6Udgzq7OoxpKh2gm64g10T3QkFa+XZRDd/eP2sS9ZbRConHZ45keKmW1Abi2xC06R9GK04DLsE3bRlVUE/i4uvWylp1hXo6XaSqZCVVcGMsLtH0Sv3MAJkCYZNGWbvDGdXGtctMzylXR90o4yebjtHyIZ9hjcVr4s5w+8ZMm3YveOQSVqEaf1RL7hB8RNh2SkITjqM8Yr9OH0rwrgZdGgY5OX1OBWWiOHfHcCaK2mYkPUPPP4ai1kr9yHFkJMCjJkj6L537x4WLFiA5557Dl988QV27dqFhIQE3LlzB7/99husxfz58xESEgJHR0d07NgRBw4csPQm1bgpxOJ7zoJWkqFxzFYkXz9k6U2yavuPHMHA7J1oIQ/D+A42MP2LiXVpGoR9rkPF7YydH3NvN4BDp06jT67uexMw/A1UV5TmOLq97jO/Z//eGjNne86pFeL6jm8/yJ2sN33ZEK3a90Qk/OAEFW4e2QBbl3DvKupoY6CSFGjUxbrm5y7Kt5ku6G6ECFwJtdL50uN0gW6M5In6QZY5eSjzqi+uHTOqqI0iTqFV0m5RANPX1QRBt4O76B0WsitYrT7nv3m6DRrTTSdJ8uYGNyjozkmHTJWhW17pA6eyirV1fQo3fQaI4XQGjeu+qzsJdlJqijaBuhMQhTToD3gGw0OWiaGyY0almEs7ZyJp00w8+PMRRKVko7GPHbLsfdBSHoZHrjyDSV+uwd5r1W92BmYDQTeljlPgXVRwcDBGj9ZN/2Npf/31F15++WW8++67OHPmDHr37i3S4QumwDPzGzhgCHY53iduJ6x/l5u8FGqNFtrdc6CUaXHLqxc8m/aukW1Va8ibYmxvUMZFJF7YgZoudc/34jNxx70LnIPaoTp7pEsQltrNw7thjyPp8k5Ud3HJ6eiQulvc9uz6CGydUqnATe9+4nb2BdsPusNPbhbX1+yaw9fbujP4ZG61EKesA7lMQtQl66yLkB15UVxf09ZDs9qWOcHk5K+b+cFHFZU/rtmszv2JmTmfY6zioGmCbrkckVOPoU32LwjNdq3YOlRp/83TbW9A0J1wC02PvIGZyqWGpbTnzUueKTnA0bn891mf0RduSE93XtB9XNsMbQJ1Y80LkcuBDlPEzYnKvdh0XpdZUf56jwEnF8Er8w4aSOG4v20A/n5xMLyf3gSVSwAaySOxUP0OPvp9M47cMjITKysZuUd+xt1/3sWp+dNxcO4I7PxyKtZtWIPz95Kqds54ZlFGlUSmYLpt27biQmnlNI67devWVptO/tVXX+Hxxx/HE088If7+5ptvsH37dtFDP3fuXEtvXo1BxVL8Rs/B6pXZ+DpxAn6JSkXzKp4qxBbs3rMTg9WHxPyZtcZ+jJqqU+vm2LptOIZnrkfa9o/h3XqwmJOzJjp7LxnnUxzRXekKj4HVbyx3US3qemCraz0g6zwS9v4Er1bW3btYWXtOnEUryQsKuRx1OoxAdeDUdgywZxUaJB6ApFZBprStOccLkt3eK64Ta/eALUjybgu/2ChoKIDAZFiblLBzoFJi9+yC0cdCc9E7+OmC7kBZnEhnNnfwr0mLBfXzxkse8Ktk0H0t8Rpc7V0R5ZCLdMcEQBaPffcOIEudgXpu9eDv7I+JmyYiLisOrhGu6B/cv9DzZfSfTCZmk5EZ09OdnQKP6/9gsMIHc7OmG1dErbx9zohHP+k4MuVUTC247GXVOZDCT4ptp57umQGlvHdtJ0Pa8wm6yq8iJvQSYlPbw9+9jCJ2ahXU61+EEhJWqftg/ND78HSfvNR1h0awf3IHtL+PRmDiLaxQzsLTv2vw8dMPoGVACUF/EZSSPm/zbTxx8Rs0lEUiv0wmZb2fXoewk/74Vd4b0e1fwcPdQ9DI37ATKVSZ/daJHXC8tQWqnGyoVDnQqHOhcvJHrnsQFN4hcApsjcDAIAR4OkFhYNFCGuKYlRqPrKhryMhWISNHBY1WBqWTG+yd3XUXVy+RPexkp4CdwrC+WzqxkJubC1V2OtQqFdS5KjHNoUyhhFKphJyu7Z2hsLOHUi6zSJFFqwu6GzdujMOHD4uglVLJSdOmTUUw3r17d7Rv314E4VS93NJUKhVOnTqF//3vf4XuHzx4sNiHonJycsRFLzU1VVzTh4QuNYV+X029zy0bN8TPTWYh/FIM5my6jMVTO1h1UZqqlpOrgfuhT8Ttm/5DEFKnZY343JX2efO47zXkrN+C4IxziDu/HZ4trHs8pbks3HcTWzSjkNRqKj5u3rFGfCbknaYDBzajfvxeZMffhcKjjk38xlXE71ckXFTNxSeD/PEA9XZoLb9NldWsY3+s290He3NbYWpYPFoF+cEW0cFnTLpaN9VT8/sq/Hmpys+bol4npMbsR1xymlV8vovSRusKYGW4N7HY9sncAsWBbz1ZLK7GpqGhj3lngtCkRIugO1nmASelLvCoiI+OfYS1t9bm/+0Sort+XpcoIwS6BoqAm7y87+Vi6zgy8QgcFA5Q5AXdmXCEvdyAbVI4wS5vnm6VRov0zGw42JWRMu4ehJNt52LliQh4OSvLXL8s9DDGXX8TTZT18UXCkLKXDT8JpSYH8ZI7lL6NYScrZdud/aFoMACyW/9ignwfNp7rj0e7lT4rhPzQN1AmXEWC5IY1vs9gSbd6UKsLZEG41AambIS0YjxqxV3BYmkmnlykwLynxiE4rxp+IZIE2Y1t2KNpg/c23kRMWg7C5NMw3vEU3Lxrwd+/NpwTLiIodjeC5bHoqL2ACUfuYvGRu+jewBsPNpEh2N8bwbV84O7miuzoa0i6fgS5905ij8MArI6ti6sxaRgkO4mF9n8Ufm1KYsjLgP/g0FT8rhkCe6Ucvdzj8ZT2TxHkKugiA+xVKXBWJ8FVk4KvlE9im7q9GD4wAMex0P5rlNad+n7uNCzTDBa3uyuu4gvlAnSChLizr0MOCUopFwpoYAc1Ptc+jOXqgSLo7ia/jD/t55T6PnyW+yDma8aI2y3loVhv9x60kGMf2uNkl+/w+mDrLSxs6PfaqKCbxm7rhYeH4+zZs/mXefPmifHcVFytWbNmOH/+PCwpPj4eGo2m2HRm9Hd0dPExHtTz/eGHHxa7f8eOHXB2rlnFrMjOnaZP7exkD+yQKXDoVgIWLPsL9X25t1vvXuhVPC+dRa6kwE2vgbiyRVfNuKZ+3qh49RZ5P4yVdiJh40wcDjWiGEo1kZANbL1IBzYyhEix2LK1ZhSWy9UCp6Um6CC7jkPLZiG5wWib+Y0zRnQmcDFSKdKBpaRIbNliYBqkDVjs8hTOJcqRseUURgbZ5th8en/mZj0PF1kOPozJRHglf5Or4vOWmxuCt3J+FvNQy9dugZcJsplNqW+ibkx3tMYDWyz0b5yTKh4ULtSVxeP7QyegumPek/+948JE73663A1bK/gbHqoOxdr0/wLu0oSnh5f5+LZt22Ans8PwrBQxtjQLDti1Y1u563XITQFVWnFFNmTQYu3m7XAvp29tV1QgNmiD0Skprsz32jU7AgPzpg27eje2zGUVmhzcdHsLdxLS4CmllblsHU0zdMG/GK84gEf2XYRvom5oQ1Eu2dHod/UzcXt27hR09NZg27aS3ye72i+gS/qXuJPliAsZnnho/gE80liDEKoxl8c98y6a3VuOOplXcSz3YcRoRsDfUUKnhs2gcGuGLBkQRgsGtML1WmNRK+U07mQ7o3WqFheTZLh6OxQjI2aIqRfzX1ec8NBZn6vFFc04cTvUoQlWK0dCrrATsRf1EFMQ7aGOg68mFil2taHQSlCptXBNvopu9qVPJyjLiEGSRhc4ZsgdESb5i2MPyOSQQwsnKRvOyIazLAeZ0n9ZA3ZSDurKdCd6UEKWvFKTnZ8+nysVPlFDtZ7o37/8vwuMeFZIGjGsju6FRoPrN29hi/oGrFVmZqbpg+6CAgMDxWXkyJH596Wnp4ux05YOuAsq2ptKZ69L6mF9++238eqrrxbq6a5Xr57oGXd3rznBIZ2toYODQYMGwc5OVzjDlOIdTqP5qZkYcO8cFGOOQ+leGzUdnVnc/eXv4vadoPEYNMGA9K0a8Hk7FhiEuI3HsVPVChN69YWvuwtqkj/+XIa+8khoQgbgiQc6oybZlHITHUJno1X6PrgP/RGQV/ifqir/jTPUoo174Iw0dGtSDw+Obo/qJLduFM79cwG3VW4YPlxX4MvWLDkSBpy7hnYNAjB6VEeb+bwtjjyCS5Fp8GrUAcNbW9G/r5KE9+/NQW7kJXTvMQTDOzWyzHZoNdBcfhNySYvaPu4YPty8tVOkSy+Ia3uvQAwfPrzC6+kW2w252lykqdKgOPoDZBGnsEzqi/Z9hsPTVYscTQ7GNRyHqwlXMWPfDDzf5nmMbaSbTUDP08ETMuqJPJPXK+fgZtg2qbOBiy+IAMkF2ejaaxBCfMv+9/ji9uvA3VC0bhKC4cOalrHuHEhX3oGbLAtKTTqGDRtfZhbkn0tP4WBcAmZ1a47hXcooxqe5D1krLuDbW40QlgO07zkAdTyKpJhLEhR/jIVcysV+TWukNx6DlyZ1KHO/kDMMddNz4bf0kij8du3yKdTySkCzFu3hm3AcdW+vEkEqZcgoZFo83jMYLw9sBMdSMwPGgiq10DsVkZyFs9uXATcp+Pwv6M6S7HFF1hD3nFvAtU5/fNOyDbrU94KfqIb/QKmbSt2kn2olRKZkIe6WC85GuENFKd65uWJWFDh5QeHiA4WbH8b6NsTDHr5wd7KDu+MAONm9WuL7kKvVYK6kxYeSQsySkJ3eDndiuuHU6bNo0rwlFEolFHYOUCjtYWdvj0ddfPGYsyfsFDLYyfoiHY/Bzs4BcqXu33Q1fR/Vamg0ajwjyfCkzA4aSYImtzsiMoZBq1GjkcwOc73qwMdCQ1IMoc+OLo/pjmTojJWrqyhWRhdL8/X1FWd+ivZqx8bGFuv9Jg4ODuJSFP0jackDM0sx134/M7gdws/GwhWZuL7uQzR5/FfUdEv23sF32dNx3rMTPpgwDUr+vAk9O7bD+KPLcDo8HenHovD28OaoKVIyVOh28xtMsw/DzTrOsLOzjTGlptJ+6DQkLvgKPpp4xF3YBr9OhQ8eTcGSv+10wNP54oc46XADl+t8BTs73RzL1cWglnXQaO1ODEw8gahbbghqVvGg1VLO3dAVXO3b1M8kn5Oq+rx1CPIWQfeFiCSM7mA90wtSh8e2xNpI1vpgelCABY+r7LCu71a8ti0afTPdzLsd1HOoShE3le61KvVaXet2/e+PC5uAzIM4neuJLn4j0LORb/5DXo5emOM5B8NbDS/19c49dhuT5++Gp4eXYdtEAZLCAdDkiPnBs9S6z3OpQg8hIOYsasMNvm5Ny17Wzg6SRyCQcg+11ZFIU0nwKWVeb/oMXYzUBTftg7zLXa/dYxtw86fDyA1Nwo4rcXiit248f77sVCSrAAfJHjM1j+OXES3Lbw87bwS4AqueccfXO6+j3/nvMDzjKHBief4imzTdsCPgOUwd3hsdg71hqPp+dqj/yAwAMwCNGukZqYiKT4SPbx10cHdBOacDSt5cKujub48G/lTgUlfksnJ07UPvkCuNzHB3Rq6fP87fTUPzzgMq8Bm3A0rMyHEBfA1vO0szdL+Nql5OPdhaI6ZxuXTpUuFxEVWIxpXTFGFFU7ro7x49atYBrDXxcHbA3c7vidsN761Gxj3ryYqwBJpD8tcDt0UaT7cR06A08fhVW0ZnWZ+/r5m4/fuRMCQYM9+mjdu3fRWaycKQDQc0HDANNU392j444j5M3I47optSqzo5c/Ei2msuiVS9Vp16obrxcLLDxx7r8LbdSsQd/u9g1FaoMlPx9d0J2Gb/FvrUs97elZLcrziI/fYvofMlXY0QaxGbloPkzFxRPMnQYlHm4lu3gUjBv5toWEpohWXo0m7VkhxO7iYsOOyoK+DlLsswrJp40c1SaZEBJ7g4GtjvRj2eTl7ipqd4zXKO6w9/h+n33kFfxXl4G9A7KfNpKK7ri2JqpUwblnALaRvfQZvsk6LXtFmdAjndZRjZJkBcLz4UisQMVaHHVEpXPJLzFkap5qBXl85GfS7reDjhswlt0WvYJJz3G4nTaIojaI3fm85HixdX47tnRxsVcBejUMLV3RuNGzSCdw3L8qvOjAq6qVCavoCaIai4miWn56J08V9//VXMG37lyhW88sorYnuo8jqznPuGjsF+ZXcooEXMP2/W6Lfin02bIVeloXVdDwxrZUWpgFaif1N/tApwQ1fNKdz64xXUBDlqDfwuLBS3w0MmQOZsO2d7Tcm11zN4VvUSpic9JtqkOok5tEykat52aQdHX928wdWNurHupIl/hO1N/Xb75HYxptJVnoMmwYGwJcH+PgiSxyE465JVfW8Sj/6BRxXb0cM7tYxU26oRlFf86l5ipug9NRtHD/zZYC7+p34SPm5Opg+6kYlU6nY2UnreVGnOhkwXppf375CnLA2p5QX66TH/VS83JCXYOy/olkWLivIlurUb7qfn40nFZlFx3kFp2GdoXAsXvOS+Fx1Td2HGH6fF1KyErl/56ywuRqYhxr4+XrqvYkW63LtPQ5sZf6D9zGPoNvMAHp30MBr4WfakErNeRqWX04/T+++/b3BhMaogbkkTJ04UJwloarOoqCi0atVKFF6gucSZ5dAUA4pBs6HaMhQNUo4g7vQm+HX4rzZATXEvNhGjrryBBx2ycbfzMq7mXkpv95vdXdFj0xdQRmmRdnMK3Bp1R3W2d/8+DJHOQgM5goa/jpqqZ6cOeGtXCmJSs7HtYjRGt6uL6iBbpUbj6C2iRg3aTER11aT3BKjOvod6mnuIu3MRfiGtYCsyLv8rrsM8uyLQxqau8WveE9gKNMY9XAiLRLuG1pFi7nVpKWbbncNvbpY/iRGQehrf2v2IW9o6iEvrX/Z0UpVh74IDym7YrAnGB6aYo1vP0T2/pzvG2J7u+Jtocegd/E9ph4MOLxr+vKkb8fRfV3HoegruL+810/RBt6eBQXeD/KC71J7uu0fF1YnS5ucuhdvNjXhFtRAJdu5YEJaErzZIeD3oOk4e3In9UaNhp3DBDw93qPQc6jwbDzN50N2nTx9cu3bNqJ5uJyfzTsdQnueee05cmHXp0aUztu4fjeEZa5C77V2g7VCRTlOTnFr9JcbI4pGo8EW7jtU7kKyM3p3aY+fOARis+hdxmz+C20vVt7K7KPR45AdxO9RvABrmzSlbEykVcjzUpR6++fcGVhy9g9Gta1WL34hjR/air+wecmCH+r0nobry8/XHGYd2aK86hbuH/7apoNsvTjetqKxB4XmObYHMPQAJCn/4aGIRcfEQ2jV8yNKbREUM4Jl2Xdy0q9PG0lsDZWYcRisO4ZSssUgxN1vQTTFumm5YlK8oemXanm43ZOFGeaneRaVGIDByOwbI6+KMgxEZBy6+cHSmNOeUstPLaQhqRoF5ug0JupsMxYZQGb69YIfuJfV0UzbC3SPi5gmpKcYGehq+3a0mAIe/h0/ibbxn9weyz/4N1Xk5ukk5GKf0RfeH3kLfJrY5rSGzPUYdwezdu9d8W8JqFDorGDJhFpKW7IAiJxmXL59Fi9adUFPcuBuJ3tFLRW9XRvfX4W1n2ZNT1v5Zcej3OjTbd6FB0iGk3TkBt5DqWc37yJnz6KfaKz4XtYbU3F5uvYc6ByF+7wI8E7UBUUdmoU6vKbB12adWiutQnz5o6qwbJ1ldpYcMAa6dgkfYdpqQB7YgJSYMQZq7Yjqbhl0rXm3akhK928Enbgc0d4/Tt8jSmwMkh8JRm4UcyQ7+1nDyxUs3pKOeLA4HEzPRqb6ZhvBEnkWrpH+RIPOHr2uBQmiWHNOtyhBXYky3g3EnMd3yxoCnlhV0ZyUBWt3jCfCAt7MBQbdvI2Q2sset8xdQr6Se7uS74mQBTTl1RtsIHxjR0y2yAp45CFxYhdjdP8A/44aY2uq0thHajn0FQ1txHR1mpWO6GTOl5iHBWN7gc/TP+QrvH1SZd2yVlbmy5hP4yNIQYxeIev2ftPTmWL3e3bpht72u8mbsxo9QXa0/fA43pUDcdW0H12qeRm+I2h6O6OqnQaAsHjlHbX+mg/i0LLRN2SVuu3d9BNVdSM8HRPDaSHVVBLO2IPTEZnF9XdEItWrZ5gG5MlhXDd876RysgSbygri+LtVF0wArONHkqRti6C9LRlRckvle5/zfeD/7C0xQ7IefKdPLQ/piWbdNeFT1P+PHdKvSxVW65AgXY8Z0X9uGiRFz8YBib9mBft547iTJFRqZUhRVNES9vHH2JaaX56WWX5RCADsnNDa2EJ+9C9BxGnxfPY4vAn/A1+oHcHvATxjXkYeasqrFQTezqAfHjoNk54JTYUnYcqHw9G7V1bmr1zEw6W9xWxrwfrVImTU3uVwGRd83dL1PifuQEXYa1Q19B/4K98FozVw4PKLrDWWAf78nRfXf+ulnkRl+0aabZOP5aIzL+RBLnB9DnRpQxyIwqAGuKpsiU3LAhVO6lG1rp725R1zH+dvuSa86LXXTtjbXXENUspkrdBsg6Y7u9/qmrH5+ETOLcvJCjkIXuGXE0uwh5qFJjcwf21zZMcOFOLhC7hWEdDgb39OdkyauMuFoXE933BW0jtuMrvKrZQf6eRXbKbXcy9le/NttiIYZp/G4YjOUSTeLd8Dc1f12HNc2RcsADzH0qCLkCjlee/wRPP7eAkzoVz2z5Zh146CbWVQtd0c83bcBZNDizKYFyEmwjd6QiqJ/TCI2zIGLLAf3nJqhdrfqW0jJ1Pr26IE9drqDyaiNc1Dd/LTvlrge0y4QtWrrpjlhQOfWrXBYqeu5C9/5o003ydozEYiEL9DzRUBpW1NRVdSRtnPRIecn/B5fserAVe3vzA74R9MHTq1s96SIY70OuKZojC2arjh3x/Ins1URuqlBk92bGhyEmZVMhmxXXYE5TcIds72MOiVKXMfJvA3u8TWUm6NufeVWEi8lvTwdjnA1Zkx33pRhHkgvO9D3a4ZrPb/G1+oJho3nzuN/bgHet/sDrbVXxfRyhcRdr1ARtdKGq7nntR1jVY2DbmZxT/VpgM+cl+M91be4+2f1Hse691osUlJTRY+t8/CPdPNfMoMo6GCt92u4og3Czwlt86c9qQ5uRcaizrXf4YxscRKK/YcO0tNbTxW3695dBylHlx5pa67HpOF8eAqUchlGta05J1W6deoo5pvffyMOWSrrmcKqJGEJGViZ2gZva59F884DYbOU9ljWejHeVz+GkxHZlt4aOCVd1d2oZQXjufNIXrrUYvs0M05rm6Y74ZHj6Gvakw3qHLS7+hU+Uf6KrKxSqn2Xk16eIRk5pjsv6PaSUdBdxr+9brVw3X8otmq7GhV0y310/+6FyKJx8EZ84Qenb8Hjbj/hsLYl2tUzoogaY1aGg25mcTRXpE+fJ6CRZGgctwOJF3VjHqsbrVbCZ9uv4231k/i57Sr4tB5s6U2yOX179cFzbt9hVVYnLD9afbIizm78CbPtlmKb+ydoxHN8FtP9vnEIk2rBRcrEvQPLYYuubfkRS+3m4fngu/AxZaqplWtRxx2BXk7IztXiwJVwWLP9eQf7HYK8jC4yZW1oH8jpu2Ycs2ygN3x+wIScD+DWUJexYg0cfBuIYSuynBRk55rhZJAkQZGpq+Ktdall2nXLlQi68gsmK3dDm51SoZ5ukV5uzJhup7x5uv/f3n2AR1GtfQD/z/ZseiE9ISEQeu+9SJMiWC+IKCoKNvRiuVe9ChbEa72iIlbk83rFgghSpIiA9BZKCDUQWgrpvWx253tmNokJhJCE3Wz7/55nmc3usDs7OTuZd8457yv1dJfW3bueVWguF9yQoLt6re7fjtYcnZGYUYjf071QpnBjpnFy3aD7zz//xD333COXBrt06ZL82DfffINt27ZZavvIRQwdPBwb9OZMsSW/PgMYnacXs9JPBy7iWEqenAF00qjBtt4chyTN5XpsmHmY6udbz6CozPHbSUp2PnomfyPfF7pO4eiHWvh66HAo6DbzPtr3FRxNWbkJUed+xGDlYYwNyoIrkYZzzoi4gHWa5xC88XHYs7J936K9cBaDWvnD0UlBtxZlKE8+gtJyo02nVO1PNWKf2AatI+wnMZ1uxAvojm+xoPw2XMppYG9xfZTmQ1Vunk8veFr4cyuUMGo8za9dktew/zviVdwXtAwLym9tVE+3z/V6us/thM/5DQhBZsOCbv/KoDsNW0+m1/jbvuaweZh+/5YB8KlPNnQiZwu6ly1bhlGjRsl1uOPi4lBaap6DkZ+fjzfeeMOS20gucmLW/I435IyXoaVnkLTuQziTghIDCta8jCghBbOGtYJvQ/4YUQ0TuoSila8SE0pWIPGbWQ6/d3b+uhiRwmXkCV6IuGmGrTfHbkXe9BB+KB+M2QX3IKfwijl/dm7Xrj/REadRDiWihz0IV9M1NhqtFRfRKn83DCXmnjZ7U1KQjXvT38Fq7YsYEWqFIKyJNdcV4YhuOn5WPo/j52w3rzs1rwTZRQZ5elCroAZmnbYiQeuJEF/z9ki1ui2uIot3vugGTy8rDInWmuc2K8ryGlb5RaFEhkFbkUitAXO69dV6uovr6One+REmHH8Gw5RxDezpNg8vj1KkyReJpMBb9t1kdN71JFoIyRjb0X4u2hA1adD9+uuvY9GiRfj888+hVv+VlKBfv344cMD5MguT9bWNicKWcHPQ4b/3bRgLrpjX48B+X/YZHjD9jF+1L+O+HgG23hyHplYq8Ex3AS+rv0G7C9+h6FK8Q5eQ6pD4qXw/s/00c2kTqlXn2BZY3OxZ7C2PwbI4c1ZgR1G0e4m8POM3ECovCw81dQBtuw5ACgLghlKc2vkr7FHi7tVQC0acQyhate4ERyd4NEOB0gcqwYSLCTttth0Fm97Hy6r/w2i/VOjUDQjymkBlJvUL1gi6PYKwNOZNvGh4AAGelr/ILkj1p6W3EQtR1MBcCYUV+VAa09MttSdTSR2BfkX28gzRq2FBt1TGTVDIeU0CkYN1R9PkofDiqfUYZNiOckGDke1d79hJzqXRQfeJEycwaNCgqx738vJCTk7OjW4XuahBk57FcTSHp1iIxKX/gDO4eDkT3U++J9+/3P4BaPTmP5bUeMOGjsBWZR8oYULK8n857K7cuuJLxAoXUSC4I2rsbFtvjt2PhrmnT6R8/9td5xrWu2NDlzJz0TNvvXzfp9/9cEVKpQJn/IfI90uOrIA9Kk34TV5e8O8vtzVnkOXbWV4azu222TZ4Jf6KB1S/oZd3A+ceW5vJhMdz38ZPmrlIT7XCRTydF7ar+mClqb9la3RXENzMPd2eKGpYBvPNb+Kpog/RTkhq2JxutRsKHzuMNiWLkWMy52ioK+jObGjQLVVz8IlEmT4E7kIJNh5Lg+HcXgimciSLfoiKacOh5eS6QXdISAhOnz591ePSfO4WLZh9lxrHz9MNZ3vOxWFTNF672BnpV5aOcED7v5+HcCEDmcoAtJjwvK03x2l6u8sHvyAn34vJ+AN5p3fBIXu5Ty2S76e3ewBCRU8CXdvELmHoqE3D9Jz/4PTq9x1iVx3c8B38hXxkK/wQ2NVxy1DdKH3nifKyRdafMJU3sMyRtYkiIrK2y3d17UbDWaiamxOX+WUdtM0GGMvhW2g+T3SL7AK7olCgZcF+9FCcRFmGdWp1p+WZM8cHeuks/tqCzjxk3Usoqrtu9pWOr8ZE8XcECjnwaGCyQH1AJMoE8wWEa5YNKzSPUMyEd8OCbknbW6C8bzny3ZvL88YvHf69qlTY2E4cWk4uHHTPmDEDTz75JHbv3i1fFU5OTsa3336LZ555Bo8++qhlt5JcyojRE/FCwAL8WRKD11YlwJEdiE/A8AxztuXSwS9D4PBhixkyYBB+1w6T72eufBGOZskf8ThuCke+4IGocU/benMcgjQc8uGoy7hb9Qd84z6x+4SLUsUCv5Pfy/cvx9wOKB07I/aNaNdnJLJFT/ggH6f3b4A9uXRiL5qJWSgStWjTx3mC7qB25tGIbY0nkJZrg3nqWYnQiGXyfg2Pbgd7U+ZpHjmDHCtUwkjahnaZGxAppCHYCkG31JMu8UJh3XWzryBWZC8vFBs4p7titFFloJ5XWzI1QwlQmte4nm7JyNegDGqLEe2C5R81x36Wl1ISvpEVjxG5ZND93HPPYeLEiRg6dCgKCgrkoebTp0+Xg/HHH7fvDKVk/xmq59/WGVJZy5WHkvFnvHWuQlubwWhCzop/wl0oxXl9e4QOvNfWm+RUpLqnHqNeQqmoQnTePmQdXgdHkVFQis/3ZmCW4QkcvHUze7kboOvYh5ApeiLAeBnJu3+EPduemIGfS3vgEFoharhrJ8nTarQ47j1Avp97YDnsSfI+8zzz425d4OFuP8m+bpRbZDcYoEIzIRfHjjV97ouiC+Ye9uNiBNqG2d9IHoVflLx0K7hg8ekq4r6vMLfsXYxQ7EOItxWC7uFz8aDPV/jGOKJBw8vF0nx5WSgnUmvgRcC9X2C+chF6CsdrD/SLzL3cZaISedA3POiuMKp9EAYqDiO03FxisDysN5PPklO4oZJh8+bNQ0ZGBvbs2YNdu3YhPT0dr732WlX5MKLG6hjujfv7RmCW8md0+WkAStISHW5nrv31RwwzbIERArxve5+loKygb7cu2OhuHrJbuHaOPEzUEXy29Yw8J65zuDcGdGxp681xKOGB/tjlZx6qXLbtI9izpXsu4EfjECzr+jW0QeZSd65M6Hg7fjIOwtLc9nY1J1970Ty0vLi5eeSM01DrkKo3t7ucUzua/O1zzsTJy3OqFnYZNOkDzVMhA42pcoZ1SyrPNZe5Shd9Eehl+Tnd8ApFsXsYSqCtu4TXlSp6uosFPbSqBoYAZzZjrHETWisu1N7TXTmfG9J8cwG+jSzv1S8mAIPVx+X76aI3Onfr06jXIXKqoFui1+vRo0cP9OrVS+7xfuKJJ9CyJU8i6cbNHtkWgzQn4IlCJP/vEYcJqCqzob64X4+3DXfhVMz98G7Z29ab5JSk4W5h41/EKmNvPJw3DWcy7LMcUXXpecXw3fVvedjhU8NjnSZpU1MKG/GE3JsSVRSP3FO2y8xcl5TcYvx21FyqaXKvimGsLq7joFvxLzyKZTmtcDS5gfWFrUTK5Dw5/++YXPYiwvr9Dc4mrdUUzDPcjY05YU3+3qaUw/Ky0Lct7JEqwBx0SyUbLZ3B3JRnDrqLdM2gVVkna7uXzlw5KK+uEl41NsoEhcH8N1LUuDf8b4+buWyYL/Jr7+n2aY7LIxdivmEy3DXKRmer16gUONLmSUwrexbTyp/HyA6hjXodIocPuqXM5FOmTEGzZs0QGhqKBQsWwGQy4eWXX5YTqEk93l999ZV1tpZcirtOjdLR76JUVKNF7m5c3Pp/cARSD86/folHvkGBfZEPoPU95szlZB1d2sZiecw8HDNGYP5a89Vxe7blpw/xiGI5VunmYEiMp603xyF1bhuLbTpzNuzUdfb5/dq+9n+4T1iDoVFatA1hxQKJNJz1pjbmsj+/HrKPsm/bT2eg0KhAsm9PNI80Dzd2Jn4D7sfnxnFYn+aOsvJrZJy2EqHwsrxUhnaEXfI1/74jhHSL1+pWVnx2eFhpLnLKYUzK+Rz3KtfV3utcm4qAW6ZpxDSKimSfPoI0j7yW99T74ULYGDlju5/HjY1smNgtHJtNXRHWumejh6kTOXzQ/cILL2Dr1q2477774Ofnh7///e8YN26cnLV87dq12Lt3LyZPnmydrSWX0793b6wLmCrf9978AkqzLsDebdx7GNtPpkCjVOCN2zqyJ7MJPD+mDVQKARsS0rAj/uqqCvYi8dJl9D/3iXw/p+tMCGo3W2+SQ5J6aIS+5oSdMRkbUZpphURIN6DEYETs8U/kWvIv+G2x9ebYlfGdguVyRc0O/AemctsnwvvjhDk4Gto60CmP1dEB7vDVq+WAOyGlaUcXzHB/Hz1LFsKvlZ2O9PJpDhMUMEHA+cwCy71uaT5U5eYAV+NjpazbWWcwJPM7jFXurv+c7oqh5VLVD5XWXKO8cUF3wTWTt2UVlslLv0YOLa8kfR9/fXwA3r3LXPaOyCWD7tWrV2Px4sV45513sHLlSrlXLzY2Fps2bcLgwYOts5Xk0vrd+xqOIgaeYgGSv35AHiJlr3KLShG49iGs0ryIl3qJiGnmPEl57FnLQE880DsYr6m+QqefBsGQY595JQ79+AZChCxkqgIReTMzlt+IAQOHYaOiHz4un4jfTtjHUOVKf25eh044JSexih7Fah7VDWnli+808zC9fClO7TXXL7cV0ViOyUemY45qCYZHmYfqOhvpQsKIkBJMUGxD4rGDTZpI9GRqAdLhg3bhAbBLXqH4qN92DC17HxeyzeW9LCI/TV4UiDp4+5iHZFs3e3k9L165B2LzxH3oV/qhPJKwwfTmz+KDgtrf8+I+6E6vQbhw2SK901JuH8/GbCeRswTdUmmwdu3MpR+k4eQ6nU7OWk5kLQHeHsgZ/TGKRQ2i8/bg7Nr/2O3O3rz4ZXQWTyBSkY67BrS39ea4lMdGtEcn1Xl4oBDnlj4Le7P3yHGMzP5Ovl8+9GU5yRHdWK3204M/wvvld+DDXVkwmuwj54N0IVrY+5l8/2zQSKi8WV+2Op3ODcd9zRfo8/YthS2diduETuIJ3Kbchh6xEXBWM0q+xAeahRBOrm2y9zx9uQBlRhM8tSqE+9rpiB4pJ4i/OXi9kG3B4eUF5lwOaaKvdcqFSXTe1ep017OnW6FALvRIgx88Glgu7Mqe7lrfc+8XGHjgKYxT7EKwNTK2E7la0C3N31ar/7rypFQq4e7ubuntIqqhf5++WBf2mDy/e1lcSoNKZDSVP/7YgJsvfy7fT+8/F1r/5rbeJJfirdcitd+rMIkCWqauRu7JbbCnes2XV82Fh1CCi/q2COo7xdab5BTu7h0JL51KPsFfG29OXGRrBxJOYGDpn/L90JFP2Xpz7JK2613yslXmJpSXldpsO7L3mkvOJXgNkC8GOCtVc/Pwbp/MuCbLGq9dOxtfqt/GhICLdj1sP9LfPMzaonO6A9vhPwGvYH753Qj2tkLmconWHHR7oqhB2csLSs3r6jUNLBdWPei+Vk93VfZyLwR7Oe/3iajJgm7pgD1t2jTcdttt8q2kpAQzZ86s+rnyRmRpI+59Efe6fYiPCoZg7oqjdrWDL6ZloPnmJ6ERjDjtPxTNh8+09Sa5pOHDR2Ojbrh8P3/503YzFWHj1i0YVWKuI+55y7/lHgeyTPbeB/pHYbDiEIJWToGpONfmu/XSxkXQCuU4r28Pjxg7nctqY+37jZXLCvkgHyd2mGtkNzXRZETzyxvl+6oO5hJ0ziq4/SB52dF0HGfTLTh3uQ4BKZtxkzIOMX5WCjotJDZlJX7SzMWYvJ/kIfEWoffDb+XdsNHUHcHeblbu6S5GQXE9h8YnH0S3g3PwgHItPBpao1sS1gPLh27EmLL5dZYMyxC9rXexgciBNfjMT0qgFhgYCG9vb/l2zz33yFnMK3+uvBFZmjQH6bnJo6AQgJ/jLuHHHfaRqbrcaEL817PQQriETIU/oqZ9wZrcNqJUCAicOA95ohvCi4/j3LoFsLXcYgPmbS/AgvLbcDJoDLzbMPeFJd3frznmqr9Bz/IDOLXqfdjSxYwc9M5aLt9X93vEpttiz9RqDU763yTfLzlo7m1uamfiNqOZmIUC0Q3tB06AM9NG9kAZNGgm5CIh/oD13zAvGV6GdDlhl39sT9gzT2MueihOoq1wFik5lpvXnZpnfi3rDS//qyKCsSS/fv8n8zTapizHcMV+uDdmeLlaB5VPGEqhqT2RWmGG+W1EL+tdbCByYA2+1CUlUSOyle7N/fD0yNZYs34d+q6bhaTCZxA1wrYnt6uXLcGE4tXyfeMtC6HytNOkMS6iS9vWWB4yA7em/geBu99ASbfx0AXF2Gx73lx7DOfyBfwaMBWPTh9os+1wVt7uOuyInYHoU3MQnPAFTCVPQ6GzTSm2r/84gs6mNuinOY2QPs5X89mSPHtMAtb9jNY5W1BWXAiNW9NOU8vc+wOko0KCV3/00jv5FDm1DmleHRGRtx9FJzYDw6x74a/s3B5IabROiJHo0sK+58orfJv/Vas7u6hquPmNKDv2GwaV/Il9iLVe0K3SwqTUQmEshVjfET5l5lEOhXCTy/c1hqfO/P+uGl4uTVuoHF4uBd3W+txEDoxjHMnhPDokBg+HJiJcyEDo9n8h67jt5u5uTEjDa3Fa7DPF4lTMNAR2GW2zbaG/DLv3BewXOqBUVOGnjVtttmt2J5zBj3vOyvffvK0jdOpG9C7QdfWd8DDOicHwFvNxarVtEi1eyCrCkoP5eMIwC2f/9gegYm3ZurTtNRypCJC/owcO7EGTDy1PMw8tV7R37qHllcSoAfLSJ32v1ed1Z53YIS+PK2MR4WfnPZ5WqNVt3LYACzQfoZ/mJLzcGhfc1sflv63FgNIPkFhaz9GlJeYqD/lS0N2YOd0A2hx9H++oF0FTYs7QXqU0DzCW/TWnm4nUiK7CoJscjpSUZfhDb2Kbqi80KIf4w70ozUlu8u1ISM7DrKVx8vyllZ0/QavJbzX5NtC1k6qVjV+IEaVv4aX4QOw5m9Xku6qo1ICSZTPxq+ZfeLpzGXq38G/ybXAVPh56HG35kHw/KP5ziKVNM2+1uo//OA2DUcSAlgHoERvZ5O/vaKQkrMs6LETv0o/x9Zm/hso2hfhzaVhr6IazYgjaDXSNoDuoo3k4fydjPC5kWjBpWC3Ei3vlZZ5/Z7tOoiar6OmWht6npGda5jXzzUkdy/XBVv38uvCOuCg2Q4HBXKLtukrMPeK5onuje7r9E5fjDuVWuBWbe7WvHFoulUkT1Ho5wSUR1cSgmxySu06DyAeX4DTC4W/KROqi22AsbrpavZezcrH4q4UoKjOif0t/vDSxmzzci+xH326dcVPPTvKot2d/OoSisvpneLWENd8vwmDjbrRSXMID/aOb9L1dUZ8Jj+CCGAgfMRcnf32vSd/7/OVstD74BiKFNPx9RKsmfW9HNnxAP5RDhY3H0nC5Yg5sU1h1PBdzy6fhndhvoXe3zVSEpqaN6o13vF/AuNI3sCvJihchjeXwy02Q77q16AO75+aLUpW5DRRdPmORl1QVXZaXglcQrKl6MrR6ZTAvyZEXedA3rmRYtQzmGkNuzRET7gE4OXAB5pbfhxBvnf1fbCGyAQbd5LAiQ4KQNe5rZIseaF5yDOc/HAtTifV7uErKynH002l4u3w+XvZahYV3d5drBpP9eXFsW4R669Aq+0+c+ehWoNw8/M3aDp04hSGJ5pEPFzo8AvfILk3yvq7Mz8sdR1uZqwaExy9ESa75xLcp7Fv+Ae5XrsUy/ZvoHsFEovXVOtgT3SJ9YDIZ8dv2phliLgUKqw+beyLHdgqFy1DrYGw7ARnwxu4z1gu6xaJMJCAaaaIPott0hSMo9TDPOzdlJd34i5UVQl1uPg/ReIfBmlTHluMl7VJ0E07Wr1Z3RU93nujeuJJhUtDg7icvpak8UqdDFZ03EvyG4yfjYARxPjdRrRgpkEPr1aMnjgz7GnmiHtFFh7Hzy9lWna9WWFKGrR/cj6Glm1AOBcaMHg9v/V9168m+eOrUeG9cBN5Tf4IOeVtx6uuZ5oQvVnQ5Ox+GpdMQIOQhVROF6IkvW/X96C+D7nwCm4Q+eLpsBj7fa+7VsbZzqZnom7xEvl/S8zFAwXn7DTGzTTG2ap/CTXumw2SsdhJvJSeOHUFYzgG4qwUMbR0IV9I72hww7Umy0DDqWlwq98StxS9hgOFjdIow94raO8E/BhfFAOTkFdz4+UN+qrwoFLXw8TPvb6s5tgoPCivRWZFYv57u4uo93Y0MuvUVQbdwda3ulFzzaBWpp5uIrsagmxzeoMEjcGDQl/jd2BUPXxiJV1clWCXwzskvxL7378TIwpUwiQLO95uP4G5jLP4+ZFl9OsZia6c35d9Zq4vLcHat9RJtlZYbseezR9FDjEcR3OA59b+cdtCE9FoNCiYuxm+mXvh4SyKSc4qt/p77l3+AECELWcoARAw397RT/Q3s3RteQhHCxDQc3b7K6rsuZ+sifK99DV/4LoGbxrUukPQIVuEJ1S/4R/6/kZxtnXndB86bA7u2oT4OkzhSM+lrDCxbgJ9LeyCjoMwiQfdl0Qch1u7xrajV7Yli5NVWwutKd36NSfovsNbYq9FzuoWK4eU+kILuau+ZfBC+59ehuZDKJGpE18Cgm5zCkJvGIHPCN3IpjMXbk/DoN/tQkH7eYq9/OTMbxz+YgMGlm2GAEueHfoAWI3mC7SjG3nYvVgbOkO9H7HkVlw+tt/h7SBd6fv76fYwrXin/nD/mY7hHdLT4+1DdxncKQa8oP5QYTHj/172AqR4Jhhop7thJDE39Ur5f1OtJXmBpBDd3TxwLMFd9MOwx70trKSspQptU8/dT0+EWuBoPvRtmqX7GOOUuxMfHWeU94s9ckpfdIh2jl1uiVasQ7mvOsn42o9AiSdQuw9f6wWdFrW4vobD2utlX0nrgjMEXBdA3rk63pKKn21coqBnoH/wfJp15HncqtzDoJroGBt3kNO7qEYG37+gEtVJAqxOLYPy4Hy7sMZ9g3YjdiRk49/EE9CnfixJocHnMV4gacp9FtpmahpTUZdT0efhDMxQqmKD75QEUJB+36Ht8s+sc3jsdIpePS+o4C0G9brfo61P9f9dzbmknn/w9f3oyEn//0mqjGjKXPS2ffCbrWiJ8+CP8FTVSwGDzBbGO+duQmXbBavsxfsP/wQf5cqmyTkPvgstRuyHFs4N8N+/4Fsu/fkke/nFoJDZonkXPMMdKLBod4CEvz2bcYF6YqIF4VvU8FpTfav25zRU93V4oQl5x/RKFFpaa13Nv5JzuykRq0vDyvOrDy1mjm+i6GHSTU7mzRwR+fLgnRmoOwxv5CFt9L47+39MoLzInEGkI6Y/TnBXx+Nvnu7GqtIt8dTjv9qUI6+UaJWacjZtWhTYzvsJRtISXmI+yz0chI+WcRV576Z7zmLvyKNLhg4PDvkHUra9Y5HWpcdqHeuOmSAF+QgF8dsxDSZ65nI0lrVy+FMPLt8IIBbzu/ARQMrdDY8V07IMTqtZQC0acWvcZrEV/2Dz3PjHydqjVrvn7Mkb0k5deabst/tql5/ZBKV3URBk6t3CgJHUFl/Fa5jPYpJmNM+k3FnQb3QPxc1EnbDd1bIKe7oqgWyiq1/By8den8KTp/+CBokYPL0e3e/FI0H/xgmF6zTndVUG3N3u6ia6BQTc5nS7NmyH0qU3Y6D4OCkFE+zNfoPCt9jjy/VyU16OsWHFJGXb8thRPvfcFluw0B2WGbg9AfHQXAitqnZJjCvH3g+qeH3ACUfjV0BO3/V8ikm5gOKE0pHzlj4txdsUbMInA5F4ReHBwa0DBQ6ut9Zr0LyQhFP5iNs5+PtWiCfROpuVj7kEPvG+4HWdjH4RHTC+Lvbarym03RV5GnP3BKgnVzh7djTaGBJSLCrQa9RhcVbOOw+Vle8MRXM61bM6Dy8e2ycvjqliE+ZiHazsEnQ8iiuLRQpGK7NQbuxCbWVAKo0mEQgCaeVi5t19bOadbCrqv09NtKIGwfzEeVq2Wf2xsIjWpp7vcPQSl0NQY0i5WBN0Z8GLQTXQNDndmmJSUhAcffBDR0dFwc3NDTEwM5syZg7Kymskvzp8/j/Hjx8Pd3R0BAQGYNWvWVeuQ8/Lz9sTQp/+LjR3ewnkEy73eHY+9j/x/d8CnP/4ql4yRkiyZjCZkZ2Xg/Ol4HNr+GzZ/NBP582PRb9cM/Kv4XcR4C/jvg73xxu1d4BnY3NYfiyygdcsYuM1Yj8WeM3E+uxh3LNqBwxcaXkJHOrFa/uWbGBM/G8+rv8O7XdLwxq0dWZ/Ujo4BWTd/glJRjbb5OxD/0zyLvK7JJOKfyw6j0KjC0dhHETP5bYu8rqtrP2Ia8kU3hImp2LN9o8VfP23TJ/LysOcABIa57rHcI6YvDFAhTMjE/kMHLfraxgt75WWOX2fHOg6qNCjxiDTfzzh1Qy9VfPAn3KLYjtYeRVBZu5RoVU934fVLhlXU6DaKAooFHXTqxm+bp84csFfv6TYVmIPuHMEbAe6ONbWAqKk08lKX7Rw/fhwmkwmffvopWrZsifj4eDz00EMoLCzEO++8I69jNBoxduxYNGvWDNu2bUNmZibuu+8+uVfqww8/tPVHoCaiVAgYfscMFI69D5tWLELL4wsRiTQs2l+A7P0H5HUWqd/HaOVeSLOUKv7kAgKQCw9khQ3Biru6wcM3gL8zJ6zx/uOjPrjvqz04lZKF7M8n4qifPyLuegteoa2u+/8PxR9G1qq5uK3kd7m9JIbegtvvnCpNKG6S7af66dZ7CDaemI3hZ/6N1vHv4WLrAQhq2/+Gdt+Pv/6KE+fL4KH1xGsT2ztWcGHH3D298VvLp/FpghqlcXqsHihabN/mFZdBm3FU/q5q+zwEl6bR47Jne4TlH0JG/EZgUF/LvK4owi/nsPktmjveyA8hoBWQfxb6/LPyBVXp/KEx/Pe+iwWaM/iX2xuwusje+KH7d3h/ewb6Xm94eUWN7nzooddoGv/dKszE5KxF6KLKwLHMOebHTEYois0XrhUegVA0ct8ROTuHC7pHjx4t3yq1aNECJ06cwCeffFIVdK9fvx4JCQm4cOECQkPN84reffddTJs2DfPmzYOXlznjI7kGdzcdhk16CgXFM7Ftyy8YV9IBcReycSwlH7miu7xOkahFvsIT6R5toe5+N1r1vx1d1bxa68yaeWrx/Yw++N/iBRiQehjKHBGln/XFoaipCB/xBPxDo2sE0dJFu5OJZ3B+5esYlLsSWsF8lT+x9cOImfQWA247NXTKP7Hj7V3oV7IFmuUPojjUPAS2MdZt2oibDzyMtppgnBqxBCHeDjSE1gH0vu1JPHP6DxSk5GF9QhpGtQ+2yOv+fOAS5pbOwQS/i/hPv7FwdcqWQ1Fw4ARSU1PlhIBa1Y2X9hLT4uFlzEGxqEFEuz5wNNrg1sDZ9WguXpJHwUX46Rv1Oppic4+vwisEVqfzhjGoA1Jw5Po93RU1uqVzHveKnupGMZWjZ+p36KYUMP78E+bHirIgQJTLcuq82ElB5DRBd21yc3Ph52cuYyDZuXMnOnToUBVwS0aNGoXS0lLs378fQ4cOtdGWki15uOkwYPQkDKj4ubjMiIK8nihz10Pv5g7pT2wQf0UuxVOnxoxHnkbc3t4Q1j2PLuWH0TnpK+Dzr5ABH1zSt8XGkIexLT8Ip9MK8LrpP5ig3GHu3fboDp9b5iEm1kI9RWQVSqUCraZ/iXMfDcIGQ2ds+fkUxjXivHDb3v3osuVBeAnF8PHyxu29W1tjc12ar7sG0/pF4aM/TmPJ+j0Y2XYchBvMjyBdLJMqC0hf2u4DR0NQOEbtaGsKHDkbA470QXKBCX3OZmFgq2Y3/JrZh9ZAOgvbJbZH38hAOBpFs1h5GSMk40xGYeOC7rIiaMrz5btavzA0BT93jbzMLCyr1/DyvBspFyZx85EXSkFEcloqisrKodfo8UfHf2P9gZMI8jFngSciJwy6ExMT5SHjUk92JenqbVBQzfDJ19cXGo1Gfq42UkAu3Srl5ZkTbhkMBvnmKio/qyt8ZpUA+Hj7uMzntUf20t46dOkNY8eN+OO3bxFyeCFaGhMRIOQgoGgnZifchkTRPOohURWGc5pYKIa/hMguo+XebVtvO12f9D0/fddqvLf0JIrO5ONsmhJ9+hcg1K9+J4iHTiQibNUUBAk5SNFGI/ihn2CAUmq43P0Wdm+fcHjumI/7clZh//oP0PmmGyvttW/vdmSkp8Fd441xHYKa/PtqL8e4GlR69G8dgh/3X8L6o6noE2X+O3gjdpREosQ4CKl+vTAAJhgMJjgSwSdaPiFuoUjB+rQ89ItuxD7JPAspJ76Um8Dby8f6v3OTEW1OfYZ/qk5iWf49Nc5Xr3xvoTBT/nxST7deo7yBbVNApXaHYCiEFwoRdy4TvaL8sF07EN8ZIzDNQ21fbZ1c8xjXxOr72e0m6J47dy5eeaXuMjt79+5Fjx49qn5OTk6Wh5rfeeedmD59eo11a5uvIl3xvtY8lvnz59f6/tJQdb2+ccOMHNmGDRtsvQnkQuymvQn+yOv8EuINpTBknYc6Pwmx/kEYoDciWC8iUDsWB5XjgRQRSFlr662lBnqkNfDpcSXSCw04vnAS9kaPg9q3KpvDVYyilJTzNMZkfIVoRQouC/6Ii3kShj92cN9bUZRbGXQlBnjsehuri90hNHKOqNFYjq6H/4Ut2lx87DUbf27668K6yx7jKngXSPtUie1xR7AaZ244HcVHRz1wyjATE3VGrFmzBo5G6qHuI/giyRiELfvi4Z8V3+DXCMqNgzSwPkkMQmrSCawpPA6rEkWMP/gfzFQZ8V3eyBr7/cr2Fp2+A53knm53lOTn3NDvaAR00KMQvsjH9xt2IyNMxIGT0ogUBbKTz2LNmjM39LHIMdnbMa4pFRUVOVbQ/fjjj2PSpEl1rhMVFVUj4JaGifft2xeffVazrmdwcDB2765ZgzI7O1u+EnFlD3il559/HrNnz67R0x0REYGRI0e61BxwaR9JX5wRI0a4bB1TajqO0N4m2HoDyKJGpOVhzxdPYBR2oeDsIRwomICgoTPRIrZDjfX2n8vGn8sW4tni9+U6H7kKb+in/YIRIW35G7GynF5dUbRoI9oIZ1Hglo/ON9V9bnAtu79/E9FIRrbgiUemToGnDZJi2usxbljaadyddCs8UYjL3Q4gNqTxvd3SfOKnd2+WokA8fttgNG/kfGhb+zGsP174JQEDPf0xZkz3Bv9/xe4k4AxwTgzGyIG90LeFP6zuhDdQnAWNqRgjRv1N7v2utb0ZhuLnXXfjrfWn0T4mCGPGdG30W6qS3wbSMuEjFKLUIwRjemhx8uw65AreGNJrNMZ0aoL57GQ37PUY15QqR0c7TNAtlfWSbvVx6dIlOeDu3r07Fi9eDMUVc76kQFxKmJaSkoKQkJCqHmutViv/n9pIz0m3K0kNyBUbkat+brINtjdqKjFBXjjSdhziT59HB8MRDMpYCvy4FHGabrisb4kMgw6LxFtxIasYPmiDB3WeyA4fjui/vQ2F543PfaXraxYSid1hk9A7eQn8dv8bxQMmwsvDs0G7Lj0tGR1PfWLOv9Dh7+gRaNtAwN6OceqQllAq8qAXC7H1wFa0n3h7o1/r9O8/oLWYDUOz9mgZZC5j5Ygqtz0pq6hxv6ucJHlxVgzGGD+PJvl9i1LZsOIseah3oQHw0alrb29qNbLUwUhFNvrobrAt6qV6L4AP8rHnUh7Ux3fg2Zx3EKgcgXD/O+yqnZPrHuOaUn0/t8PV6ZZ6uIcMGSL3QkvZytPT0+V52tXnaku90+3atcPUqVMRFxeH33//Hc8884xcWsyVeq2JiOhqGp07Yp/egGNDPsVRfS85627XsgMYlfMD+uRvkANuyeie7SA8vh8x079mwN3E2t35InLghRbiBcR9/pg8Pawhji99Ad5CIc4qo9Ft4pNW206HpVQjLXiQfFdx8gaGg4si2uz9F1ZrX8QD4RfhyKIDzNVMkrML5azuDVXQ8wk8UPYMVhr7IdhLh6YgVNXqLkLWdZKpFZSaK27otTfY3+ZmTlzsKxQiJbcERdnm8+9M0bvJPjeRI7Kbnu76knqsT58+Ld/Cw8NrPFf5R1mpVGL16tV49NFH0b9/f7i5ueHuu++uKilGRESuTVCq0HbIJGDIJGScP46Lm7+C1lgI0bc5lnXtiwhfPQJ5Amkznr5BOD1yAXzWT8Pg3BXYtvJLDJhQM3fLtRw9uAt9s1bIvdzGkW9AoXK4U50m4dN1gpybolPBdmQUlCLAo+FlMg2XDsHbmCWX3YztORyOLCDpV+zWPoudpjY4nzkErYIaNroiVWiGTaZu8NSp4H6jgW196cwdSZ4wB90t/K8R9O79An1OH0AHoS08tC1u7D1Hvynf9n+ZAKSVICv9klz9JQPeCPRiqVWia3G4v0RSrW3pdj2RkZFYtWpVk2wTERE5roDINgi49y1bbwZdoWW/W3Hg+Ba0Ofc/LN+fhKA++dcNhDKys6Fe+ShUgglHvAaiY+8x3K/X4NtpDAxrVIhRpOC3/XswevDABu+r5H0r0RzAHqEjBkZZpq66rQhqNwQJWVVlwxoadCfnlMjLJu3trdbTnV1UR093wgr0Sd2KFsJjcNfc4Km/VIPcZMKtfqswM+sHBKftlx8u0/pZpOY7kbNyuOHlRERE5Bq63Ps2XgpZhGVlffHEd3HILbp2aZZLOcW464uD+L2sHbLhhbC73mvSbXU4Om9c8jHnuSk6vLJRLyGcMmcszgwZDGUjs8zbDf9W8qKFkIKz6QUN+7+5l+C+5z8YodhXNUy9SWgrgm4U1j28vLiyTrc7vNws0N92bAUeOPsMxir3QAUjdpva4IJXw5PPEbkSBt1ERERklxQqNf4xZQwCPDQ4npqP1999Czu3XF2u7/TlfNzxyQ6cySzCf93vR960P+AXHmuTbXYkmnbj5GXzjC3yEPOGEIuyEVZoLq0V0HUsHJ5fNIyCEu5CKbJSzEnR6i3lELqf/gizVD+jVZAHmszA2VjQ8kv8z3gTsusKukty5UWeqId/I6YRXCX2ZpR5Ncfi8lEYVfom/lb2Mjx8mr46AJEjcbjh5UREROQ6Aj11WDytF+Z8txkvFnwMnz8K8ef+sVD0fBAlaaegSk9AftoZZJU8iJhmvvjv9N4I8Xaz9WY7hJDet2H9/m34X34ndNt9HrNuMvf21kdK3BqEwoSTYjh6dukMh6dUo9g9Ah4FSSi/fBJAA+aoZyXKC6lcWKvAhg1LvyH+MSgOMCAHicisM+g293Tnwh3+7pobf1+1Dson4/DOKxtQWJF0LtibSdSI6sKgm4iIiOxax3Bv/O/hfjj936HwSV+FgXmrgd9X11hH6+2G7jO/hZ8lggoXIXiHo/jm97F56UEc3XUOMwa3qPe83IyELQgFkOjdF7E3Ok/YTpj8WgEFSdDmmoPoess6U1UubGhgE/Z0S3F3RXu/5pxukwkoMdcRzhPd4e9hme+HlLS4U7gPdp7JlH9m5nKiunF4OREREdk9nXczdHjsWySN/wlnNa1QJLjhrK4tDjW7BXHt/oH+Mxcw4G6EmzuEIMhLi/T8Uqw+nFKv/2MyiXgu/248VDYbhi5T4Sx0Ia3lZWDZBeSVXDt/wJXKLp+Sl0liMGKaNWHQnXEKfS4twV3KP649p7tUCrjFqp5uS16U6hLpU3WfPd1EdXOOS5NERETkEqK6jwCkm1Rb2dYb4wQ0KgUe66qFafunyN8QDLHrAghC3UnRVh1JwfHLhbik7YO3+/SFs9BEdEPcnra4IAYiKaNQ7smtDzHT3DNe7BEJN00TZvDOOIkOx/+DycqWeKloQp3zuYtFDcqghp/egkF3BINuovpiTzcRERGRC7s16DKmqdZjQtEy7D91sc51DQWZ+HjdYfn+Q4NawMeCQZzNdbgd84Pex1fGm5FY3wzmhhJoCs0jBNSB9Z8TbxE6n6rs5dmF1+iZ9wrD2Xt245ay1+GjV0OltNypf9dqQXcI53QT1YlBNxEREZEL8+wyERmacPgIhTizbmGd6yb98Dz+Vzgdk9z24YEBzjfWoG2IORHakYvmedDXlZ0EASLyRDcEB4ejSXkEyotmQs61h5crVUgVmuGUGG7x6ReBXjqMbh8s93g392/CUmlEDohBNxEREZErUyhh7Pu4fHdAxve4kG4eknylkqxLiDz/M/yFfAzs1g4eWuebpSjNU9aiDEfPp9XvP/jH4NnATzHDMBstg5owc7nEI0heeAnFEA1FKC4zZxK/UmVAHuBugXJhV1g0tTt+eaw/1BbsQSdyRvyGEBEREbm4oAH3I0fhi1AhE798/TZyi64ernzyl39DCwMOC61x06hb4YxGxD+HY9r7EZq6CQaj6fr/QanG5uwA7DS1R6umDrq1nhDVevluoJBTewbzczsRdWA+xil2MtEgkQ0x6CYiIiJydWodynvNlO8+UvAx/vfxSzUC7+z4jYg5/718P6f7LOicpEzYldw9faEQRDQXL+J4Sv51188pKpMzv0taNnG5MAgChIre7kBkI7uWCyW4tA/tk5bgJuUB+FmoXBgRNRyDbiIiIiJCwPDZyI29AyrBhNi8XZj6xU7sTziJfe/fBd+fboc7SnBM0Qr9Rk922r0lBHeQl52EMzh4Mee66+dveh+PKFeim1eebYbbVwbd0rzu2nq6K7KX54ruCGANeyKbYdBNRERERIBKA+/JXyB10JuYo56Nw8n5ePWbteiWsx4mUcAa3Vhg6s9QqZqwLFZTi+wjL3ooTuLQuczrru579Gv8Q70UXX2LYRNj3sY/Axdis6lL7RnMi80XDvKg5/ByIhtyzrFBRERERNRwgoDgYY/gy3b5mPLFLhwtaoUVgTPRrvdIjOkxzPn3aFBHlKvc4VVeiNxzhwB0u/a6hhK4F5vLhemDY2ETIZ2Q71OOovMpck+3OZ957T3dUR6WT6RGRPXDoJuIiIiIamgd7IlNzwyB0SjC132M6+wdpQqm8J5A0maE5sYhr+QeeOnU1y0XFhYaAVvxrxg2LvV0Xx10V/Z0c3g5kS1xeDkRERERXUUKNn1dcB6wJnqAvOypOI7DF2ovnybLOiMvzolBaBXcxJnLK2UmYmTWt7hHuaH27OUVPd15ojsTqRHZEINuIiIiIqJKMcOwx2skfjP2wqE6kqmVpJ2Ul0liMFo2s1HQnX0WA84txBTlxlqzl4vV5nT7W6FONxHVD4NuIiIiIqJK4d1xuOe/scrUF3Hnrx10F6SckpeX1eHw1l9jCLq1eQTXWac75/YfcHPpfBw2tYCvrbaRiBh0ExERERFV1zXSR14evJADURRr3TnGjNPyssQnxnY7z9McdPsL+cgrKLzq6XSFP46JzaHWe0GlZF8bka3w20dEREREVE37EE90UJ5HbNF+JOeW1LpvFke/i7tKX0J+uA2zurv5QRTMeZEVRRlXPZ1ZUFYj2RoR2QaDbiIiIiKianTntmCV+p+Yp/oSB68xxPxkejH2iG0RHhpiu32nUMDkbs5ZrilJR41O+eIcBO56HY8oV3I+N5GNMegmIiIiIqouoidMEBCtSMPpM+Zh5NUZyo04fNGcGbxVoIdN951QMcQ8QMxGsbHaEwVpiDn1FWaofoUfe7qJbIpBNxERERFRdTpv5Hq1lu+azu6ouW+yz6H8nTZ4rOQzBLhr0DnCPP/bVhRefyVTKzTUVi5MD38PDi8nsiUG3UREREREVxCa95WXAdn7kZxTXPW4mLACbiWX0Vq4gAcGRkOnVtp23900B/dp38cvxv4oLK/2eEW5sFy4c043kY0x6CYiIiIiuoJ368HysjuO49VfE6oezzuwTF7+LvTBlN7Nbb/fAtsgxzMWhXBDgUGopafbHf4erNFNZEsMuomIiIiIriA07ycv2wjncTwhDr8fSwNyL8I78yBMogCvrrfC280+al/7VszZLqje011i7unOg55zuolsjEE3EREREdGVPIOAsB5QCCJ6KE7i5RVHcfbP7+Sn9ouxuGtYT/vYZ7kXcWfR93hY+esVc7orhpdLPd1MpEZkUwy6iYiIiIhqM+lblI1+Fzs9R+NSTjGy9v4kP3wpZCRCvN3sY58VpGFs+he4X7UOheXCVXO686Q53RxeTmRTDLqJiIiIiGrjGQxNn+l45Zb2CEAuAsQs+eFOI6faz/7yMGcvb4YcFJaZqh429v87xpTNx5LykRxeTmRjKltvABERERGRPRveLgi92kYhI9EbJdpgtI4xlxOzCx6BECFAJZigKs+vejgbnkgwmRO9+ertY+45katiTzcRERER0XXMv6MHLnV9Gn7TvrWvfaVUo0zrK9/VGcxDyiVZhWVVAbdKyVN+IltiTzcRERER0XV4u2twy62T7XI/leuDoC3Ngrvxr6BbvXcRHlOexAG3ETbdNiJiTzcRERERkeNnWpcW5X8F3YHHluBZ9Q9oof1ryDkR2QbHmhAREREROTClV4i89BZzUW40J1NTlebKS42Heeg5EdmOQwfdpaWl6NKlCwRBwMGDB2s8d/78eYwfPx7u7u4ICAjArFmzUFZmnttCREREROQs1EOexeiyN7HEOBI5xQbAZIK6vEB+Tsugm8jmHDrofu655xAaGnrV40ajEWPHjkVhYSG2bduGpUuXYtmyZXj66adtsp1ERERERNaiDIhBmi4GBdAju9AAJG6CAibkiW7QeZuHnhOR7Ths0L127VqsX78e77zzzlXPSY8nJCTgv//9L7p27Yrhw4fj3Xffxeeff468vDybbC8RERERkbX46jXy8uTlAmD3J/L9H41D4Oup504nsjGHzF6elpaGhx56CL/88gv0+qsPJDt37kSHDh1q9IKPGjVKHo6+f/9+DB069Kr/Iz0n3SpVBucGg0G+uYrKz+pKn5lsh+2N2ObImfEYR02mMAMvea3G4ZwM/LwhGxOLN8IEQR5uPlun5HkdWQWPcaj3d8vhgm5RFDFt2jTMnDkTPXr0QFJS0lXrpKamIiio5lAaX19faDQa+bnazJ8/H6+88kqtvea1BfbObsOGDbbeBHIhbG/ENkfOjMc4sjZ9aTpGJH+Gvko1ZuS2Qp7OGwdNMTgvBuF0fBzWXBD5SyCrceVjXFFRkWMF3XPnzq016K1u79692LFjh9wL/fzzz9e5rpRcrbaAvbbHJdLrzZ49u+pn6T0iIiIwcuRIeHl5wZWu1khfnBEjRkCtVtt6c8jJsb0R2xw5Mx7jqOkaWzGQ8DR0ggFxppa4SfwEetGcSO3mYQMQG+TJXwZZvtkxbkB9py7bTdD9+OOPY9KkSXWuExUVhddffx27du2CVqut8ZzU6z1lyhQsWbIEwcHB2L17d43ns7Oz5YZxZQ94Jen1rnxNiRR4umLw6aqfm2yD7Y3Y5siZ8RhHTdDIIGq9IJTmoZtfKTZnuQPwkJ8K9HbnOR1Zufm5btygrufntpugWyrrJd2uZ8GCBXLgXSk5OVmer/3999+jd+/e8mN9+/bFvHnzkJKSgpCQkKph4lJQ3b17dyt+CiIiIiIiG3BvBpTm4bFuOmzeaH5IGuDpq3fNYIjInthN0F1fkZGRNX728DBfxYuJiUF4eLh8XxoS3q5dO0ydOhVvv/02srKy8Mwzz8jJ11xpqDgRERERuQYhK1Fe9tw2HX2jf8POs1nwcVNDpXTYYkVETsMpv4VKpRKrV6+GTqdD//79cdddd2HixIm1lhcjIiIiInJ0prYTzMvW4/Cv8e3gplaiS4SPrTeLiByxp7u2ed5SgrTaesRXrVplk20iIiIiImpKxhGv40iBL9pNnIf2bh7Y9o+h8NRxaDmRPXDKnm4iIiIiIpfiGYKkgGGAypwY2N9DC42Kp/pE9oDfRCIiIiIiIiIrYdBNREREREREZCUMuomIiIiIiIishEE3ERERERERkZUw6CYiIiIiIiKyEgbdRERERERERFbi8HW6raWy9ndeXh5cicFgQFFRkfy51WrWdiS2N3IuPMYR2xs5Kx7fiG2u6VXGipWx47Uw6L6G/Px8eRkREWHp3w0RERERERE5Uezo7e19zecF8XphuYsymUxITk6Gp6cnBEGAK12tkS40XLhwAV5eXrbeHHJybG/ENkfOjMc4YnsjZ8ZjHOQebingDg0NhUJx7Znb7Om+BmmnhYeHw1VJATeDbmJ7I2fFYxyxvZGz4vGN2OaaVl093JWYSI2IiIiIiIjIShh0ExEREREREVkJg26qQavVYs6cOfKSyNrY3qipsc0R2xs5Kx7fiG3OfjGRGhEREREREZGVsKebiIiIiIiIyEoYdBMRERERERFZCYNuIiIiIiIiIith0O3ESktL0aVLFwiCgIMHD9Z47vz58xg/fjzc3d0REBCAWbNmoaysrMY6R44cweDBg+Hm5oawsDC8+uqrcgH46rZs2YLu3btDp9OhRYsWWLRo0VXbsWzZMrRr105O8CEtly9fbqVPTLZyyy23IDIyUm4HISEhmDp1KpKTk2uswzZHlpCUlIQHH3wQ0dHR8rEpJiZGTv545fGL7Y0sZd68eejXrx/0ej18fHxqXYftjezBwoUL5WOj9LdYOjf7888/bb1JZGe2bt0qn/+HhobK8cEvv/xS43npPH/u3Lny89Lf2CFDhuDo0aNXxRdPPPGEHD9IcYR0Dnjx4sUa62RnZ8vnglL9auk2depU5OTkNPi46VREclqzZs0Sb775ZilKFuPi4qoeLy8vFzt06CAOHTpUPHDggLhhwwYxNDRUfPzxx6vWyc3NFYOCgsRJkyaJR44cEZctWyZ6enqK77zzTtU6Z86cEfV6vfjkk0+KCQkJ4ueffy6q1Wrxp59+qlpnx44dolKpFN944w3x2LFj8lKlUom7du1qwj1B1vbee++JO3fuFJOSksTt27eLffv2lW+V2ObIUtauXStOmzZNXLdunZiYmCiuWLFCDAwMFJ9++mm2N7KKl19+WT7GzZ49W/T29r7qeR7fyB4sXbpUPgeTzsWkczLp3Mzd3V08d+6crTeN7MiaNWvEF198UT6vl+KD5cuX13j+zTfflM/3peel8/+//e1vYkhIiJiXl1e1zsyZM8WwsDA5fpDiCCme6Ny5s3wsrDR69Gg51pDiAOnWoUMHcdy4cQ06bjobBt1O/KVq06aNePTo0auCbuk5hUIhXrp0qeqx7777TtRqtXKwLVm4cKF8clFSUlK1zvz58+UvhMlkkn9+7rnn5PeobsaMGWKfPn2qfr7rrrvkL151o0aNkoN5cl5SICQIglhWVib/zDZH1vTWW2+J0dHRVT+zvZE1LF68uNagm+2N7EGvXr3kYKg66Rztn//8p822iezblUG3dH4fHBwsB96VpDhAOu4tWrRI/jknJ0e+uCNd5KkkxRNSXPHbb7/JP0sXfaTXrt7BtnPnTvmx48eP1/u46Ww4vNwJpaWl4aGHHsI333wjD4e70s6dO9GhQwd56EilUaNGycNF9u/fX7WONLS8er1uaR1pyLA0vLNynZEjR9Z4bWmdffv2wWAw1LnOjh07LPypyV5kZWXh22+/lYdjqtVq+TG2ObKm3Nxc+Pn5Vf3M9kZNie2NbE0akiudv115viX9zPMtqq+zZ88iNTW1RjuS4gApHqhsR1I7k87xq68jxRNSXFG5jnRMlIaU9+7du2qdPn36yI9VX+d6sYizYdDtZKQLV9OmTcPMmTPRo0ePWteRvlBBQUE1HvP19YVGo5Gfu9Y6lT9fb53y8nJkZGTUuU7la5Dz+Mc//iHPy/H395fn6axYsaLqObY5spbExER8+OGH8jGP7Y1sgcc3sjXpnMtoNPJ8i25I5bl5Xeft0lKKF6S4oa51AgMDr3r9wMDAOmOIK2MRZ8Og20FISQ2khAd13aQeZunkMy8vD88//3ydryetX1vAXv3xK9epTKJmiXVqe39yzDZX6dlnn0VcXBzWr18PpVKJe++9t0biPbY5smR7k0gjb0aPHo0777wT06dPr/Ec2xtZur3Vhe2N7AHPt8hW7eh6MURj13EmKltvANXP448/jkmTJtW5TlRUFF5//XXs2rWrxrBwidTrPWXKFCxZsgTBwcHYvXv3VVkGpeEilVedpHWuvNJ0+fJleXm9dVQqldzbWdc6V17dIsdtc5WkzJPSLTY2Fm3btkVERITcFvv27cs2RxZvb1LAPXToULl9ffbZZzXW4zGOLN3e6sL2RrYm/e2VLnbzfItuhHQsk0jtSKpEU9t5u7SONJ1Bihuq93ZL60jTCivXkaa6Xik9Pb3G61wvFnE27Ol2oANqmzZt6rxJJSIWLFiAQ4cOySXCpNuaNWvk///999/LZU8k0klqfHw8UlJSql5f6p2UAnWpxETlOlJZgeqp+6V1pLkXlSci0jobNmyosZ3SOlKAXzmX91rrVH4xyfHbXG0qe7iluTkStjmyZHu7dOmSXMakW7duWLx4MRSKmn/K2N7Imse3K7G9ka1JQ3Kl87crz7ekn3m+RfUllZuTguHq7UiKA6TywJXtSGpn0jl+9XWkeEKKKyrXkY6JUq6VPXv2VK2ze/du+bHq61wvFnE6ts7kRtZ19uzZa5YMu+mmm+Q0/Rs3bhTDw8NrpOmXshNKJcMmT54slwz4+eefRS8vr1pLhv3973+XMxV++eWXV5UMk8pHSSXDpEyIUskwacmSYc5l9+7d4ocffii3Malk2KZNm8QBAwaIMTExVdnv2ebIUqRMpy1bthSHDRsmXrx4UUxJSam6VWJ7I0uSSi5Jx7dXXnlF9PDwkO9Lt/z8fLY3sruSYdK5mHRO9tRTT8klw6S/y0SVpONW5TFMig+kcojS/crSctJ5upStXDrvl87/pTigtpJhUtwgxQ9SHCH9Pa6tZFinTp3krOXSrWPHjrWWDKsrFnE2DLpdMOiWSF+usWPHim5ubqKfn5/cyKuXB5McPnxYHDhwoJy+XyohMHfu3KpyYZU2b94sdu3aVdRoNGJUVJT4ySefXLUNP/74o9i6dWv5j4FUvkKq/UfOQ2onUp1FqR1JbUVqB9IBWQqIqmObI0uVbZKOabXd2N7IGu67775a29sff/xRtQ6Pb2QPPv74Y7F58+byOVm3bt3ELVu22HqTyM5Ix63ajmfScU4inefPmTNHPu+XzukGDRokB9/VFRcXy3GDdN4nxRFSMH3+/Pka62RmZopTpkyRa35LtylTpojZ2dk11qnPcdOZCNI/tu5tJyIiIiIiInJGnNNNREREREREZCUMuomIiIiIiIishEE3ERERERERkZUw6CYiIiIiIiKyEgbdRERERERERFbCoJuIiIiIiIjIShh0ExEREREREVkJg24iIiIiIiIiK2HQTURERERERGQlDLqJiIiIiIiIrIRBNxEREdWQmZmJwMBAJCUlNWrP3HHHHXjvvfe4V4mIiBh0ExERubannnoKEydOrPHY/PnzMX78eERFRVU9NmjQIAiCgNdee63GuqIoonfv3vJzL7/8svyYtJw3bx7y8vKa6FMQERHZL/Z0ExERubC9e/eiV69eVT8XFxfjyy+/xPTp02sE1gcPHkTz5s1x5MiRGv9/yZIlSE5Olu9369ZNXnbq1EkO2L/99tsm+xxERET2ikE3ERGRCzIYDNBoNNixYwdefPFFuada6rFeu3YtVCoV+vbtW7XuqVOnkJ+fj2nTptUIuqXHnn/+eflxSffu3aueu+WWW/Ddd9818aciIiKyPwy6iYiIXJBSqcS2bdvk+1IvdkpKCtatW4etW7eiR48eNdbdv38/dDodJk+eLAfgpaWl8uPSUPMuXbogJCQEAQEBiIiIqPo/Uu/5nj17qtYlIiJyVSpbbwARERE1PYVCIQ8L9/f3R+fOnasel5KnhYaG1lj3wIED8pDx2NhYuLu749ixY/Jy4cKF2LdvH955550avdySsLAwOeBOTU2Vh6UTERG5KgbdRERELiouLq5GwF05p1vq1b6yp1sKqqUh6FLwHR8fj6VLl+Lhhx9GmzZt5OdvvvnmGv/Hzc1NXhYVFTXBJyEiIrJfHF5ORETkoqRh5VcG3dIw8ezs7KuC88okadL6H3zwgTx0fM6cOSgrK8PRo0ernq+UlZUlL5s1a2b1z0FERGTPGHQTERG5KCkpmtRzXV3Xrl2RkJBQ9fOZM2eQk5NTNXxcmsMtDSmXSoJ5e3vLryElZbtyeLnUGx4eHi4H8URERK6MQTcREZGLMplMOHz4sDy3Ozc3V35s1KhRcs91ZW+3NHRcynLeoUMH+ef77rsP6enpVSXFpPnevr6+iI6OrvHaf/75J0aOHNnkn4mIiMjeMOgmIiJyUa+//jq+//57OenZq6++Kj/WsWNHOXv5Dz/8UBVUSwG3Wq2Wf5aWUu+1NL+78nmpd7y6kpISLF++HA899FCTfyYiIiJ7I4iiKNp6I4iIiMh+rFmzBs8884w8RFzKct5QH3/8MVasWIH169dbZfuIiIgcCbOXExERUQ1jxoyR63FfunSpRu3t+pJ6wz/88EPuVSIiIvZ0ExEREREREVkP53QTERERERERWQmDbiIiIiIiIiIrYdBNREREREREZCUMuomIiIiIiIishEE3ERERERERkZUw6CYiIiIiIiKyEgbdRERERERERFbCoJuIiIiIiIjIShh0ExEREREREVkJg24iIiIiIiIiWMf/A/sitglHdf9KAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hp22_py_real_imr = imr_modes_py[(2, 2)].real()\n", - "hp22_c_real_imr = imr_modes_c[(2, 2)].real()\n", - "hp22_seob_imr = modes_seob['2,2'].real\n", - "\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", - "# hp22_py_real_imr.start_time=0\n", - "# hp22_c_real_imr.start_time=0\n", - "\n", - "M = (m1+m2)*lal.MTSUN_SI\n", - "R = distance* 1e6 * lal.PC_SI / lal.C_SI\n", - "\n", - "# plt.plot(hp22_py_real_imr.sample_times/M,hp22_py_real_imr*R/M,label='Python')\n", - "# plt.plot(hp22_c_real_imr.sample_times/M,hp22_c_real_imr*R/M,label='C',ls='--')\n", - "# plt.plot(t_seob,modes_seob['2,2'],label='SEOBNRv5EHM',ls='-.')\n", - "\n", - "plt.plot(hp22_py_real_imr.sample_times/M,hp22_py_real_imr*R/M,label='Python')\n", - "plt.plot(hp22_c_real_imr.sample_times/M+12,hp22_c_real_imr*R/M,label='C',ls='--')\n", - "plt.plot(t_seob+8,hp22_seob_imr,label='SEOBNRv5EHM',ls='-.')\n", - "\n", - "plt.xlabel(r\"$t (M)$\")\n", - "plt.ylabel(r\"Re [$h_{22}$]\")\n", - "plt.legend()\n", - "plt.grid()\n", - "# plt.xlim(left=-4000,right=-3000)\n", - "# plt.xlim(left=-100,right=100)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e4bd117b-cda0-4853-b808-aae85cd4fb3e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(hp22_py_real_imr.sample_times/M,np.abs(imr_modes_py[(2, 2)]*R/M),label='Python')\n", - "plt.plot(hp22_c_real_imr.sample_times/M,np.abs(imr_modes_c[(2, 2)]*R/M),label='C',ls='--')\n", - "plt.plot(t_seob,np.abs(modes_seob['2,2']),label='SEOBNRv5EHM',ls='-.')\n", - "\n", - "# plt.plot(hp22_py_real_imr.sample_times/M,hp22_py_real_imr*R/M,label='Python')\n", - "# plt.plot(hp22_c_real_imr.sample_times/M+12,hp22_c_real_imr*R/M,label='C',ls='--')\n", - "# plt.plot(t_seob+8,modes_seob['2,2'],label='SEOBNRv5EHM',ls='-.')\n", - "\n", - "plt.xlabel(r\"$t (M)$\")\n", - "plt.ylabel(r\"$|h_{2}|$\")\n", - "plt.legend()\n", - "plt.grid()\n", - "# plt.xlim(left=-4000,right=-3000)\n", - "plt.xlim(left=-500,right=100)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7166baea-aa99-4171-bdc1-fcd59894828f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The match with C is: 0.2150\n", - "The match with python is: 0.2149\n" - ] - } - ], - "source": [ - "from pycbc.waveform import get_td_waveform\n", - "from pycbc.filter import match\n", - "from pycbc.psd import aLIGOZeroDetHighPower\n", - "from pycbc.types import TimeSeries\n", - "\n", - "TS_py = TimeSeries(hp22_py_real_imr,delta_t=delta_t)\n", - "TS_c = TimeSeries(hp22_c_real_imr,delta_t=delta_t)\n", - "TS_seob = TimeSeries(hp22_seob_imr,delta_t=delta_t)\n", - "\n", - "f_low = 20\n", - "sample_rate = 2048\n", - "\n", - "tlen = max(len(TS_seob), len(TS_c), len(TS_py))\n", - "TS_c.resize(tlen)\n", - "TS_py.resize(tlen)\n", - "TS_seob.resize(tlen)\n", - "\n", - "# Generate the aLIGO ZDHP PSD\n", - "delta_f = 1.0 / TS_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", - "m1, _ = match(TS_c, TS_seob, psd=psd, low_frequency_cutoff=f_low)\n", - "m2, _ = match(TS_py, TS_seob, psd=psd, low_frequency_cutoff=f_low)\n", - "print('The match with C is: {:.4f}'.format(m1))\n", - "print('The match with python is: {:.4f}'.format(m2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e6461bd-c4bf-4d26-89c6-410a13e3a400", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From fff15efd7122fa2c1adc7f7f2205c837c25c73db Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 03:34:09 +0530 Subject: [PATCH 35/36] Added author/translator name --- esigmapy/python_codes/esigma_go_terms.py | 2 ++ esigmapy/python_codes/esigma_pn_inspiral.py | 2 ++ esigmapy/python_codes/esigma_pn_main.py | 2 ++ esigmapy/python_codes/generator_python.py | 2 +- 4 files changed, 7 insertions(+), 1 deletion(-) diff --git a/esigmapy/python_codes/esigma_go_terms.py b/esigmapy/python_codes/esigma_go_terms.py index 0ddec31..5b37bf3 100644 --- a/esigmapy/python_codes/esigma_go_terms.py +++ b/esigmapy/python_codes/esigma_go_terms.py @@ -1,3 +1,5 @@ +# Translated from ESIGMA_GO_Terms.c by Samanwaya Mukherjee, 2026 + import numpy as np from dataclasses import dataclass diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py index aff67ec..b17b8af 100644 --- a/esigmapy/python_codes/esigma_pn_inspiral.py +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -3,6 +3,8 @@ ==================== 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 diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py index cd3d9be..49f0c55 100644 --- a/esigmapy/python_codes/esigma_pn_main.py +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -1,3 +1,5 @@ +# Translated from LALSimESIGMA.c by Samanwaya Mukherjee, 2026 + import numpy as np from scipy.integrate import solve_ivp from scipy.interpolate import CubicSpline diff --git a/esigmapy/python_codes/generator_python.py b/esigmapy/python_codes/generator_python.py index 14bd2c1..5552a1d 100644 --- a/esigmapy/python_codes/generator_python.py +++ b/esigmapy/python_codes/generator_python.py @@ -1,4 +1,4 @@ -# Written by Samanwaya Mukherjee (2026) +# Written by Samanwaya Mukherjee (2026), based on generator.py in esigmapy # """Functions specific to generating ESIGMA waveforms""" From 4107f2b889b1cbd6bbf11445acaa7823ee7ec1a1 Mon Sep 17 00:00:00 2001 From: Samanwaya Mukherjee Date: Thu, 14 May 2026 04:51:06 +0530 Subject: [PATCH 36/36] Add plots for relative difference between orbital variables --- notebooks/esigma_python.ipynb | 582 +++++++++++++++++++--------------- 1 file changed, 331 insertions(+), 251 deletions(-) diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb index c614ed5..2f992cb 100644 --- a/notebooks/esigma_python.ipynb +++ b/notebooks/esigma_python.ipynb @@ -23,7 +23,7 @@ "import matplotlib.pyplot as plt\n", "\n", "import esigmapy\n", - "from esigmapy.python_codes import generator_python as gp\n", + "from esigmapy.python_codes import generator_python as genpy\n", "from esigmapy.python_codes import esigma_pn_main as espn" ] }, @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "4ec5f8c9", "metadata": {}, "outputs": [ @@ -84,9 +84,9 @@ "source": [ "m1 = 8.0 # masses (in solar masses)\n", "m2 = 48.0\n", - "spin1z = 0.8 # dimensionless spins\n", + "spin1z = -0.8 # dimensionless spins\n", "spin2z = -0.8\n", - "eccentricity = 0.3 # starting eccentricity\n", + "eccentricity = 0.4 # starting eccentricity\n", "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", "\n", "# Creating modes list\n", @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "a666270a-c644-473b-8559-57df6e16217c", "metadata": {}, "outputs": [ @@ -138,17 +138,19 @@ "output_type": "stream", "text": [ "================ Python ================\n", - "Orbital evolution took: 1.5863845809999475 seconds\n", - "Modes generation took: 0.9311780489999819 seconds\n", + "Orbital evolution took: 0.8314332690006268 seconds\n", + "Modes generation took: 0.4021174190002057 seconds\n", "================= C =================\n", - "Orbital evolution took: 0.0230427660001169 seconds\n", - "Modes generation took: 0.0905804829999397 seconds\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 = gp.get_inspiral_esigma_modes_py(\n", + "inspiral_modes_py = genpy.get_inspiral_esigma_modes_py(\n", " mass1=m1,\n", " mass2=m2,\n", " spin1z=spin1z,\n", @@ -178,15 +180,16 @@ " modes_to_use=modes_to_use,\n", " verbose=True\n", ")\n", - "\n", - "# for mode in modes_to_use: \n", - "# inspiral_modes_c[mode] = inspiral_modes_c[mode][2:] # trimming the first two entries to make the arrays same in length\n", - "# inspiral_modes_py[mode] = inspiral_modes_py[mode][2:] # trimming the first entry to discard initial time flucatuations" + "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": 11, + "execution_count": 5, "id": "9fc2cdd6", "metadata": { "scrolled": true @@ -196,13 +199,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "907\n", - "908\n" + "529\n", + "529\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -211,26 +214,31 @@ "output_type": "display_data" }, { - "ename": "ValueError", - "evalue": "different epoch, -0.884765625 vs -0.8857421875", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 34\u001b[39m\n\u001b[32m 30\u001b[39m \n\u001b[32m 31\u001b[39m \u001b[38;5;66;03m### Plotting difference ##\u001b[39;00m\n\u001b[32m 32\u001b[39m \n\u001b[32m 33\u001b[39m eps = \u001b[32m1e-30\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m34\u001b[39m abs_diff = np.abs(inspiral_modes_py[plot_mode]-inspiral_modes_c[plot_mode])\n\u001b[32m 35\u001b[39m rel_diff = abs_diff / np.maximum(np.abs(inspiral_modes_c[plot_mode]), eps)\n\u001b[32m 36\u001b[39m \n\u001b[32m 37\u001b[39m plt.figure(figsize=(\u001b[32m10\u001b[39m, \u001b[32m4\u001b[39m))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:257\u001b[39m, in \u001b[36mArray._returntype..returntype\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mreturntype\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m ary = \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;66;03m# pylint:disable=not-callable\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ary \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:68\u001b[39m, in \u001b[36m_convert..convert\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 65\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mconvert\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m 67\u001b[39m _convert_to_scheme(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/array.py:274\u001b[39m, in \u001b[36mArray._checkother..checkother\u001b[39m\u001b[34m(self, *args)\u001b[39m\n\u001b[32m 272\u001b[39m nargs = ()\n\u001b[32m 273\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m other \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[32m--> \u001b[39m\u001b[32m274\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_typecheck\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mother\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 275\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(other) \u001b[38;5;129;01min\u001b[39;00m _ALLOWED_SCALARS:\n\u001b[32m 276\u001b[39m other = force_precision_to_match(other, \u001b[38;5;28mself\u001b[39m.precision)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/esigma-dev/lib/python3.11/site-packages/pycbc/types/timeseries.py:118\u001b[39m, in \u001b[36mTimeSeries._typecheck\u001b[39m\u001b[34m(self, other)\u001b[39m\n\u001b[32m 115\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mdifferent delta_t, \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m 116\u001b[39m \u001b[38;5;28mself\u001b[39m.delta_t, other.delta_t))\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.epoch_close(other):\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mdifferent epoch, \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m vs \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m 119\u001b[39m \u001b[38;5;28mself\u001b[39m.start_time, other.start_time))\n", - "\u001b[31mValueError\u001b[39m: different epoch, -0.884765625 vs -0.8857421875" - ] + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "plot_mode = (3,3)\n", + "plot_mode = (2,2)\n", "\n", - "hlm_py_real_inspiral = inspiral_modes_py[plot_mode].copy().real()\n", - "hlm_c_real_inspiral = inspiral_modes_c[plot_mode].copy().real() \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", @@ -266,7 +274,7 @@ "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('absolute difference')\n", + "plt.ylabel('relative difference')\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()\n", @@ -291,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d58e9bd0-9bab-4d13-9ce0-faa44199ca7f", "metadata": {}, "outputs": [ @@ -301,18 +309,18 @@ "text": [ "Mode | Mismatch (%)\n", "-------------------------\n", - "(2, -2) | 0.0235\n", - "(2, -1) | 0.0059\n", - "(2, 1) | 0.0059\n", - "(2, 2) | 0.0235\n", - "(3, -3) | 0.0305\n", - "(3, -2) | 0.0221\n", - "(3, -1) | 0.0512\n", - "(3, 1) | 0.0512\n", - "(3, 2) | 0.0221\n", - "(3, 3) | 0.0305\n", - "(4, 4) | 0.0324\n", - "(4, -4) | 0.0324\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" ] } ], @@ -323,6 +331,7 @@ "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", @@ -338,7 +347,9 @@ " \n", " match1, _ = match(hlm_c, hlm_py, psd=psd, low_frequency_cutoff=f_low)\n", " mm = (1 - match1) * 100\n", - " flag = \" <--\" if mm > 0.1 else \"\"\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}')" ] }, @@ -352,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "311c00be", "metadata": { "scrolled": true @@ -363,99 +374,83 @@ "output_type": "stream", "text": [ "================ Python ================\n", - "Orbital evolution took: 0.8255226190012763 seconds\n", - "Modes generation took: 0.9974629320022359 seconds\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", - "0.04529686788536343. The merger-ringdown attachment with a quasicircular\n", + "0.057552566100066385. The merger-ringdown attachment with a quasicircular\n", "model might be affected.\n", - "Generating MR waveform from 26.534760077516623Hz...\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 (3, 3) mode\n", - "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (4, 4), (4, -4), (3, 3)] modes\n", - "INSPIRAL mode (2, -2) goes from -101.3546327642494Hz to 232.91499581554135Hz\n", - "MERGER mode (2, -2) goes from -1445.6670577656257Hz to 1960.0435620552219Hz\n", - "INSPIRAL mode (2, -1) goes from -40.93098520474169Hz to 48.64173777218727Hz\n", - "MERGER mode (2, -1) goes from -1494.4500188070979Hz to 638.6670205711117Hz\n", - "INSPIRAL mode (2, 1) goes from -48.64173777218727Hz to 40.93098520474169Hz\n", - "MERGER mode (2, 1) goes from -904.0487073878803Hz to 1570.6508051408562Hz\n", - "INSPIRAL mode (2, 2) goes from -232.91499581554135Hz to 101.3546327642494Hz\n", - "MERGER mode (2, 2) goes from -765.0123834327239Hz to 1422.1370130561534Hz\n", - "INSPIRAL mode (3, -3) goes from -148.29287498947824Hz to 252.27611774527918Hz\n", - "MERGER mode (3, -3) goes from -1838.6690771837468Hz to 578.5158946006699Hz\n", - "INSPIRAL mode (3, -2) goes from -186.96965685318918Hz to 152.28707890607078Hz\n", - "MERGER mode (3, -2) goes from -1907.947484729697Hz to 848.0514925883384Hz\n", - "INSPIRAL mode (3, -1) goes from -96.76611359952858Hz to 1006.143153104066Hz\n", - "MERGER mode (3, -1) goes from -1735.3304783700637Hz to 1461.0162129235505Hz\n", - "INSPIRAL mode (3, 1) goes from -1006.143153104066Hz to 96.76611359953785Hz\n", - "MERGER mode (3, 1) goes from -756.1697297819906Hz to 1785.4736608878568Hz\n", - "INSPIRAL mode (3, 2) goes from -152.28707890607058Hz to 186.96965685318918Hz\n", - "MERGER mode (3, 2) goes from -1809.121151166399Hz to 1800.330071935664Hz\n", - "INSPIRAL mode (3, 3) goes from -252.27611774527932Hz to 148.29287498949677Hz\n", - "MERGER mode (3, 3) goes from -1274.7265246727698Hz to 1304.1247413942117Hz\n", - "INSPIRAL mode (4, 4) goes from -391.1483343239296Hz to 292.58362783222134Hz\n", - "MERGER mode (4, 4) goes from -1228.5894136511452Hz to 1539.0787139288514Hz\n", - "INSPIRAL mode (4, -4) goes from -292.58362783222134Hz to 391.1483343239296Hz\n", - "MERGER mode (4, -4) goes from -950.8725340696178Hz to 1285.7310793344852Hz\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: 0.01063374300065334 seconds\n", - "Modes generation took: 0.12618732999908389 seconds\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", - "0.04327354478674823. The merger-ringdown attachment with a quasicircular\n", + "0.058448599740953164. The merger-ringdown attachment with a quasicircular\n", "model might be affected.\n", - "Generating MR waveform from 26.53687260414924Hz...\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 (3, 3) mode\n", - "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (4, 4), (4, -4), (3, 3)] modes\n", - "INSPIRAL mode (2, -2) goes from -101.13788122025537Hz to -9.344887798394275Hz\n", - "MERGER mode (2, -2) goes from -1602.2350110324426Hz to 815.9996534590972Hz\n", - "INSPIRAL mode (2, -1) goes from -40.35333777290539Hz to -4.213193305246234Hz\n", - "MERGER mode (2, -1) goes from -1818.998563105842Hz to 611.6671267873415Hz\n", - "INSPIRAL mode (2, 1) goes from 4.213193305246234Hz to 40.35333777290539Hz\n", - "MERGER mode (2, 1) goes from -950.5339786859378Hz to 1673.0240311970988Hz\n", - "INSPIRAL mode (2, 2) goes from 9.344887798394275Hz to 101.13788122025537Hz\n", - "MERGER mode (2, 2) goes from -836.9570900557211Hz to 1636.851885880366Hz\n", - "INSPIRAL mode (3, -3) goes from -146.3772907860718Hz to -13.353196079332903Hz\n", - "MERGER mode (3, -3) goes from -1258.1511597463514Hz to 613.3064561354477Hz\n", - "INSPIRAL mode (3, -2) goes from -205.0465309682471Hz to -9.04060303994033Hz\n", - "MERGER mode (3, -2) goes from -1310.1967127355997Hz to 936.634685338843Hz\n", - "INSPIRAL mode (3, -1) goes from -108.25866887482478Hz to -8.752817115557857Hz\n", - "MERGER mode (3, -1) goes from -1312.746914232246Hz to 1880.0058565988686Hz\n", - "INSPIRAL mode (3, 1) goes from 8.752817115557784Hz to 108.25866887482478Hz\n", - "MERGER mode (3, 1) goes from -1763.1509420426182Hz to 1866.5824036770528Hz\n", - "INSPIRAL mode (3, 2) goes from 9.04060303994033Hz to 205.0465309682471Hz\n", - "MERGER mode (3, 2) goes from -733.1580996282454Hz to 1243.2247832792584Hz\n", - "INSPIRAL mode (3, 3) goes from 13.353196079332758Hz to 146.37729078605327Hz\n", - "MERGER mode (3, 3) goes from -1528.9189421384854Hz to 509.8709756686946Hz\n", - "INSPIRAL mode (4, 4) goes from 18.728407634539536Hz to 302.89715013651397Hz\n", - "MERGER mode (4, 4) goes from -1340.1955570824953Hz to 1547.4317942831349Hz\n", - "INSPIRAL mode (4, -4) goes from -302.89715013651397Hz to -18.728407634539536Hz\n", - "MERGER mode (4, -4) goes from -977.3154617421369Hz to 1237.298819625818Hz\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": [ - "from esigmapy.python_codes import generator_python as gp\n", - "\n", - "# m1 = 20.0 # masses (in solar masses)\n", - "# m2 = 30.0\n", - "# spin1z = 0.5 # dimensionless spins\n", - "# spin2z = -0.3\n", - "# eccentricity = 0.15 # starting eccentricity\n", - "# mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", - "# modes_to_use = [(2, 2), (2, -2)] # Only using the (2,2) mode\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**12 # time grid-spacing (in s)\n", - "\n", "print('================ Python ================')\n", - "imr_modes_py = gp.get_imr_esigma_modes_py(\n", + "imr_modes_py = genpy.get_imr_esigma_modes_py(\n", " mass1=m1,\n", " mass2=m2,\n", " spin1z=spin1z,\n", @@ -491,13 +486,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "05fba2cf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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LN/Vrptfr0a5dO2zatEmMNn///fe47LLLxIgvpaK74/UiNEf7zz//FCP09bV9U722rnp9Xf3aMsYYaxgH3YwxxloVKhSm0+lEsaqqKCChIMqalmtFQVPPnj2rpe/S8k7EnsrSNN+WUnntUTNF2OqZZ54RASYFQ9Z0YyrMRZWup0+fLlKsR40aVed+g4ODRWpxTdbUYgq+rG1DRbsoSCV9+vQRwSrNj6YU7o4dO+L48eMiBbquyuVUqXvs2LGiWjd1TOTn54sAko6R0peb+jWj00svvVQtnZuey4kTJ8Tzc8frRcEypYY/+OCD4vioDazLrRH6mzoF6LVsqtfWFa+vO15bxhhjDeNuTcYYY60KBWoUbFgLWFnt3LlTFOKytb0zS2NRES16LHtONOppCwVkVBys5vzeQYMG1Zq3a0uvXr1w5MiRWvOQrYW0KEC1BmVVi2dZ13um0dLnn3++2mhpXW1CxbhobjUFjNYAj0ZrKTD8999/0dyv2dGjR1FWVlZZKdwdr1d2djYyMjLw/vvvizawnmi+eUlJibh80003Nelr64rX1x2vLWOMsYbxSDdjjLFWhQKPq6++utp1tGTToUOHcNVVV1W7npZtonRaZ4JuV6Qr0/UUfNEIKo3EWm3ZskWc11Wky2rq1Kn46quvRMozpQNbffvtt2LfQ4YMEetHU5px1TaYMGGCuI5Gh63p9dR+9RXZWrt2rdgv+eeffxAbG4sePXqIffz9998YP348mus1owJr06ZNE8XYqrajq1+viIgIm9XTKQ2egnpqBxrJbqrXlrji9XXHa8sYY6xhHHQzxhhrNZKTk0VF6ZqjozSflUYKa15vHfWzNZpqLwpmnLk/oQrYlNJOqb2UBkwBG1WbfvPNN8UoKQVPhAK6MWPGiHTqqinVdDvdlypkUyozpRHTqCsFTpQWTOs4U7BF83mrjoTSfehUFW1Ho8Q1l7iqyjq/l4JMmhtMgZmHhweMRmOd96FAj9KoKbBzx2tGj33ttdeK9qLRWne+XvRcL7roolrXz58/X7R11dvsfW3ren3teW2Jq15fV762jDHG7MNBN2OMsVajroCsvuspjZhSeJsTFf5atWqVCHQefvhhMWpJo4x33303nn322criYhRUmc1mWCyWWvugJbYohZiCNZrv27VrVxGcXX/99ZXBlj1rM9N86ZqBWlU0Qrpy5UqxrjYtfWVNCaf1pCdNmmTzPjTKay1Q547XjNqD5h1TAErp0PUVHmupr219r29Dr62rXl9Xv7aMMcbso6AS5nZuyxhjjJ0TrOs00+hidHS0SFemAMQ6qng+s6Z8U8rzyJEjxQjoe++9hxUrVuC///6z2UbLly8Xo6Y0eu2ODo677rpLFE6j0V8alWXnzmvLGGPnAw66GWOMnXdeeeUVUb25qnnz5uG2225rtmNqSfbv34/HH39cFPiidGSad0zp0raW6CJPPvmkqIxNS1a52pkzZ9C2bVsRbFcNCmkOMgWOrPW+towxdr7goJsxxhhjjDHGGHMTXjKMMcYYY4wxxhhzEw66GWOMMcYYY4wxN+GgmzHGGGOMMcYYcxMOuhljjDHGGGOMMTfhoJsxxhhjjDHGGHMTDroZY4wxxhhjjDE34aCbMcYYO88UFxfjkUceQVRUlFj/um/fvvjpp5/suu+ePXtwxRVXiPvS2s5du3bFzJkzUVpa6vbjPh/bm3CbM8ZY66Zu7gNgjDHGWNO68sorsWPHDrz11lvo3LkzFi5ciBtuuAEWiwU33nhjnfc7fPgwLrjgAnTp0gUfffQRQkJCsH79ehF079q1C7///nuTPo9zvb0JtzljjLV+CkmSpOY+CMYYY4w1jeXLl2PSpEmVgZ/VuHHjcOjQISQmJkKlUtm87wsvvIDXX38dJ0+eRIcOHSqvv/vuu/Hll18iNzcXgYGBTfI8zof2JtzmjDHW+nF6OWOMsfPeF198AZ1Oh4EDB4pAyMpkMmHkyJGIjo5GRkbGOdFOS5YsgY+PD6655ppq199+++1ITU3Ftm3b6ryvRqMR5/7+/tWuDwgIgFKphFartfs4zpc2d6a9Xd3mjDHGmgcH3Ywxxs57EydOxPz583HixAm88cYble3xxBNPiKDol19+QXh4uEvaiRLMKLC05+QOBw8eRLdu3aBWV59h1rt378rb63LrrbeKYO/ee+/F6dOnUVRUhGXLlokA+v7774e3t7fdx3G+tLkz7e3qNmeMMdY8OOhmjDF23ouNjRWpv1QgjObeEip09fHHH+P9998X85jJnDlz0L9/fzH6+MorrzjUbuvWrRP3t+eUkJDg8tcmJycHQUFBta63Xke316Vt27bYsmWLCBQpvdzPzw9TpkwRgSG1lavbXK/XixFh2pYea+jQodi8eXOranNn2tvVbc4YY6x5cCE1xhhjrAKlOn///fciyLnzzjtFkasHH3ywsn0iIyMxY8YMfPfddw632YABAyqDzIZQteu6rF27FqNHj7a7+jVVzLZSKBR1blvfbRSQUsBHI9C//vorQkNDxaj0a6+9Jip0z507F65scxp5bteuHTZt2oSYmBix3WWXXSbmQVPl9KZs8+Zob3e1OWOMsabFQTdjjDFWJTijVORLL71UBHtfffVVtbahUVlSV5Xuyy+/HKtWrar8u6SkBBs2bMCIESMqr6P5vVUDsnq/pGukJFdFFcRrHl9d4uLiKi8HBwfbHF2lImjE1qis1TPPPIPCwkLs3bu3Mq35wgsvFFXMp0+fjltuuQWjRo2Cq9qcHuOll16q/JtGdx999FGRkt6nTx+72ttVbd4c7e2uNmeMMda0OOhmjDHGKlAgZx15/O233xo1mlozGH/44YeRnJyMYcOG1Up1tnfEND4+XqQX20Kj7jQy3Fi9evXCjz/+KEaRqwaYBw4cEOc9e/as874U+HXv3r3WPOJBgwaJc0qBbmwA2Jg2P3r0KMrKyiorp9vT3q5q8+Zob3e1OWOMsabFQTdjjDFWgeZs06jrhAkT0KlTJ4fb5dlnnxWjsUuXLq21HJSr0ssdNXXqVDFiu3jxYlx33XWV13/77bfi8YYMGVLv8VCQR2nNNHpsRanhhFLA3dXmpaWlmDZtmlhCq+pjN9Tezd3mzrS3u9qcMcZYE6N1uhljjLHz3dq1ayW1Wi35+flJnTp1qnfbO+64Q3r55Zdt3jZjxgxp9OjRUllZmdRSjR07VgoMDJS+/PJLafXq1dJdd90l0U+CBQsWVGsPlUolno/V77//LikUCmno0KHSokWLpFWrVkmvv/665OPjI3Xv3l3S6/XVHof2OWrUKKfb3GAwSJMmTZJuueUWyWKxnJPt7Yo2b6i9GWOMNQ+uXs4YY+y8R+sl0ygkpelSVfKTJ08iPz+/0e3ywQcf4J9//sEff/wBDw+PFtuulMZNo8Y0X5rmUlNhLkqBvummmyq3oRjObDbDYrFUXkdFzGgONVXQpnTuyZMnixHbu+++G+vXr6+2ZjSNzFrTsp1pc3p8mrdMI9hUNKxq4bFzqb2dbfOG2psxxljzUVDk3YyPzxhjjDUro9Eo5vueOXMGu3fvxvHjx0UhrpUrV+KSSy6ptq11LWdaMzk6OlqkOtMyUxQQUpr0119/jdWrV8Pf3x/nu+XLl4sAcd++fWJes6Ntftddd4nUcQquqwbW3N72tzdjjLHmxUE3Y4yx8xqNHn7++eei2BatA00VsAMDAzF8+HAxMkmFuazBHo3I0pJhVc2bNw+33XYbAgICUF5eXq1Y1po1ayoLXp1vnnzySaSkpGDhwoUOtzkF5VTUjC5Xnav9999/i2W0uL3ta2/GGGPNi4Nuxhhj562ffvoJN9xwAz799FPcf//9ldfPmjULb731FrKzs1FQUABPT89mPc5zCbc5Y4yx8w0H3YwxxhhjjDHGmJtwITXGGGOMMcYYY8xNOOhmjDHGGGOMMcbchINuxhhjjDHGGGPMTTjoZowxxhhjjDHG3ISDbsYYY4wxxhhjzE046GaMMcYcVFxcjEceeQRRUVFiLem+ffuKJbEc8fXXX0OhUMDHx8eh+9PSZkqlEh9++CGaQ1FREZ566imMGzcOoaGh4rnQuubuaM+1a9eK/ds6bd261aHjp8e+6667EB0dLdZab9++PVqy1atXY/r06ejatSu8vb3FcV9++eXYtWuXy9+rjWnv+fPni+t37txpc1+TJ08Wa68zxtj5RN3cB8AYY4y1VldeeSV27Ngh1vTu3LkzFi5cKNb9tlgsuPHGG+3eT0pKCp544gkREFHw7AgKciRJwqBBg9AccnJy8OWXX6JPnz644oorRCeCu9vzjTfewOjRo6td17NnT4eO/7HHHsPixYsxe/ZstGnTBv7+/mjJ5syZI9r84YcfRvfu3ZGVlYX3338fQ4cOxb///ouLL77Y5e9VV7Y3Y4ydTzjoZowxxhywfPlyrFy5sjJ4IRSQnDlzBk8++SSuu+46qFQqu/Z1zz334MILL0RQUBB+/fVXh4Nuerx+/fqhOVCgmpeXJ0Y5s7OzGx10O9KenTp1EkGmswwGA3788Ufce++9uP7669EafPbZZwgLC6t23aWXXoqOHTuK4Lhq0O2q96qr2psxxs43nF7OGGOsxdu4caNIW6bRx8DAQEyaNAknTpxo1mNasmSJSAW/5pprql1/++23IzU1Fdu2bbNrPwsWLMC6devECKszaBSzW7duItW4OVjTjZu7PRuL9q/T6UT69bvvviueQ2sILGsG3ITaj0a9k5KSWkTb2qOutHU6JSQkNNtxMcaYK3HQzRhjrEWjecGjRo1CbGysGI2kEVQKKsaMGSMCJUdQGrbJZLLrVJeDBw+KIJfm/1bVu3fvytsbkpmZKebZUspvTEwMnEEj3Y6mlruiPZzlSHvef//9Yns/Pz+MHz9edM401tNPP41nn31WXP7jjz+wZcsWfP/993And7U3TU3YvXs3evTo4fL3amPb22w223w+9NyrovaueqK56jQ/PSIiQmR+MMbYuYCDbsYYYy3WsmXLMGPGDBGUzp07FxMnTsRVV10l5t5S4P37779XbktzWmkEnEZ6ac4qpdPWhUaWNRqNXae6RttoPq2toMB6Hd3ekPvuuw9dunQRac3OoHRuShWuGnQ3dXs4qzHtSRkPNJf5iy++wJo1a/Dxxx+L98NFF10k5jM3BhUio84byqCYMmWKGOWmNGoq9uVIEG8Pd7U3BcUlJSV4/vnnXfpedaS9qR1tPR9Kda+5nfVE71/aN3Ue/PXXXyK4Z4yxcwHP6WaMMdZivfTSS+jQoYP4wV911K9du3bw9PTE6dOnqwUcNDpGweZ///2Ha6+9FidPnkRwcHCt/Q4YMECkY9uDipvVpb506oZSranj4M8//8SePXucSssm1ucycODAZm0PZ9nbnjRvverc9ZEjR2Lq1Kno1auXqKBOo7CNQRW/qQ2aijva+8UXX8QPP/yATz75xOZzcea96kh7f/fdd2J0vaZHH320Vvq71QMPPCCCbfp/0b9//3qPiTHGWhMOuhljjLVI6enpIiAlNOfWloCAAHFOI5VLly7FqVOn4OXlhcsuu0xU0aaRcFpWqSaa30pLJtmjZkquFQWvtkYIc3NzxXl9qbF0vBQUP/jggyKoys/PryzoRehvGhW0d342pZZrtdrKdOHmaA9nOdOe1vcCLUf1+eefo6ysTHTK2IPSoPfu3Stei7ocOnRIFLs7cOCA6ASaNWsWhg8fLqq103JaVKCM9kPHQFXQKTsjMTFRBL80haBmUOvq9qbHe+211/D666+LwNXVbetIe1PAXbUTqOqoua2gm46f9kUZLVQQjjHGziWcXs4YY6xFsv4wp3WnaVTQ1unmm28W21BRNQpkaN63FY3CUbDkrvRe2v+RI0dqzbulwKyhpZQoHTwjI0Ms8URpzdYTzVmn9GC6fNNNN9ndVhR0U8Bt7ZxojvZwljPtaWWdL9yYzAF6zNLS0jpHuqkjhNLOr776apE1QCO79DdVaqeK8xs2bBDbUQcRrU9u/Xv9+vUYMWKEzWNxZXtTwE11D+j03HPPua1tXdXettDa3jRST8/BVqcQY4y1djzSzRhjrEWyjr7RD3pbI2ZV0chuzfmf9DcFt+5K76X02q+++kqkidOSS1bffvutuM+QIUPq3CelfdPc2Jpo7joFZH///TdCQkJgL3oul19+ebO2h7OcaU9CQTDVAKARZA8Pj0Z1WJC6gm6q7E1rWdMUB0LH9tFHH+Gff/4Ry2/p9XrEx8eLIPuuu+4SS3kZjUbxN6Vhu7O9X331VRGovvDCC3j55Zfd1raubO+aqB2p3SjYru85MMZYa8ZBN2OMsRaJ0nhpLWEKKCiIpMCARtbS0tJEwHrrrbeKQk6ERnULCwur3Z/+putt8fX1bTCQb8iECRMwduxYUQSNHovWR6aRagoiaBmwquseUyBN1dZpjjqdKEixHnvNET+6n63bqPOBqrhTOnNV1B50qlpErTnag1BnAY3UFxUVib8PHz5cue44FcGjVHdb7dHY9rzxxhsRFxcnjpk6J2hkn7IGKHuA2tCedqs6n5tSpdu3b2/zdlpSq2rGgHVNcrqe0Gg2jW7Tida83rx5s9gn/U3BpLvam54vtR2lYlPBvK1bt1a7veqyZ868Vxvb3o1BnRW0jBm1PS1fVvM50DzyuqaWMMZYa8JBN2OMsRaL5iXT6C8VZaI5nzRvlH78U1pv1TmxVG2aAvPk5OTKpbdoGaRp06a59fh+++03USmaghOaH0uVsCmYuf7666ttR50FNOeXRkwdYV0aLTIy0q4ias3VHhTUURV1q19++UWcrAEWVQSvrz3sbU9KpV+0aJGYA0zPk7IiKPilpb6qdj7U125WFCDXV7SLRoJrzkGm+drWzAIazaZAlfZDrwH9Tc+D2r5q8TFXo2JjhAJnOtVUc2kuZ96r9rZ3Y9F7hfZ3/Phxm1kBVd8zjDHWmimkmp/KjDHGWCtEI2ZUpImqN69atUoEmDQi15g07ZaKllmiolX79u0T83PP9/ZwZ7tZUbBHo8CDBw8Wy7o9/vjjopjakiVL8L///U9Uzqe595SefvHFF4uUccrAoNHacePGicyM+pZpY4wxdv7gQmqMMcbOCbNnzxYpv1SpmZYlopG5cyXApGCORiQbEziey+3hznariarCU9V3GhWmtnzzzTfxxx9/iICbWEezrSO1FHzTSHFd87kZY4ydf3ikmzHGGGOMMcYYcxMe6WaMMcYYY4wxxtyEg27GGGOMMcYYY8xNOOhmjDHGGGOMMcbchINuxhhjjDHGGGPMTTjoZowxxhhjjDHG3ISDbsYYY4wxxhhjzE046GaMMcYYY4wxxtyEg27GGGPMQRaLBT4+Pnj88cebvQ2//vprKBQKcTy2FBcX45FHHkFUVBQ8PDzQt29f/PTTT05v6+j958+fL46XTmvXrq21D0mS0LFjR3H7RRddhPOFM21P7Wht05qnrVu3Vm63evVqTJ8+HV27doW3tzeio6Nx+eWXY9euXW58Zowxdv5SN/cBMMYYY63VoUOHUFJSgkGDBjXrcaSkpOCJJ54QgVpBQYHNba688krs2LEDb731Fjp37oyFCxfihhtuEB0HN954o8PbOvtYvr6+mDt3bq3Aet26dTh16pS4/XzibNuTN954A6NHj652Xc+ePSsvz5kzBzk5OXj44YfRvXt3ZGVl4f3338fQoUPx77//4uKLL3b582KMsfOaxBhjjDGHfP311xJ9lZ48ebJZW3Dy5MnSlClTpFtvvVXy9vaudftff/0ljnPhwoXVrh87dqwUFRUlmUwmh7a1xd77z5s3T2x35513Sp6enlJBQUG17W+++WZp2LBhUo8ePaRRo0ZJ5wNn237NmjXi/r/88ku922VkZNS6rqioSAoPD5fGjBnj4NEzxhirC6eXM8YYY3b46quv0KtXL5HyS6OGNCK4fft2BAYGokOHDs3WhgsWLBCjwrNnz65zmyVLloi082uuuaba9bfffjtSU1Oxbds2h7Z19rEIjeKSH3/8sfI6Gq1fvHixSIG25ZVXXhEp03v27BEjw35+fvD398fNN98sRm2rOnr0qHiM8PBw6HQ6xMXF4ZZbboFer4e9brvttjrT9l3J2ba3V1hYWK3r6HFp1DspKcklj8EYY+wsDroZY4yxBtAc24ceeghXXHEF/v77b9x///249dZbxeWBAwc2uv1ovrLJZLLrVJ/MzExxbJSKHBMTU+d2Bw8eRLdu3aBWV59V1rt378rbHdnW2cciFDBfffXV+OabbyqvowBcqVTiuuuuq/expk6dKuZ9//rrryIQX7p0KcaPHw+j0Shu37dvn0j9p/nMM2fOFK/Xm2++KQJug8EAV3LFa+ps21vR+5P2QW1L7bFx48YG70MdHbt370aPHj3segzGGGP24zndjDHGWD1oxPXjjz8WxaysQSDNl83Pz8dzzz2HadOmVW578uRJUZyKimHRiHhdaGS65pzbusTHx6Nt27Y2b7vvvvvQpUsX3HvvvfXug+bvtm/fvtb1QUFBlbc7sq2zj2VFI9rUHjRHnoI+CsBptLeh+dw0yv3OO++Iy+PGjROj2TfddBN+/vlncf7YY4+J4JMyEkJDQyvvR7e5miteU2fbnkb7aZ42zY8PDg4W78d3331X/P3XX3+JALy+QJ3qEzz//PN2PQfGGGP246C7EdavXy++vKi6Z1pamkgDo1EPd6He+N9++02kxnl6euKCCy7A22+/LX5gWdHtX3zxhTgm+jKmVDuqdMoYY8w1Xn31VTFaWnPUlVJxSdWR7v3794vP6PoCbjJgwABRLMseVBytrs6AP//8U3zuU6p1Q+rbpuZtjdnW2ccio0aNEin6FGxTKje1DRX2akjN4Pnaa68VGQhr1qwRo+AUCN9xxx3VAm53ccVr6mzb9+vXT5ysRo4cKdqBpkU89dRTdQbdL774In744Qd88skn4nkwxhhzLQ66G4F6gPv06SPmVl111VVwN/qxQD3P9GOP0tGo95l68g8fPiyW+LAe0/Dhw8WIwF133eX2Y2KMsfNJenq6SFH+8MMPa92WnJwszqtWLqdt6XuiITR/1t4O0pqpxoRG0un74cEHHxQBHI26E2vKNP2t0Wgqvyto1NPWKGlubm61kdTGbmuLI/enYJK+W2fNmoXy8nJRtZsCxoZERETUaivr4+fl5cFsNtebdu9Kzr6mrmh7WwICAjB58mR8/vnnKCsrE534Vc2YMQOvvfYaXn/9dTzwwAON3j9jjLGG8ZzuRpgwYYL4YqJ0Nlvoxw71JNN6l/RDZ8iQITbXHrXXP//8I3r8KdWOfsTNmzcPiYmJ1dbRpLTGl156CZdcconDj8MYY8w2a2AdGRlZ6zZayomCvqpBHQXd1vm3DXWqUlBszykhIaHW/bOzs5GRkSFGg6mQm/VEc6GpM5YuVx0FppHOI0eO1JpPfODAgVrLSTVmW1scvT9939HzouCQAnB7O0WqosekoJWCVwpQVSpV5WvoDHtG9519TV3R9vXNN7f1PCjgprnwdKKpEowxxtyDR7pdiH4k0BcpzfujkQdKP7/00kvFl2WnTp2c3r917VVHeroZY4w1njUtmQpYVU0vp8JdmzdvFiOIVVF6+d133+32VGQK9imFuiYqqEbBHxUMCwkJqbyeUoyp+jqlpFd9Ht9++63YP3USO7KtLY7enzqsn3zySTGlilLE7UEp0VXToWkuNwWsNIeZRnQpbf2XX34Ro7hV28OR9wGNEtNa2VTgzV3p5c62vS004r9s2TIxCl912gNNm6Bg+4UXXsDLL7/c6P0yxhhrhDoXE2P1oqZbsmRJ5d+0RqtCoZBSUlKqbUfrXT777LNOt6bFYhFrsI4YMcLm7fHx8eKY9uzZw68cY4y5CH32Dho0SKx9/fHHH4t1kGfMmCEFBQWJz1y6bEXrTNv6HmhKda3TbV3rOTAwUPryyy+l1atXS3fddZd4DgsWLHBo27Vr10oqlapaGzTm/tZ1unfs2FHvc7K1TvfLL78s7tumTRvpySeflFasWCF9+OGHko+Pj9SnTx9Jr9eL7fbu3Suua9++feWx/Pjjj9INN9wgFRYWVu6P9lXfWuBLly4V28ycOVPatGlTg+tlO8Pe18lW+9Pzevrpp8U63fRepX106dJFUqvV0sqVKyu3e++998Q+L730UmnLli21TowxxlyLg24XBd0///yzuI5+7FQ90RfdtddeWy0wru90//3323y8++67T/y4SEpKsnk7B92MMeYe9PlKwQkFbwEBAaIDdO7cueIz+6+//qrcbsOGDVJwcHCzvgz1Bd1FRUXSQw89JEVEREharVbq3bu3CEAd3ZaCOmoDCoAdub8rgu5du3aJ14NeG19fXxF0ZmRkVNv28OHD0jXXXCNeGzqWuLg46bbbbpPKy8srj5X2df3119fb+ULBbGRkpNg2Ly9Pchd7Xydb7f/mm29Kffv2lfz9/UVAHhoaKk2dOlXavn17tftSe9b3W4QxxphrKeifxoyMM1TOi6pavXzRokVi/hwtd0JzyGoWV6FUQFo39NSpU/U2Ic3DoyVPqqJCObT2KFVPb9eunc37UVo73cbVyxljrHnMnj1bpDdTPQ4rSkXWarX8krgYpUXTfOSsrCyn0sbJ8uXLxTQBmo9Pc6oZY4wxV+M53S5CS3RQldTMzMw6K65S8RRav9Ve1B9CATcF91SQra6AmzHGWPOjoI3mU1etDk3BHC3rxVoumht//fXXc8DNGGPMbTjobgRaouXkyZOVf8fHx2Pv3r2isBktb0Ij3bfccouoJktBOFVhXb16tfginzhxYqNfHFoOhqrj/v777/D19a2s0urv71/5o46WEaGK5qmpqeLvY8eOiXMaWa+5lApjjDH3+eKLL8SJtS7vvvtucx8CY4yxcxynlzcCjTaPHj261vVUZXX+/PkifZyWFPvuu++QkpIiliwZNmyYSIFzJGWtriVKaOkwWlqF0OPaWlqFKpFS+h1jjDHGGGOMsebDQTdjjDHGGGOMMeYmthebZIwxxhhjjDHGmNM46GaMMcYYY4wxxtyEC6k1wGKxiCJlVMisrjnWjDHGGGOMMcbOL5IkoaioCFFRUWKZ0Lpw0N0ACrhjY2Nd/fowxhhjjDHGGDsHJCUlISYmps7bOehuAI1wWxvSz88PTYGqoK9YsQLjxo0Ta3szxu87di7jzzzG7zt2PuHPPMbvu3NHYWGhGKC1xox14aC7AdaUcgq4mzLo9vLyEo/HQTdrKvy+Y82F33uM33fsfMKfeYzfd+eehqYht6pCauvXr8eUKVNEzjw9saVLlzZ4n3Xr1mHAgAHw8PBA+/bt8fnnnzfJsTLGGGOMMcYYY60q6C4pKUGfPn3w6aef2rV9fHw8Jk6ciJEjR2LPnj147rnn8NBDD2Hx4sVuP1bGGGOMMcYYY6xVpZdPmDBBnOxFo9pxcXH46KOPxN/dunXDzp078d577+Gqq65y45EyxhhjjDHGGGPn+JzuLVu2iGJkVY0fPx5z584V82lszZfW6/XiVHVyPKHt6VQXs9kMk8kkysY7i/ajVqtRXFwszpltNMWA2kelUnETuYD1/V3f+5wxd+D3HmsO/L5jzYXfe4zfd+cOe383n9MRXXp6OsLDw6tdR39TUJudnY3IyMha93nzzTcxY8aMWtdTNXEqbmYLVaujU31rszVWREQETp8+7bL9ncvrqNPaeHRirrFy5UpuStYs+L3H+H3Hzif8mcf4fdf6lZaW2rXdOR1026okZx2JrqvC3LPPPovHHnusVhl4GjG3Vb08IyNDbBMaGiqC8oYq19mDjpHmr3t7e7tkf+cqaid6o2dlZaFz5861OlhY43vq6AfA2LFjuWo+a1L83mPNgd93rLnwe4/x++7cYc2KPq+DbhotptHuqjIzM0VKcnBwsM376HQ6caqJUtFrpqNTSjmNsFKwV9f+HB29pQ9kT09Pl46en4uoY4LaiF5XylzgVHPn2XqvM9YU+L3HmgO/71hz4fcec6XEnFIk55figg4h/L5rQvb+Zj6nI7phw4bVSt2hNPGBAwe6JKiw5vDXlXbOmoa1/XkuMmOMMcYYO99IFjMufHc1bvxqG46l85TLlqhVBd1UWGzv3r3iZF0SjC4nJiZWpobfcsstldvfc889OHPmjEgXP3LkCL755htRRO2JJ55w6XFxCnjz4vZnjDHGGGPnq5TtS5DgcRMSPG5E9skdzX04rLUH3bTcV79+/cSJUDBNl1966SXxd1paWmUATtq1a4fly5dj7dq16Nu3L1599VXMmjWLlwtjjDHGGGOMnRMyD62vvOybwUF3S9Sq5nRfdNFF9S7JNX/+/FrXjRo1Crt373bzkTHyyiuvYOnSpZWZCIwxxhhjjDH38krfXnnZUpLDzd0CtaqRbuY6t912m0jLphPNb2/fvr1Iu6eq6fag+1GAzRhjjDHGGGseOfkFaGc4cfaKsjx+KVqgVjXSzVzr0ksvxbx580QBsg0bNuDOO+8UQfecOXO4qRljjDHGGGvhDu9ah5EKU+XfqvKGg+4nf9mHUoMZH1/fF2oVj8E2BW7l8xgtjUbLqtE65DfeeCNuuukmMXrdsWNHvPfee9W2PXjwoFia69SpU2jbtq24burUqWLE2/q31ffffy+u8/f3x/XXXy+WVbPS6/V46KGHEBYWBg8PD4wYMQI7dpyde0Lz72mfq1atElXmqTL5BRdcgGPHjrm9PRhjjDHGGGtNFBmHqv2tMeTXu31pWSmwdwG0h37GT9sT3Hx0zIqDbhejOeelBpPTpzKDuVHb1zfX3V60LjiNek+fPl2MgFdFld9HjhyJDh06VAbJtA0Vr6saNFNQToH7smXLxGndunV46623Km9/6qmnsHjxYnz77bdirj0F+OPHj0dubm61x3v++efx/vvvi+J5tK46HRNjjDHGGGOsSuxRXijOsyV/ca4zFtTbPOayYryr+RIfaufgq9WHoTeZuTmbAKeXu1iZ0YzuL/2LpnZ45nh4aR1/Obdv346FCxdizJgxuP3220VFeLpu8ODBIhBfsGAB3n33XbFtaGioOA8ICBAj5VVZLBZR0M7X11f8PW3aNDFq/frrr1emrtPtEyZMELd/9dVXYi11WsrtySefrNwPbU9F8MgzzzyDSZMmoby8XIyOM8YYY4wxxoAcRSC2WbrCoA3ASNNWeJrkILwuJpOx8vIPhoexeGcv3Di0DTelm3HQfR6jkWgfHx+YTCYRWF9++eX45JNPROo3Bbk0uk1BN21HAe8111zT4D4prdwacJPIyEhkZmZWjoLT4wwfPrzydiriRo9B66hX1bt372r7ILSfuLg4lzx3xhhjjDHGWruNPuPxq6EHLo3zwpent8IzIBxf1rO9yWSovByjyEbmqd0AB91ux0G3i3lqVGLU2Rk0WlxUWARfP18xj9rex22s0aNHi5FnCnyjoqLEuRUVVaNR6g8//FCkkV933XVifnVDqu6D0Pxsej7EmgJP11VF19e8rup+rLdZ98MYY4wxxhgDisvlImrBwSH452RvhBi09TaLpcpIN+lUsJkqNXFTuhkH3S5GAaIzad7W4NKkVYn92Bt0O8Lb21vMqbZl4sSJ4nYKyv/++2+sX7++VlBsNjduDgg9llarxcaNG0XhNkIj3zRv+5FHHnHimTDGGGOMMXb+KdbLQXdMoDw4ll9qtDmgZWWuEXQH6lOb4CgZB93MJpVKJdbyfvbZZ0WwPGzYsFpp5DRXm1LFqQp6YGBggy1JQfy9994r5m4HBQWJVPF33nkHpaWluOOOO/iVYIwxxhhjrBEeznweH+qOI778DVymPIwIRS5Kc/vAOzjarqBbIZ1dboy5D1cvZ3WiQNhgMNisHE6VxakAGi031q9fP7tbkSqZX3XVVSJ1vX///jh58iT+/fdfu4J2xhhjjDHG2Fm+pjyEKgrg66nFY5rFeE7zI0rS6l5q11xlTjdRWKoH4cw9OOg+T1EFcVraqz60HBgt13XLLbfUum3KlCk4ceKESA9PSJDX+HvllVewd+/eattR2rj1dkLVx2fNmoWsrCxRnI1SzQcNGlR5+0UXXSRSYqgyulXfvn3FdTXXA2eMMcYYY+x85mEpFec6b38UKeVixmUF2XVuX+4ZhXsND2ODuaf4W8VBd5Pg9HJWi16vR1JSEl588UVce+21CA8P51ZijDHGGGOshfGSygAF4OHjjyyVP2ACjEV1B916jS/+tgyBGUpoFSakquNwdviLuQuPdLNafvzxR3Tp0gUFBQVizjVjjDHGGGOsZTFbJHihTFz28glAudpfXDYW59Z5H5NZXg1ohWUQrjO8hJ99pjXR0Z7fOOhmtVABNapMvmvXLkRH2y7CwBhjjDHGGGs+xeUG+CjKxWVP3wAYtPL0TEtpTp33UZRkYrJyC4YrD4i/Tbwkb5PgoJsxxhhjjDHGWpniwvzKyzovf5h1ctCtKKt7pNsj9yg+1X6C59ULxd9Gs9QER8p4TjdjjDHGGGOMtTIl5eXYbukCP6UBXdUekDzl1YBU5Xl13sdilquVd1eewU7dPUjI7wRgdZMd8/mKg27GGGOMMcYYawpZx4G9C4BBdwIBcU7tqhC+uNbwMtoEe2GdQoGMiIsw7aQavUN64ck67iNVWac7RFGIbEuRU8fA7MNBN2OMMcYYY4y5kcUi4cEf9+B/J+5GHxxHckYWYm6e49Q+i/Qmce7rIYd0qsBYbLD0hrclosGRbiuVJO+DuRfP6WaMMcYYY4wxN4rPKcFfB9Kw0DhK/C0l7XB6n8XlcsDso5ODbn8vrTjPLzPUeR/JUj3I5qC7afBIN2OMMcYYY4y50eHUQnG+xdJdnIfrEwAadVZpHN6n35n/sF33IhIK+gL4HYGWPNyoWoWwIlo6bFi96eVl0MITBqhgdvjxmf14pJsxxhhjjDHG3Cg+MRHeKEO7jj1QKHlBCyOMGUec2qelLBdhinz4olT87aNPxxuaubix+Ls67yNVpJfroRPnPNLdNDjoPo+lp6fjwQcfRPv27aHT6RAbG4spU6Zg1apVzX1ojDHGGGOMnTO6HP8ChzzuwGPa33AUbcV1OSecSzGXyuXRc7PGR5yrtZ7iXIPq87arSgvoj8cN9+BnzRXyfcBzupsCp5efpxISEjB8+HAEBATgnXfeQe/evWE0GvHvv//i/vvvx9GjR5v7EBljjDHGGDsn+Befks8j2mJfagZQdhhlibsB3OHwPiV9sTi31Ai6tVLdc7rzPWKw2HIhxngUYqh+I/IUgai77BpzFQ66z1P33XcfFAoFtm/fDm9v78rre/TogenTpzfrsTHGGGOMMXauyC7Wo40lCVAA4e37oOxMMZC8BMb8VKf2qzDIy31JOjno1ng0PNJttEjiPNczDpdlvQ4PjRI81OZ+HHS7i6Gk7tsUKvpfUfe2FgtgLAUMKkClBjSe9e9XezZotkdubi7++ecfvP7669UCbisa/WaMMcYYY+xcZjixFlJBMnQDb3br4xw/k4ILFLnismdUdxg6+qP3yTgMC+iAL5zYr9IaF+h8xZlG5yXOtZQyTvGEsvZMYu+iM7hYuRuh6Ig98IPJLAfhzL046HaXN6Lqvq3TOOCmX87+/W5HOciuQP89KsPeNiOA2/86u+1HvYDSnOr7e6WgUYd28uRJSJKErl27Nup+jDHGGGOMtXYGkwV//jALV5x+BSqFhDyPCAT2vMRtj5dxap84z1eFIMAzAB1iTChEAo5nyOnhjlKb5PsrPfzEuVZXZaDOrAeUVf6u0C7zP3yjnY31pROwCNNgskgiLqAMWOY+XEjtPET/sQj/52Lnir1J+bjis01Yvt+5NC3GGGOMnfu+3nAK2pN/i4CbFKz71K2PZ0iTq5QX+rYX553D5ZHphJwSlBkcX7IrXQrCEUscJN/IWkG3xVBu8z6SRU4990UxNmgfxkbdQyLwZu7FI93u8lxq/enlVT15stqfFosFhUVF8PP1hZLSy6t65IDTh9apUycRcB85cgRXXCFXLmSstUrKLcWd87bhVsNC9F+6GWi7DvCrJ9OEMcYYY+e1dcezsct4H3K9O+LW8gWIzVoH5CcCAXFueTyfIrmImiGoszgP8dHicd1S9LEcQe4hNaL7jXdov3PUN+OUYSp+6jxU/E2rEf3P8CjKocWXKg9Umcx6lqWiWrlSi1hllrhYZrJAo+KxWHfi1nUXmmdd16nqfO66ttV4VZx7NrxtIwUFBWH8+PH47LPPUFJSe454fn5+o/fJWHPZ8tUj6FB2AEOURxBhyUTe1gX8YjDGGGPMJhpZ3pOYDxPU6H79q9ho6QkVLChY78zs6vrtMrfHD6YxMMRdJP6mwa9+6gRcqDoAQ8YJh/dbrJcDaB+dPEinVauwwjII6y19oJdsj60qzPJ9LGp5nW5iNOkdPgZmHw66z1OzZ8+G2WzG4MGDsXjxYpw4cUKMfM+aNQvDhg1r7sNjzC6ZuXm4qvRnLNK9itNefcV1ir0LaQ4FtyBjjDHWAmXn5eHwrKtw5v2LYCrJa/LH3xWfBYvZgEh/DwxsE4i9IVPE9frj/7ntMX8pHYjnTXdA231C5XWl2hBxbixIc3i/xeXVg26NSgHr1Gy9qY60daki6FadHdgzGTjodjcOus9T7dq1w+7duzF69Gg8/vjj6NmzJ8aOHYtVq1Zhzpw5zX14jNnlzNHdYj5WvsIPpmEPokzSIqA0HsiU504xxhhjrOXYl5CJwx9NRffc/9CmaA+Sfnm6yY8hZc+/OKC7E5/qZosR57ieI6GXNMjVK93SaV+iN6GoYkQ6wv9stqvBQw66peIMh2s0LcLTWKV9HL4lCeI6ej6T1DtxrWoNjAWZNu+nqJjTbVHLlc6JyVj3ut7MNXhO93ksMjISn376qTgx1hrlx+8V55meHXFR7w44tKotBiqOoyjpIHzDuzf34THGGGOsiuN/vo9rFHughwY6GNEuYREsCbdB2faCJmsnc+I2eCoMCPWVA+D2nXug+4pv4K/2xG43VPDOKChDB0UKijUh8NGeretk9goF8gBViTyvurH0JgvaKdLgrdCjRKetvP4Z1QLEqDKRlDsFiKk9R11hqZ1ezkG3+/FIN2Os1ZIyDolzQ3A3xAR6IUcbLf7OSeKRbsYYY+e3wnIjft+bgsOphTC3gOrUReVGtM9eIy6nDXoW30iXYZHpIuyOd2yk1xHlRjNiiveLy36dh4vzDmG+sChUyC0xILvY9WnWOdmZWKV7EtuUtwNV5k4rfMPFubbcsaC7VG8SATfx8JKXDCMGyAG4qY7q5bv9xuAF4+1ICh4BoyR3Aph5pNvtlK1xLjKlRnt4eGDAgAHYsGFDnduuXbtWpFnUPB09erRJj5kx5h6BRXLxEa/Y3uK81KeNODdln+YmZ4wxdl575Ke9ePinvZg4awNe/fRLmLLkCtrNZfX+0+gB+fu5zfBrkDX0OTxt+h/mnIlpsmNIzClGX4W8apB/RdDtqVUhLkhOtT6eXujyxyzMShLnxQqfasWUNf7yMl9ehhyH9ltWWlR5WaWrkiqusAbdZTbvd9KzFxaYxyI7oBdOKWJxxBILE9fCcbtWFXQvWrQIjzzyCJ5//nns2bMHI0eOxIQJE5CYmFjv/Y4dO4a0tLTKEy2ZxRhr3bKK9GhnkecwRXYeULkUx0FLW6RLwc18dIwxxs51SZl52LJhJUoO/g2U5qIl2XA4ETuPxkOlVOA+zTK8kvsUsn99rFmPaemhAgzUz8GSHh9DERCH8T3loHN3Yp6Yn9wUUpIS4KcogxlKKMLOTkOb7HsSf2ufQcy/d7r8MUtz5WWEKb28Ks/AyGpzrBtLX1ZlBSJa9aiCSakR5+Y6RrpNZrmtNUolbla/jwmGt1HuIY+6M/dpVUH3Bx98gDvuuAN33nknunXrho8++gixsbENFv4KCwtDRERE5UmlqrFONmOs1Tl26hRCFIWwQAHPqB7iutIOEzHZ8AYWeN7U3IfH2Hlvf3I+Nn8/A8fm3glJf3ZEhrFzQXp+KbJnT8CwVVfD+9frkfPZ2Gqpw83JZLbgwO8fYbPuQXzefjOiBl8p0ogjMtZCOrGyWY6JioltOJGNYnih16irxXXdI/3gqZYQXXYMaUe2NslxFKYeE+d5mnBAJQenJCI4AN2UiQjIO+DyxzQVyEF3uUdoteu9w9uhc/m3uNJzrkP71VeMdNP8eChVtUa6LUbbQXdY6UkMUx6CrzFLVDuvGogz92k1hdQMBgN27dqFZ555ptr148aNw+bNm+u9b79+/VBeXo7u3bvjhRdeEBW766LX68XJqrBQTjMxGo3iVBX9TT1zFotFnFzF2ttn3TerH7URtRW9Htyh4jjr+7vm+7yl2p0JvKx/F9e0M+IOhYYOHNH+clGQMzklreZ5sNb33mMN+3lnMp7//TCmKA34RPsL0mefQPBdSwCdb4tpPn7ftUw7jydCv3MBogt2I2TCc/Bp2x8tjcFkwa9z38YDOCKqXpugRHDJSeT+NRO+E19p9vfetlOZuKz8d/goyjG0ayzKew3HTzvHYRr+Rta6LxHYVl4ruintS8yDySIh3E+HNoE68dwp3HvafxVuK/kGKesuhrHTz24/jvIMObW8xCsG/lXa3yemJywHFfAz5cCYnwZ4Vx+VdoZUmC7OjZ6h1V7zAE81DNAgu0jv0HuhtLhAnJfDA8oq969MLy+v/lvIenlCzrd4XrsRW7IkqJSDxHVlBgN/BzvI3teu1QTd2dnZYl3p8PDq6Q/0d3q6/Ga2VZ37yy+/FHO/KZD+/vvvMWbMGDHX+8ILL7R5nzfffBMzZsyodf2KFSvg5XU2dYOo1Woxcl5UVCQ6BVyN9ssaRq9tWVkZ1q1bJ94jzDkrVzZPL3hjbTqpxCkpGgeMZixfvlxcl15K/6oRn5WP5cv+rNbzy1q+1vLeY/XTm4E399D/PQXyPWIBCxBRsBdHv7gZxzre3eKaj993LceprEJcn/QSohRyqnbJD9uwuePjyPftjJZkUzpwVcE/Il90T/jV+C8vDC8YP4b/ntlYVxaFIs+YZn3vHT15HCMU2SiCD9ZmBcGydiVO+Q4Fiv+GT/J6/L3sD0jKpg0BDiRm4EfNPCRZOmL58rNp0UlKubq2X+Z2LP9rGaBwbxLu3gwLAswDoZLaYG/FbweSWgIkSmFoq8jAtj/nI8fPdSugmPPkOd2ZpcDhKo9ZJoqIq1FiMGPpn8tRpbC5XTIyUuFtiYVB6YH4KvsNlouTI+H0iWrXWxnL5fZPScvAe2UvIlSbg02r70daaO1K56xhpaXix+e5E3RbUSG0qmiEs+Z1Vl26dBEnq2HDhiEpKQnvvfdenUH3s88+i8cee6zaSDelsNOIup/f2cqA1hHW+Ph4sU1oaCg0Gk2dx9IY9JxKSkrg7e3tkv2dq6yj29T+1Fa0zrhS2apmTLQo1Jb0A4Dakd7LLd3Cb3YAWXkYPaQvJvaR50XpjWaEH56KS5U7UB4zG959pzTJ+zC9UI9wXx2USv7/ej6891j95q/aB4UxHnFBwfjiodvxzrdaPJX2KDoWbUOHC78CfMJaRBOe6++7/FIj7v5hD+KzS9Ah1BvPjQxEr84d6YcUWqKcYj30s6aLgDtbFYpUkx96K05hSPzHUD26v0VlSXz31XbcbHgOX/U6iuFXPQiPjDKs/HoDohQ5aN8uDtH9L23W917h+/+I8/zIEbh08hXicsDJLGT99D5CFYWY0CMQaDcSTSl37kcYpjqMtp5KhEycWHm9Yl8SSv98E74oxsTBHYHQrm49jtcOeOFHYz8snjoEo2L8q1U13/PWu2iLDPRoGwqfoWeP0Vnr930OSEBMl34YPH5itd8PqXsfwEAcRue4pxHcp/73TU1/H0zHpafjMLBNAH6cOLjy+rdTsvBD8ghM6jMO44cPqfW+89CoKCcdMXFtEXvwL0Qr01HUtQN6DBnromd8fimsyIo+Z4LukJAQkTpcc1Q7MzOz1uh3fYYOHYoFCxbUebtOpxOnmuhD0dYHY/v27SsLtLkK/SekkVtPT08Ouu1AGQiU1aDVnl2jkDmurvd6SzM46zcMUBWgqy4SGo3cO0vHrdNo4GExIjf9BALc/DzOpGbio2XbseS0Aj2i/PDCpO4Y1oGLuJ3r772WpKjMgPgVsxFy8lf49bkcPiP+B3ic/SHZHGm30tbZ2KpbhpMdHoeXx8W48drrsevD2RigPIHCHd/Db1z1aWIt+X2XV2JAeXE+IkODW1XmDC0V9dz85did5in+zk88iDa/vg79oFvhM+lVtEQfrdiLu8z7xeix343fYGN2JLz/moIOSEPh4X/gN/hGtASJOaXYlZgPpUKNblMehsbDA33beOGeDq/jn6N5eKigIx6z83PMHZ95tORV+5J9gAoI6D66cv9DOoZjudQXVyrWo/jQPwjsfDGaki5HXuJTiuhd7TkP6BiJQ1IbDFIch5RxBNqoXm47hmK9CVnFcmZqxwj/asdBlwu0EYDpAIqzziDQRa+LxSLhH0MfpMET49sNrvV699Ek4ULLASRnnYBG07iBAqNF7kDz0lV/HyX69MUKSyRGeMbafH8pIWeFqrU6mBVq0SEAycLfvw6y9/9wqwm6KaCiNHHqoZk6dWrl9fT35Zdfbvd+qOo5BWiuPK64uDiYTCaXpTZTT9T69evFaDz/AK0fdcRQmj9nBJxfqEjMZMM/6KJJQp50VbXbirxigWLAmO3epVHyi4pR/uVYTLcAf2ImDqUWYtrXm/HbjbHo3auvWx+bMXImqxCHZt+IiVLF0pkbD8C4/3to7t/UbKOCuxOyMVVaKdaO7VmRaRYT6IUlgZdjQMF7UOyaB1zyZIsPYMsMZnz433HM35SA2cq3Eaw6hPwhTyBswtNoDeYu+Rcf596H1Z5D4Hf1Z9jz1xYElxYAO2bB0v1SKNvJSyW1FBQMLT2Yjd+Mb2H5hDJ07HAhrugAvLT5QezLNGGKfihcX1PaMX/sOUMRCi7oEIpwv7PLP43pGSuC7tXHMvHYuLNZlk1t89FUjFMeF5d9u56tYUSjm7QuM/LWw3hmW5MeU0GpETHlJ0VHgH87ebURqyh/D2xXxWGQdBzZCfsR1e9atx1HQnYJglEAeIXA37N2oFTuHQ262ZxX/6pIjZFXasAvppH4BSNxXdfaWbZl2iCalA1jYePXKi81ynGHp6Z6lqeORrJF9p/tulBKqSL/XKmGhYJu6hwwuX6aLGulQTehtO9p06Zh4MCBIlWc5mvTcmH33HNPZWp4SkoKvvvuO/E3VTdv27YtevToIeZc0wj34sWLxcmVKOBzZW8lBZIUxNNa5Bx0M1ZbWkE5IhXZ4rJ/RLtqt5n824qgW5UvLyfmLluXzMalSECx0gu/3xSLuTtzcM3plxD5WzpKY7fBK6BlpNCyc5PRbMFDi/YjWt8PPXWnsE3ZDyNNmxFReAbGdR9AM+7lZjmu4/s2YaiiEGVKb3h2m1R5ffjQ65H7z+cI0qdDit8ARYemL+TUGM/+th9L98oVhyNVudDCgLBtb6DMxxeeI+9DS5ZZVI4uRz6Bl1KPEeFmBHSLQWz4s/j1k0O4WrEauSvfQ8j/WlbQ/feBNJQbLWgfGoAOF54dSOk0bDK+W3oQ+l3JuHNke7QExp3fYYP2F2QH3EXjx5XXX9RF/syPT8lAzqldCO5QPbhsKmcOboCHwogSdSC8Q6rPhffsfimuWO2BiKCh+LwJj+lAcj66K6mzAvBu06/Wb+gS/45A/n8wZxx163EkpaZil8e9KJM8AWM8oJEzQaz0fu1xJC8ORQhCWxc9ZmaRXJw52FsLjar2FEgDVTQvB6Qi2/Wp6hOe/C/+036ItFx6H86vvL6NKR6Tlbvhm0dD2NV/IxFVRdCtVGvkkW4OuptEq5oAe91114lAeubMmejbt68YDaYCSm3atBG3U4p31TW7KdB+4okn0Lt3b7Gm98aNG/HXX3/hyiuvbMZnwRhzVmpGulhnkygDYqvd5hEkF7DRlmW5raFzCkvQ/dTX4nJyn4fQo1d/vHzVYESqChAq5eLkgkfc9tiMkdlrTmFfcgE2akdA9eAOjH50Hj7UykXKlFs/pXziZmko4+mN4jw/dGC15XjG922L+ZZJ+Ng0FadNQS36RVxxIBk79u4DlWj4/OYBCHhkM+ZrrhO36VY9ByllD1qyX9buwljFDnHZ//K3AKUS7UK8kdHrblgkBUJSVwOZ7g1uGuufXTQyK+Gq/jHVMtcu6x0FrUqJo+lFOJSSj+aWXlCOfiWbEKvMQreg6j+hQ311uCE8Cbt190Dz2/RmO8ZNWZ54x3gd0rtPrzV/f1CXOOyVOmLz6XyYLU23RNTJ+JMVS3wqgSprY1tZguXOAc/8E249joIUOQPApPKsFXCT7DYTMMHwFpYE3u6yx8wvLkFHRTJivGyPJEueFZ+HZY1f511dloOOylQEWXKqXT+oaBU+1X6CDml/2byfQpJHyJUqbZWgm1cPcbdWFXST++67DwkJCaJiNS0hVrUg2vz580VlcqunnnoKJ0+eFPOjc3NzsWHDBkysUryBMdY65aeeFueFSn9AW31VAf8wOej2Nlb/EnKl7X9+hThFBgoUfugy8UH5cf38kTXmQ3G5e9bfKEyTj5ExVys1mDB3o/z+mnl5T0QH+SDER4duo67DJnMPqCwGWDZ+3OQNn1tiQJvC3eKyT5fqI9mUynmg/Z340HQN1mT6oCWnle9a8jFW657ANx034dKeEaJ9+017G8vNQ6CEhNy/X0NLPn7DrgXQKMzID+oDRWTvytvGXzgcKywDxeXS9Z+gpUjNL8Pk5A/EmtLX+eytdpu/lwZXdPXAR5pPEf79hYCleVco2XE6HcOUh8Vlj56159+26TYIOoURfiUJQImcjdXUa2Fvy/XCbPPl8BtbeypEr2h/eGtVKCw34WRmcZMdV3mi3FGV79XWZrCrje2HV40347uAe916HKaKaWdFXrary0cFyMeWkm97fWtHGLLi8Z/uKfxYfIfN21U+ch0YlV5e/qsxLEa5araFOhGq7VSuTaUw2143/g/dFLxhvAFlAR1gqahiL1k4vdzdWl3QzRhjZVly6nihLqJWY/iGVATdUglglEfDXS04/k9xntJ5GhS6swHEwBHjsFvdF2qFBQnL3uIXirnF0t2JmGOegWd9/8aUbmeLpl0zKA6fq24QP6Y2hMgjs01p44kMDFbKI6i+NYJuYi0yuOWU+zrEnLVs1yncZf5JBE7De8hZdKRPXCBO9HxYjBQHJ/8HpO1HS7T+eAautPwnLvuNoPTnszqG+WJbqFwDQ3H0T8BcMa+zmW04koIxyt2ianlIeO1gaGTP9rhYuQch5WeAtOpBeVNLObxVvDdK1AFAaO1528N6dMAJS7S4bEls2nnT5EhaISQJYi1sGnmvSa1SYlxoLt7TfA7NP4832XEV5ucgW/KDPqSnzdtjouMw1zwRy0rcW7lcWyhnABn9bCePR1cE3al5pVTV2CWPWVYgZ90VqwJsH5NPoDjXGOyrgF2VsiLolmp0ZEjqiqDbZLvz4D/1KHxpngKzfzvkq0JwxhIGA7gYsbtx0M0Ya3WsRU4MVPSkhqCgEGy3dMFqDHRL0J2ek49eRvkHd/Sw6gVfKC2ydLA88t05ZQn0hZkuf3x2fqPVLQ6s/x3DVYdwK/6ASn02hdtbp0bPoWPFj6mFJ5r+6/3Moe3wU5RCr/QCIs6OsFoNax8CX5TCO/4fmBK3oyW27emNi0QabJFHJDSDqqcIT754FP60DBOXS1a/i5bo6O71aKPMFK+BsmftqXS9hk1AvuQNT1MBkLITLUHWoTXifVOiCQZizi57ZDW0YwQ2WeRgrezwCjSrJPl9WxLaz+bSa92j/LAXcqp0wYnNTX54xxKSMV65HReFnl0Hu6auwRpcrVov5gO7KrBsqHr3vMJBGKifg7IJcjZYTe1DvSsrw1O9CnfxKksR58qgsx1qVUX6e+A7zZv4vfA6SEmu6TQxFcsZD+Ua26tKaCtGumFxoBOs4jeORV094w/q+ke6jWb5dVerFJgb8SJGGT5CUsioxj8+axQOuhljrY6qSP7ihH/tUZFQPw9ca3gZ08sfq/NLzhlHtv4DT4UBOcpg+LfpU+v2IRdPxVFFe3jAgBMr57r88dn5bUdCHoYV/SsuK3tdU/njyurSHnL2x6aTOWL5rqa0O1uFt4zX40zn2wCV2mZA8rDHMsxSvIfCdZ+hpdl1Jg/DC/8Wl9UDplWbk046hPpgR8xt+Mo0ET+o5LWPW9qqDtr4VeJyccxIQCsHMlWN7h6FZ013YZL+daT71f78ao6ODl3KVnG5LHakmH9eE43YHvMZJC6XH5Xf+82Blo+LKTkoLnt3vMDmNlQoKytAblfzGfl5NaXi+O34QvsRnsl5oc5tgtr3g0FSwduUDxQkuf2Y0gvLUWY0Q61UIjbMdj2HCD8PdNTmYgI2IfOAnKnhjvear0HuCPcMaVNnejllMngrylGW6ZpirOYSea62UWt7pJuWUOtQ/j3uDfyi0ftWmOSgW6GtPtKtUMtV9ZV1BN3tTSfQW3EKOksZNCq588hkadrvi/MRB92MsVZnlnEqxunfRvmA6umTxM9DDa1a/mjLqqga6kq/5sThRsNz2N75cZsjHRq1CsltrxaXpRPu+fHAzl+r9xzHeKU8QqkdeLPNOZuRXsBY4xrk/XR3k4xkWQO+zVk6fG6+DJpLbP/gVykVKIgcIS5rkps+IGnIv5u244KK+bqeNtqWjBk1Gq+bbsaXJ/2btBCVPXaeycMXZWPwjOJhBIx6wOY2gd5apEWNwyGpHdafaPo5xzUdzyhGL7O8fnNA17or2lvajxHn/jn7gLL8Zmvf/kq50Jd3B9tBtxAjVzT3z90PmJu2OJUyQ+4U0Id0q3ObrjGhOC7JBUilVPcXBTyVJc8dbxPsZbN6N1EqFbjBexc+0X4K1a6zVbhdKb/UiDBJntriF2Y76KZl1bJVchX64sx4lzyuolR+TLOHnEZek5+nFmaoUFTW+PeKqjLort7BptBUBN0W2/t8rfwt/KF7EV6Fp0RnSNXRb9bMS4b98ccfjd7x2LFj4elZu1gCY4w5Q28yI75YAUmKRVhc7Tl1lOId6qNDSn4psguLERtUI+3KCTRyuPZkIUosPfHsCDl4sKXjxbfi5jkqbDP0xKaicoT5nl3LlTFn0jTLDi0XIzHFfh3hE9nX5o/XkZ1CMOPoXHieNABZjwFhdf8Ad5VTWSXQmyzw0anRpp7/c6FdhsGcqoCvPh0oTAP8ItESUKdB4MnFUCokFEQMg3+g7R/lwzuGwNdDjexiPfYk5mFg25ZTiX3l4QzkwxfGbldB1aHuUexRnUOxNykf645n4dpB1Vd/aGpbT6TiesVJcVldz9rh3bv1RMKBcLRVZgApu4COchDelPbGZyDL3Bejfc4gMqr6sldVRXfqhfxD3ghACZC+H4humqXDyo1mhJacEGthe8XW/fp3CvfBUqk9etKSl/E74dv97BJt7pCafAYbtA8j39QOsFxoM5uBmII6ASmAOu+ke46joAxrLP2RporExWEd69yOppagDDDkuGYFCJU+T75grVJeAxULJAUOBN2FkheSpRBYauxbWZEBpbLYHnhQQS5ISNOTpuTMxV3aDchMpUEM2511rAmD7iuuaFwaFf3oPXHiBNq3bxlrKjLGzh2ZhXoxeEej2bTupS2PKH/CFN1vSNn+P6DtGy4tUlNiMCPAS4MeUX51btc2NgYlMSNhTMzHH3tTW8z6sqx125ucjwGG7eJHtUevy2xmWpDh3WKw53BHXKA6DNC8xCYIuo8kZeFS5XZoQ3uJpbbq0r9TLI6vjkU3RSKk5B1QdL8MLcH2+FxcaN4u8v98B99U53b0uTOmSyiyD6yAetlCYPongFfLCLw3nZRHrkd3Da13u1FdQrF7zWJccuJLmPdPg6q3nJnTHNKObBGdSGWaQHiGdKpzu6Htg7BW6oC2yEDJ6W3wboag+0BGOT4z3YnXR/fETTZS9636xgVhjukymJQ6PO0V2WTlqY5nFKGLQg4UfdvU7pCrOpqb6dMVKFsNfdJe+Lr5uIpTj4ol1nwtmjoDbnFc4R1F0O1dmixn6NTx+eaotPxyvGe6Dr3C/XFxUN3fyaJWDA0gF7om9V6jlzMzlN62Pyf8PDR4S/0loo25kPI6QRFo/wrhX3vdgb05V+GrzvKqBFYFIX3xuOEehIe1g63SdGrI88dVai2CjenopUzApnL3LbPKGplenp6eDovFYtfJy8t1I0uMMVZVRl4BXlPPxeOey6Coo/CIVusBD4URlqIMlzZe2sH1eEa9EDcGnRAjivW5sp9c5O33PUlNluLLzm3/7kvCKOU+cVnddUKd243sFIrdklzMqez0liY5tozT+/G59iO8nVf/GvVdInyxH/IoU8HJlpNi/u+hdNxjfBRLIh+BskvdbUsu7RWJ59QL0Td7GaRj8hzw5pZfasDo7B9wn+p3DAuuf7mjPjEBGKY9jalYi4K9jc9kdOUc2x3pwFzTBBR0va7eICvAS4sz3n2w19IBiYa6A153HuvhVHlJpx5R9dcKiQvywiLdlZhrHIejJU33e/hEWi46KOR6J4qIXvVuqwqTs8Q0efISWu4kZcsj12V+7erdLjhG7nTxtJQAZRWjwy4e6bYWS6uPIkDO/tCWpLnkcbdIPfGtaSxMkdUD46rLKQ5XHsJI5X7o89MavUQg8dSoql1v9m+DxZYLsUdtu/NFBXn+tkqthqSUR9qlJp4KcT6yK+i+9dZbG5UqfvPNN8PPr+5RIOb6tDh3zF1lrCUqyEzGzepVuN24iHKobG5j8ZZHepQlrq0eropfg3vUyzARGxvcdkKvSDyl/glf5dyKrCMbXHoc7Py07Wg81lt6o8SnTb0pq0HeWqT7y+mlliYq5mRKPSDOi/y71Bs80ZzOTD+5srnpTMuoYE4B1YrDGUiWQuF34X2Ad0i921/YORTrIKcXFxxaiZZSYO9G5So8pVmE4LL656LS3PqySLkwmSal6Ze1skrOK8Ou0lC8abkFAZc1nJGU2P4GXGF4FX/r6u8UcQf6jeVRkgydwoSuEb4NZnv2jpGLZu1Lbvzay44qOHMQWoUZZUofwL/+aQP+cT1hlhQosyjdvnScR6H8flTVk8lAYkODkCFVFBvLc00Rs6qycnIRgCJENRB0ewRGiXNPvWtqHvxmGIKXTbdD1cl2doaXVoUCyB1JpQWNW06x1Ci/dp7a6kG3rqKujaGOSvBq6exIt2QtGFnH/G/WxEH3vHnz4OtrfwLKnDlzEBJS/5cWc503lh/FpFkbsD+5eYqLMNaUSrLllK9CTUidP+6VvuHiXOvidCmfPLnIkjKq7tQ9qxAfHXr5FCFCkYeMrT+79DjY+ScptxR7c9R41PwwLPfvBJTVf2TVpGkjF3PyLk6g/zRuD1q9C+T1uVWR9Y+wEUWM3GHgl3egRawVfSS9CGkF5eLHL83ZboiXVo2iioJw2sT1NNkeze3wMTmF10I/62LkgLo+fh2GwSQp5bn1+e6vYG3L/oqAtGukr0h5bkjvGHmE+UAz/NY5lFqI37Sv4KBuOjxy5O+B+nSP8EE3xRl4HP6lyd4fpgz5/2ChX8cGU7Ojo2PRTT8ft3h9ZnOlAVcp0ZsQZpDfXz7RtWuwVEX1VxIluYiZIfu0y48lPGUF9nrcjemJT9W7nV9oLA5Y2uK4urPTWWpUbLGw3FiZrWGLWGqUOkpoXn6RXOncXq+UvY2l2hcQlC93elp5WUowWrkHPUptd2yqrXO6Ndqzgxct4LP4XOe+/2msSdAH2saTWSgpyseSL2agyPMYQvTJ4rY8TSjyg/ogePD1GNB/UIPpsIy1BqYCOf2qTFf3vEWNv1ycycvQuF7jhgq4tTGcABRAcKdB9t2n0yTgwBqEpaxwyxw1dv5Yf0LuQOofFwhfz4ZniXZuG4vjB6PRWZkiz+vuOsmtI5btzWfEXHP/dg13SEV27ItH998LU2R/fNJA50FT2HYiDXM0HyIr5AJ4KGk0quFjCu1xIUrSdfCmz5jMQ0AD6bzuVn5aXhO60L8zAjwazjTs0yEaB9e3RV/FaUiJWypTapvS8YREDFYcQffIkXZt36si6D6RnAGpJBuKBjISXOl0QjxGK/JhoS+AeuYDW3UN98Ij2pfgkWgE8qYCwR3cfowrSzpgn+Eh3N+nJ+Ru57p1DPOFARokZJeKwJCyH9zhTE4p2inSxWWviPqD7kAvDVIVdOTHUZR2EsEuXtFOVVSRuu1df+sEhUViiuENtPH1wjonv7OpOFoc0lEELwR41v25UqbyBcXBhuLGBd0dLQloo0xHmqJ6wOxXloR52neRVUxrgD9SZyE1tUpTmV7OI90tLOguKCjAkiVLsGHDBiQkJKC0tBShoaHo168fxo8fjwsuqGcJBeYW3jo1Fk/vBenjfvCz5AMG6jaruNF0BsjcieN//IMxaz7DE+O7YmKvCNGrxlhrZaGKx7S8hVfdX5xewXJ6mJ8p12XB7qn4M+iukIP4sE6252bV1HXEVJTtfw5h5gwUxO+Cf3v77seqKyg1YufpDBQc3wRd3jF4mwvhpVUiMLI9YrsPhUdMn3O+Q2P70QQxX3Nkx/pTNKvO291l6SyCbkv2SbeuD0pLAvVQygWc1HYEn33iAvG4ZSQ8MpUwWSSoK9aJbS45Rzfif6odKCmOB5Qz7LrP0E6R2LqyO8ao9sB8YhVUzRh0F9NoYt5e8YtO03aYXfehUeOFUlf0xWmUnNgAn97XoqlZTq/Hz7rXkJdIhf4angbRPdIP96r/xOOmRShZeTt8rvgATaU4Sa6lUOgZi4B6iqhZdYwIxAkpGr0UCZAyDkHh5qCbVtbYne8Js2UoXuzfcJE5Wo+aigLS/ZLzStEm2D3z5BNzijBaUVFbpYE2oN+mq30uwx95g3FnxETY9062n0eZHPyrA+V6K3Wh1U+IK6Zt5pXosUL7lJiWgOLhQECcze30aj8RdJtK8hq3xj3kY9R5Vc9GVuvkWgIaiYKC2qtgfGi6GhqFGTd7+FaOdCt4Trfb2fU9nJaWhrvuuguRkZGYOXMmSkpK0LdvX4wZMwYxMTFYs2aNWCKse/fuWLRokfuPmlXj6x8En24Xi3l+h7s9jJSJ3yF1yg841vcFHPcbhqXKSxCfU4r7F+7GTV9tRXKa/MHDWM2CHPQFLIqeGEpbbOOoSyq+wH0j6tzGL0QOujVUodNFBVnSj8lpWmnqGCjsGEkiMeEh2K2VA+2ULZxi3hj0g2LDiSzc9d1O9H9tJe5esAsX73sUk5Lex0WpX2FwwhfotOVpeMwdhYI3OqPw3zfO2fQ4qtuhO/0fVumexK3xT9h1n87hPpijuAa9y7/E6c53uPX4UlOSEKookEcBw2zVyq2ufYiPWFqs3GjBiUx5Dd/mYrIA/pny/21T3Ai7O2+6hPtiV0WRopIjK9Cc9ibmY4DymLjs3bHupQyronTujAD5+M1nmn5eN42u+uTJ63MrovrYfcwKvyioFRYYk9y/vnRV6iz5WE2h3e3avmOYD45JcoBVkrQf7paQUyK3qU6NcD85aKwPjWxf7X8Mi7UvQ/NX/cUPnZGVmY4jUhvkqUMbnGdOSsL6YZVlAE7r6y9W11jUNn5GucaLV4jtwNcq1Fduv1KDGSUVqeGOKizMlwPuepYMI0at/JvCUmr/7xVaotGrIujWelbvNFFr5XnrWtQ+fvqc/tQ8VQTeak9fGDW+yJL8oFc0/L5hTTDS3adPH9xyyy3Yvn07evbsaXObsrIyLF26FB988AGSkpLwxBP2/TBgrqGc/AG8dX7oXjVVb8BkAE/iPr0J2g2n8fm6U4hMWArd5z9h+5g5GHzhRG7+81huiQEbNq7D/vgULM2KRk6JAUqocdX+YYBCQo5PJ3j3uxoeF9wNeAaipfDUy1+c6ooUcltC/P2xwdwT5UpPXGI2VCZ/OMOQIv/Iy/PrisasLFzUbgJwfDP8E/4B8I4LjuTcdzi1EF/99jfapy/HStM1In2nfag/dmunINaSgmJNMMr0RqgLk9DTcgz+xkzs3vwbNiivxv0Xd4Ra5c5x3aa3Lzkf/cwHxDe2T6x9I6rUBuHR7ZCYkId9SfkiCHCXojQ54CvSRsDfjlFAmuo0PMKEmOS/YF69EbjpTTSXM8XAcOmQyBDz7Tra7vvRc9DHjgAS58GcV7FCQTNlWxw+k4rpijPyH3FD7b6frs1g4CBgMOgBi7nBOgGuzo7oajklMvl929mfAaSN6U3Zx/DKPyrPla5nCSpXTuOLKJOP1bue9a9rLcvl2REwrEd58gG473+f7FRGIaar/gb820NhuZgm6zZ4nyh/LQaUnEBuhvtW1zhZrMOLhldx7wUd8LQd76+YQHmENilXrjTuKjnFekRAzlTzDY1rMIP0Hd1cTMBmlG+fCe8L/+fw4xbnydOCjFBDU89no7ki6DY3YsCDBkq8K4Jujxoj3VoPufi1VqS/1nisKi+3RqXA3nZ34sZjI3FNaAwutvvRmduC7kOHDok08vpQdfMbbrhBnLKyeK23JldPUEQ9n49c0hlX9IlC+ZczEWrMh/+qm7E68UlcdMPTUJ5jP1BZ/SiV7Pc/f0efk5/hcuUBxFg6Ya5BTqmkIjwZCEQkchFcfALY8Cb0W2dDe+mrUPS/pdlTeGn009eYLX4gewXH1LldiK8WFxqfE5cPaIJdsg6pV95x+Rgi5MrL9mp3wVQYjr2CaOMZlKYehleUfSMl5yMa0f109SFIGz7EO6rfoFGbEdp5CAaMn4ZO4fQqXlQrTW7riVTs+ns+NmVqsXXVCWw6nYNPr++DMPrNoT03lq/ccioHU5TySJuy/Si770cp5lTVem9SPq4aUPf/F2ftLgrGA4YHcUu/aAy299iCgfsyfoDhlCdgea1JA76q4vONuF8hL2mkbGff3GKrtt0GYPjxjxEb2Rk/NeNnY2b8QZigQrkuFD7+9r/OHTt2Rs+dX6NDSCR+b+L2P5CUj4uUFVWto/vbfb+wdj2hP6aGzlwK5J8BgupfhsoVTmeVoGvF+teedgbdxBDcDUgDNHYUXnNWevJpvKT5HuZCeh0ftOs+uvCuQCrgV3pGzhJyQ0G1pDw5eI6tCKYbEuuvwSTlVnQ9tQYwv+WyY0ovLEe0Qp4vrQpo+P+Il1oBX3MZUvJSnXrcskI5HipW+SGwns+IHVE345GU0XiobRd0s3ff5eUIVFTMzdZVD+i1FenlaloarMZra7GY0U2RAiNUUCsU0FR0XNFUH+ZedkVbDQXczm7PmkbbUB90eOxfHAy4WCwrcfHJt7Dl45tQXl7/mp7s3ECp458u34X1H0zD/afvwQjlAZihRFB4LP68dxB2PTca7ww2oeB/u/HLRavxrsfDOGaJgc5YAMWfD8H0x8NAM8/5KSw34R79wxirfwc+vSbVW13Yu2IJjezi2j29jnjMcA+GlX8C5YBbGnW/zm1isEI9WqxFuz2xxCXHci4qNAAPzF2FCzZNx2PqX8R8s/J2Y3H95EsrAm7bo40XdInGAw8/h+uvuVF0MG6Pz8VfnzwMw1dj3V61u6mcPnkEbZSZsCjUQBv7ZzpS4am7VX/i2kP3AfHuW7Zub64KyyzDoOl3vd33CW3bA2WSFlpLGZBb/xJX7qQtOCXSP0t1YXYVyKpqWIcQpCAUe5MLYKxjaZ6m8GdWGHrq5+LUlF8bdb8+sQEohpeo3t7Ux5+SeBLBiiKYafg4vIfd9+sSFYyTUsWc3Ay5I8rdTmfkoWPF+teNOVaPaDkz1Lc0CTC6duS2ptK0isrlnjF2jXKTkNiOKJc0UEtGuQPDDRJz5ZHb2CD7lh2ODvLBB5o5uCL7K6Cwos1dICuvULzfBD95+ll9yjzkOMZUUDGdzUHGIvk7qExdf7q8t5eXGPSgwmv2Ki+tMjWnxii6RldlWTRT9d/4alMJ/tY9i/90T0GtVFTW1GjOz7DzRaOHOC11LH1A1ycmyj2BrOXSePqh58O/YV/XR8UajcMLl+PQh1NQXFzY3IfG3OhAcgHe+PA9XL3tKtyo+k9cl93xaigf2o129y9BrzZh8PPUQKcCOoT54JqLBuDxp2Zg0yVL8b75elgkBdR7vkX5Ufm+zSWjsByF8EamRzt4+IXYMS9LQlZBqUsKeWWXmpCGYMTFNK7KLxWH2df/VbxqmoalCbxgRF0VbpcdTMHMzAcwRHkURrU3MPULeNzyi12BELXxFf2i8ccDw9EjyILJxn+gzToIwzeTW33gTaP/nslyZerysD6Azv68jS4RvuipjEcv4z5IKbvcVsQro1BOcWwfan8SbZeoAByT5P9LUppcpKqp0Y/MNno5WDHH0rSaxo1W09x0Pw95bvrRtIof9E0svaBctD91yHTq3LgsGhp99NWpRYfsySaeW29K2SvOC307Apr6102u+Z6mOcKkrKK4mbudyczHJ6ap2BU4gRa4tvt+MbFtUCB5QQkJyHX9ElhVKXPlbA1joP0F29qH+iFeqqiN4objo8y0u/M/xBrto+iabV/dg9hgbyRJFQN3ea7rjMvOL8Q803hs977IrulyJs+KYyh2Lug2F8sp7QZNxfrjdfD3lDtKCsvsr0ui15chVQpCHvwAVfUVLXQVI93EZKje4SPRVBK6XlKKTNf2WauwSDsTl2bNq/UY8dkl+Gl7ovgeYk0YdBcWFuLaa6+Ft7c3wsPD8fLLL8Nsll84Qinl7dq5P82HuYBCgT7Xv4KTY74SIw0D9Ntx5uNLkV/UvAVtmOtR+u2nq0/gwzmf4ZWS18Wa0cU+bYBblyHk5rlQ1JOaR6OI0y/shBG3v4GH8QQeNtyHh3aFin02Z9BN7CkU87D0A47pbkPAzo+cftxT2fL/jQg/DzHfq7HG95B/2Kw6msm9yTUcTCnAm1/Ox1d4FdGKHBgC2kNz91qgz/WND4JCfTDvnrF42vctZEoB0OYcgfH7q1t0YcCGHE4rRC/LEXHZs8PIRgeFx60BSrJ7ijnFZ5WIdNCpXvvhD/szOTqF+eKw1FZcLm3iolhWR9KKECQVwAQlvLtUn7pgD/qMHB1lxlea9xH90xin1/R1dL4/6RzuKzJ8Gnv840JzxQ/u0J8vQ1OhYMwjV+7skMJt1wmqC2WzpHnIgWVpkhy4u9uxXIsoPLWn/xuNmkPeJcIPLxtvw/2WpyA1Iu3fkfb0K04Ql9Vhne2+X/tQbyRK8iogZZly0O5KWcV6tEEq2ikzEOhtX5EumtOdIskd6voc142+J5dpMMN0K/7s9Lp93ys+8nrh6jLnpstKpXJKu0lXf9AdiSx8opmFq8/MtHvfRapAXKD/FFf6fF/rOel0WrxovA1PG++qVSBNkiqC7oqlEWnZQ+rsjtDL76GqRr+3Fs/8dgB/HahYbo05xe5PjxdffBH79u3D999/j9dffx3ffvstLr/8chgMhmr/8Vnr0eXCa5Ay5QcUwRNryjri+rm7kV3s/BIJrGUoKjfingW78N6K41hr7oVDXoNRNvA++Dy8DWjE3MUh7YNx2/T78bfiQqw4nIFZq0+gueRnJOJV9Te4HX80uK1Gq4NOYYSlxPm1uvOPb8Fnmo9wn6djVYr7xQUi3EuJXoa9OLrtX6eP51yx7XQOrv9yK3Tl2fBS6KGPGgzt3auBUPt/ONYU5ueBN/93FR7WvYo8yQea9D2wLLlHLrrUClG6/KCKytSKRqSWE1oSKM9XbktL2kG3FcR6SfMdPrS8BeSesvt+nloVMrzk5c/0TRQ81bQ7KR8vmO7AfTFLoexznUP76NQmFqOUexFENTDyav9odbdjCUn4W/s0XsUch6r3x0RGih/cQXn73Z4CbUVLMS0p7y8CUu9BNzb6/oWhA/GbeQSO+DbNMrX0HieUBdYYtAzXH9II/GXoiyyD+ypD55UaRYFJ4htt74xgwNdDgyyNnGpdnOr673UqhhajkINWtZ1z72nEN1slB91FGQnN0mFPVH5yR7mH3rlMqZOIw7emscgKr/83l7/WgimqrehTKmc12aPUIP9/99TUrsegVSnxvXkcFplHQ6+okUlSMdItpnbQ94paHmVXSKZavyEvU27Ci+rvUXjC/uNiLgi6qTL5F198gauvvhp33nkndu3ahezsbEyZMgV6vRyo8frPrU/HgeOQedNafOsxDUfTizBt7nbkl7pmDixrPvQj4aFZP2H14RTx4fvmVX3Q4/G/4Tn5TZpj0Oj9DWgTiNemyiMSv67ajPx519E3IpqaMes0pqn/wyVlfze4rcVDXp5DWeZ80G1K3o1Jqu0YLDk2WkjLs7wQuh4LtW/Ac+uHTh/PuTLCfce3O0V6cm6bCVjX/hkob17skkr54X4eeO2uK/EonoBBUkF55Hdgy6dojbafzsEbphuxN/YWIG5Io++viJDnoHoWnqI8Q5cfX2J6NsIV8mgrAhuX7WYMkyuxezRBoSlbdp+Rj7tn24hacyLt1ad9BA5IFVMgkpp+6a2ShF3opkxCN/1eh4pOxbXpIJYLUtIiwenu6ZipieaQn5BisCFoKnRdLmn0/XVtBuEx431Ypmp4PWpXLDWlzT6Mtoo0dAiyPw3e2ullrcZ9OrvErcuFtVPIS8FqGjHSTYp92iLJEoo8k33zwBsjJSsfEahYAquO9altKfOU1wfR57huyipVEQ9EIcIqlgNriC5Q7ozwMeY4lcGyTeqGl023I7vLDfVu5+kr/17xkkrt7jwrN8rBs1dF/ZqaWSxUmZzoTWezkqullyvk+ykragCoLNXnk+9IyMU41S7cof4bsWVythVroqCbAuw2beQ0NRIcHIyVK1eiqKgIEydORGlp603fO9916NQVi+4ehhAfHeLTsrB21v9QlO98oMKaBy0P9MHsz/BpyeP40Gs+fr57KK4bFOd0FdBrB8Ziar9ovK+Zg4Az/8D8j1wdvCkZ8+XefKrS2xCFT7A4V5U7v063Kk8ewTMGdHR4HwH9pojzNoW7IJUX4Hx2OqsYr839Gb76DAxtH4Svp/VHsX8XhzqE6tIh1Ac3XnsDXjHdJv62/DfD7fMqXY2mcuw4kyfWrbWMnelQh0RYdAcUSl5Q0ShGtlyB35UK0+X/G3q1T6OPzze2l6gX4WHIBSrSMJsKZebtPiN/NvSPqz/1sz59YwOw1yJ/LpQm7EBTPwfPbDlQNoXbX1W7qp4xAThokTtLLGnuX0+aHEmTa8h0i5CXSWosmtdNaKDA3VLyyvCy8mus1T2OmPTG1zTpHiRhonIrlHu+g7skZuUhSlExItvIYoDHY67CSMPHWBlxl8uPKz89HkqFBAONtHrXX4OlKqNPRaGzIucqh1d1cfYC7PG4B8NO2TfdzCc4Gvst7bBf3ZvW8XL4ca2DWIFe1edc1+TpJ/9eEez8faBL340l2pdwf/Esm7cPUp/Chcp9MBbX+A0kyVlfpooFrBRq+diUNUa6N5/MQY5UUUOklddGaXVBd2xsLI4cqd7T4evrixUrVog1uqdOneqO42NNhOZC/nDnEMzy+BJXlC9B6meTUFrkfLDCmtbmU9n47avX8bHlLXgr9BgfY0LfyMb1ztfnxcndMUs9XfxQVh36FUjeiaakKJJ7883eFcVf6qGu+JLXGZx/H/uVyGlu2nA5HdYRA/oPxmkpEhqYkLbrL5yv0grK8MxXv+Mz8wws8XoNX18eDp2N9DhXGNcjAj4X3ImfTBfhJdyHbG1F1eNWlLFCqaOUPtgzqv7qt3XpHOmH41LFfNJsOU3dpSoqj+t94ho9B79DdDgmGd7AVQG/AF7ySE9TSc4rwxPls/CH9gX01zv+OSZSdH3lAmb6RPcUq6tLZpEeHU1yWrB3u0EO7aNDqDdOKuRRyOImKkyWmhSPq5TrMdLXsaCqW6Qv1DDBlHEEllz3VN22OpVVhA4K+TiVDkx76eVbitnaWehz6B23zfk/k6vHlYYZWBjzMuAjz9G2V1yQPBKfmOP6gTN9ttzJWeQZ1ajPBqWf/DmtK3HdPGIvfVa1EeyGBAcG4DLD63hY+Sytx+Xw42pK0hGEQgR41B9u+Xt7oViq+K1WXpE51ABlaRb6KU+ijcl2Gv5bik/xnfZtIFOun1DJYqqeXl4x0l0z6B65/yncql4pLqvLeSCuSYPucePGYd682pXtfHx88O+//8LDw3U/7FnzoN7j9le+hAJ4o4vxCBI/mYzyEq5q3lqsOJiGrfOfwwzll1ArLDD2uh7aWxa7dPQwyFuLqyZPwq/mC8XfpuVPN2nxIE1ZRUq7b8NBt8ZPHg33Mtn3BVZfemG4MVlcDox1fI1tKnJ0zF+e11W873ecj2gO2oNz1+DN8lfF8i0hoeHw8a/Sw+8Gj4/vgnnBj2NB2VC8uLRp0mddZVt8Lm5WrcSt4aeglRwbbekS7otTlihkS/6w6F2f4qotrkgBDZSLojVGt0g/UYn6UJapyQsM0trlg5VH0Vt5GlqNc1lA6lh5nWmfvMMOzat21OHUQnRXyD+4NdF9HdqHWqVEEWWZ0AhjatP8/9Ck7sD72s8xIeEth+7fNtgbL2h/xDLl4yja8BncKSUlCQGKEligAILtrwxu5RvVSVSJ1llKgYpOY1dLyNVjn9QReR0ua3THV5tgr2pLe7mSIl/+bDD6NK6InDmiD+4wPI55ES+65DgovTrQLGfSeAfbdywhPtrKJUcdrVdF93vD8DZ2e9yDyKz6l2yk1WPotzcxldiX9WOu+Dw3qWz/xjMq5OdgrFGrgdYMn226DEvUl4q/lTZGuql+zijD2WPW6nkQrkmD7hkzZuCVV16xeRuNeP/3339YvXq1Sw6KNZ+OvS9A6mU/iXTEroaDiJ81BYYyXlu4pVu8MwmnFz2Fx1SLxN+m4Y9Dc+XnQMWHqStd0TcaS4Omo0TSQZ26Ezi5Ck3Fs6KoidaO3movf3mk28dc4FTHQHJ2HqIh95IHt7F/jVZbVN0ni/OorI3NvuZ5c6RKP7loFx7Jew0dlGkw+URBffMvgIdjI7j20qlV+OC6PmJe/d8H07F238lWk2a+91QKXlF/i2eynwNKHKuiSyNZryruxED9HMTHXenS46NCO6FGeTRKF9b4gCQ6wBPeWhUMZotYmqYpnYw/LdY+J1L0AKf2FdGuB4okT2gsevdkE9TheHIG2ioqOiIj5PnxjlBVrD3tnX/c7Z2o1LniVySPzqsq6g041FHgI6fEG9NrjOK5WEmKnOFZqIt0qAM7Lizw7BJYOe4pQnomp6SyM8KRz4dPNbPwaep1wBnXFstKK1Njr6UDzI2sUB8cGi6m1OzS2zcq3ZDMQj3CFHLQ6BVsX7YTTbck9NlUWOrYd3WJwYwAyFMgfALqnxJHSw8WSXIHiL1ZplLFqhxmle1BT5NCHsG2GKqv012kCsI7puuxUHet+Fuh9hC/5/Q4O68/6eCmavfxMnLQ3aRBd2BgIHr0qPsDkka8R40a5ZKDYs2rW/8LkThxAYolT1Gc5cQnV8Bc4z8tazm+2RiP3KVP4R6VXNHbfMmrUI99qdE93vaiAh13TbwAC81yERvD2nfRFGjE2d+UbXdvtU9QJLZbumCrsp9TAW7WmaNQKSSUwrOyoqmj+gy9BNmSH3ykYuQdXYvzySerTmD4sbcwQnUIZrUX1DctsitjwRV6RPnjzhHtMFBxFL2WjIH5l9tbfDVzGiUpj98mslb0XpFAQOPWh6/6/7VtmDxn+XRWicurE8cp5MBVF9reoWMbGVqC19RzoVv+MJqS4Yw8/zpDHe10x0+vmEBss3TFbnSD1EQVwEnhmX1izmypNrhyiSNHBLTpgWQpBCe1XQGDezs/KEDsiCRx2Sva8U5MKVhO9dbmuXk1jYpAWe/f+Pe3ddm+05IcPFqyXF9TQTxG9hrcpvoHnZVyRlZjxAZ5wR/FCEY+TBXp4K76/Pq+ZBCuMLwKy+jGjVhH+sudG2kF5S6rXB5eEXQr/OwL5D00Krzp8S0O6O6AYcschx43r8SAQIVc+V5XkXlXX0dSmUJ+3mV2ZphWBt3qs2tyV2WqGOk21fj9bk0qUivl34hFsaPRQz8PT3q9VrmNoUw+blpOkfiancsYZDL7Fxxk55WeQ8bgxNh5KJV06FG6HXs+u6VZ12dmtr/UPlx5HDOXHcYGSy+RSmSZ9BFUIx5ye3Nd1CUUW8KuF5WhtSlbgTNb3P6YtJxdWEUlVJ+QhoPuAP8AXGt4Gf8zPubUiH9+ZpJ4ntnaxs1LsyU8wBt7POQK1Kl7zp+lw/45mI6UtV/iRvVqSFBAdc03QGTvJj2Ghy/pBL1fW2gkPVRpe4E936MloznHHcoOiMuqdsOd2pd1BCy+Yr15V0nKK8U7puvwnu9TQPvRDu2jU6gnblavQlTissqlbJqiA883V27bPC/HgqmaU7PuMT+FK8tfRIq341NQGiszOxunLJEoC27cSGJNnaKCMUI/C/crnwd0jVsWq7FOZBSjs0IuiKkMc7ytPKPk+/qUpwEVwYc7eBfKgagqTE7Bb6yoAA8kQK7GXZLq+grQlG0y3rgKr2i+Q2zh7kbfP9RHhxSF3PlZlOa6DoycEgMMJov4yozwb9z000h/D1EAbFLhz5AynF/ZIDsnBz6KisCzEXPevTRK+CrKUF4gdyw2Vl5xKfwUFe9Nz4ZrVjyuewXtyxcgLdLOiv5Ged+S2nb7mpXyyLXZVH0pYKVZjzaK9MqOCGuVc1OVKT6Wiv9TWUq5s8BfKuBloV2Ag25Wp34jJuDQhXOQLgViZuYIEdzxWuwtA3WAzPjzMD5eJX9JDhpzDdSP7oNy0O1N8vi0POBVFw3G1+ZJmKW4CeVBXd3+mNRbPdUwE9drPoYqpuF00EBv+Qun3GhBmcHxH/O7VH3QVf8tfujimiWnMnrejYn6N/ChybF1gVsbSht+6pc9mK76R/ytuPgFoMuEJj8OmlP/v4nD8JHpavG35b+ZgN791Y+dmc89sGJ9bnXbxq3PXVP7YC98pXkPV2+YBBQ0fjSsLkm5pTgmxSEhcgJg5zq8NYXHdRGdu9QZ0lRp/1SgrrvlpLhc7uvYcdccFesc7lu5FF5T1Uf4Nb8Txhjeh+UGeVqRo2jev3Ver3XtX3c5mZaHthXLWyHM8e+N6OhY5Eo+UEJyW9p2QakRUSZ5VN43xrEOAhrBLPSSC9UZMu1fx95eZ3JK0aZiioFHWEeHvsuLvOQsGkOW644vNZ8yPiSE+3pAo1I2esnHW1Ur8JRqIUpOVk9zdkRxtvyZV6b0blSnkkEnB8qmIseC7uJ8OTNP1APwbHiFBI2nLyxQorjc1LigW2N7WoGlIr3cbKheDyS67BjW6R7DzJJXK9+jYnfmswNrFqPcSZGua4fx+rcwVv8uCsuarl7FuYqDblavQWOuwpZJ/2G/1AHzNyeIkVXWvGhO3LOLtqH3jqfQTpGGGZf1wINjOtmdNuUq43uEY6Hv7figbBIWH3Z/wb30gnIUwhtl/h3t+uL00akrenAl5JaUOxVY0BdhaJhrUqEHDByCw1JbbDyV7VRnQGtA64je98NuFOoteDPiI5gvfgkY8VizHc+kXpHYE3E1TlsioCzLBrY6ljbYFHadzkR/ZUUwEedc0N0uzEes4xtE86+zXReg0P8Na4qqo7pEBeK4VDHPMuMQmsK+xDz0UsoBfoG380E36RUtp6gfTUxtkhH7Y+lFYvo1zT0N9XOuWGawj07sh/Z3IsW9SwMVphyBRmGGXuUDVFSpdkSncF+crHjfSFnumUd/KrsY35vH4mvVtdC1dzzbxBwoZ1Oo813fqZSYU1w5xcPRji+jn7wcsCpfXonAFdJyi3BINx2/mR9o9HKAtL55nkYeYS3Ndn6t7oxSYJ5pPPaHTGrU/SyecpFPRYljlbvLKkbISxXetBh2g9v7eKgrsxfsYbAoRP0li9an3pFuqeaSZ5L8+WRWyI/nVXga8zVv4wX9h2e3qZgmY9T4IUXbHlkIQE5J9RFz1ngcdLMGTR3cETMvl+derVvzD/Z883CTVqxm1YOYx75bjyuPPIwrVRvxR/BnuHWoY3M9nUW9o7cPl7/kF2xNdHsWREaRvrIX3N4e/Nm6T3FMdxss+39xKs2XxAS6pgp81whfUUCKRuA3nTy3176c8echsSZvsLcW79w8AqoLH6eJvM12PDSH+MmJPfGhdbR706wmXx/aXrmnd4ll/+hHD0K7OZ1efqpiXqkrg+6yjJNiLulAyfFgmUaIj1rkkcDylCZaJzoxA1st3ZCji0Ghp/zYzuoZ449fta/goe1jmqTz4HBqARSwoHuUY2td1zQlMBHbdfchbsnlcCdFxfJF5QEdnZqu0ybYG6crgu7iZPe096nMYqyx9MOayDuAMMf/D0oRffE/w6P4oa1j1drrk5V2Bh4Ko7z8k79jvwXUwfLKAx4lrlsXOy8zWXx+hZkzAY+GR3lrKvOQO7lNeXKmgTNO6AMww3Qr9vV8pnF39JYDf5WDy2XpC+Til6Vq+2pGjDZvwSeaWQg9/pNd2//mNw299V/jSPdHbN6+0edSzDROQ5ZfL5tBt0UhdwRoLWW4SLUP/aWzqxdYa1NIKg+xag3JLXF8vXIm46Cb2eWWYW3x4phwfK99C/0S5+Pwdxx4N7VivQkPff0f7ox/FEOUR2HU+ML32s/t6kF1l6v6R8NLbUHHjH9Q8N3Nbh3hMacexKvqbzDZ8Lfd99GoVNApjJVffo64M/ttUd21A+R5iM6izoBrOljwvmY24pbfhHPVb7uTUbDzF9yk+g8fX9fX7s4Sd7ugQwhKOk7BIUsbKA1FwMYP0NJkFpUjsrAiAI0b4nRHRbsQClDkeaXGDNdVew7O3S3mkg4487XD+/D31CDdQx4JLE2S51m72640Pe43PoL14/6GpWI0yBUj3WWSVqQ7S6l74G5pCUdxQHcnZuY/65JO8KCIWIQp8uFbdNpty57RnNHfCjvhVsPTMIx4yql90WjoXp8ReN94NU4FXAB3OFVReLBDqHPz3KMiI7DCMgg7SuQVNVypPEOeJlFEQWrFesuN5RshrzzgY8qtHOF0VlmWvJRdkS7Moc8vo4/cSagodP57N6OwcR32ViofOejWGRzrmM00+2K+aRwOBdo3R7stkjFFtRU+Ofvs2r7MKP/e8tLa/g14wHckvjFPQI539ZUlFJVBtzzSrdLIQbUaZ3+/bYyajo7l3+HfuEdxnXI1XlJ/B30TLSl4LnNp0K1UKnHxxRdj165drtwtayHuGDsQW9rLRbq6x3+Loz8919yHdN6gHsYHPl+GJ9IeRR/laRh1QdDcvkz+Qd6MAry0GN8tFDM18xEQvww47b6K3JqcI5im/g/9i9bYfR+9Vu5hNxc7NqJM6d/DLbswWbUVEX6u+XFOhnWNwVTlJnQu3gFLvuvm2LakedxfLP0Pb2u+wuuabzCivGVVan9qYne8Z5aXS8nJymhxmTs74vPwrXkc7vP7FJoxL7jk/2m6Rh7R1bso6Ka6Et6l8ntXHexcirYxWB5F1GQ7XzSpIVTc6UiaPJe/V4zrlqujDJZDqOg8SNgJdzOnHRDFoWgNaVesVBEe21leBpLWg891/dxjkpBTimyzN7ar+iO4z0Sn95cXNQqfmK/EbotcydzVilKPYpRyH3r5OlfRvV1FIUN6/q4mVdRB0PvKKeKOCA8LwxFLLPapewPlrpkqZv1e03s5Nu1N6S8XS9WVyksSOsOYn4ogFCLct3EFVbUB4U4tlxWPKLxiug27Otxv1/aSVs5aEZ3BdiitmJ5GNSVs0aoVlVMSq1JYqgfd6opCs6oq63RTVqUJamh0nhhjWofp6n+gyHT/5/O5zqVB9zfffCOWDXvoIfdXT2bNY9wtz2B5tLy0S9djs3F88Ux+KdwsraAMD89Zgpk5T6CzMgUGrwho7vwXiOrbItr+6iEdscw8VFw2OZHG3RBFsVwsxuxt/9xqk4dcCMXi4Jys1IyMyiU/fMOdr3Js1a9bZ+yD/EMxZetinEsoqHn8x+14Bx+Lyq9S7DCgx1S0JF0j/BDUezIu1r+HJwx3uW15PUdtj8+BBCXCOvZ32f9zQ4AcGCtdVKwsq1iPKEn+P+kZ3vgCTlV5xMjpjyaTkda3gTsdTS9EgDkHAZ5qxLloyoj1h2+On1xsy5Ts3oEH6vDwyZc7T5SRjq/PXVWXSH8clyrSk92UHn8yUw4mOoX7iKkezuoYJo9An8xybVV+qw4ZK/Ct9m1cmPy5U/tpF+qN/orjmJD/I0wnXdsB6Vkkz3lWOjifm8QEeWOC4W1MM71AX3QuOS5VkTxCbXGw1owuWH4v+ugzne4UvaPkS+z2uAedEhY26n5eARHYa2mPo+puDh1DbsX63gFednbYe8hBt8rOoPumgi/xneZNROZut3l7uDkDAxTHoC1OqTe93NZId7lJvqzTqKDXyr+jzEWOZwwyNwTdt912G15++WVs2uR8tUHWMlFq7KV3zMCysP+JvzsfeB+nlr3f3Id1zqIRw6vnbMFNBV8hTpkFg39baO9aAYS6p2ffEcPaB2OLl7xckHT4D5elp9WkK5OLkqj85TRZe0gegeJc6eCcrOwUOXWvQOEH6OQKv65AqZEJoRVtdmgpziUf/nccEzO+EBkZFo9AKK7+GlDJPeotyQNjOiEBUVhzLKvJKk43pnI5Gdyu4WVm7KUN7STOPcvSgYrKtM4WUbNWTVY5OdIdFxuH3uVf4a6g+U4t72ePfUl5WKV7AhsVd0BR4HyRpqpU0f3FuU/+MaDGMj2udCa3FB0tcvqud5xrOmVobv2xiqC7NNk9af6n03LwmPpnXK3b5pKpSJ3CfBGjyITfmVVAiWvrY9DoYFDZGXHZM9K5mgpUwXuyZgeeUv+EkgN/uugI5dHI90svxRX6mdBccK/D+6EaI6Sw3IRCO4t4NcSrTB6h1gQ6VjPBN0weudfSqgZljo00W6flhUjy9793SOPmvPsGheMKw2t4Uvu8Qx2z5qJMBKMAQR72hVrKiqBbbbSvE6mj8QQuVB2At9l2dsIl+T9jsW4GOqQssZ1eXjG1RlXxmauBubI2z8D0RfhY8yk6FW2tHLyQXPx/7HzEc7pZ4980SgUuvftt/BU4TfzdYedMHF37M7ekix1ILsDVczYjJb8Ms/0eRmnnqdDeuQIIdDyNzF3vh7i+FyNZCoHGVAIcd8/6014GuZdVG2h/xVuFtzyHTlPu2Jd2SYY8KpincU3l8qp8B14jzmOK9kAqdF0Bm+a0+VQ2Tmz4GXeq5Xn3yitmAxVpgi0NzXOe3FsehVm0cj3QQjo/8ksNaJu1Ch9oZmO4eYfL9hsWEY0zljCc8ezm1I9YK1peqrJqcmA7p9e5ppUJjmVQRW73pvqnnTogMjB0MDhVPduW6HZdxDJWIk3TjcXUDqcWoqtC7jBQuWik21OrQqanPPezLNk9Be0Kk4/gIfVSXJfxIaBQumSk+zPNLDyT/zKQuAWuRO/vdgr5c9k3uqvT35HF3vL3tjFL7sh1heS8UhRK3jil7Qr/OMfXavfWqSuLZaXkOpdKb+0MCDTJnw3eYXKRtsaKCPIXc/9v9/jAqQ5vWvUkHPLnnUcjfjtYq/qT7GLHOtAuz/kauzzuRd/EeXZtr/aSp7toTfYF3VpJ7jxVe9heMkxSVnRgUgZRFWnqaFHN/ZC3nKGo1sjBtwYmmC3y52+b4n24XLUZIfoUSBW/o9QODl6wsxr1qZeWloYFCxZg+fLlMNRY962kpAQzZ7o/1Xj27Nlo164dPDw8MGDAAGzYsKHe7detWye2o+3bt2+Pzz93Lk2Ina1cfcl9H+Ev32ux3twL167SYeMJ7gVzFapq/eiXfyCnxIAeUX6Ye884eN0432WpX642pW80/jQPc1uKOX2JB1nkD3yfEPuDOI2v/GWhM+Y79LjGHHk0qczbtT/OyQX9+2KP1FkUXkrdZF+10paMgsW3flqFd9VfyFcMuRfo6vy8TXe6f3RHdFYk4ZX4abAsuQdwcBqCK22Pz8XFij1idQL/7N0u22/bEB+MMnyER33fA/zszxapS1pWLkIVFRkCgY79sLZqH+IDtVKBonITUgucH4WvV4rcpsWB3QGlazMwesUE4IBFnoYiVTyOO5xKSkUbZUWHR7jjwVZNxmA5uNTkuK7YXjVZR8RZWUAnl0zpaB/qjZMVVflLU1w73/RURhE6VATditAuTu/PUrlsmOuW5UrIlueIxwV7iSxEZ1zvtQPbdPch8O97nD4uCnSpcCMtNesZIWfYNFZkgCfWWfpgY1Fk5dxjR2QWlCFcUfH938jPPVp1g9Dnkt7Y+OKCHkb581HtIy891hCNl1yDRmexr+NDZ5E/K7Uetgv9SdYikTWWDDut6SyquW8NnCL+VmnkzgUTlDCZ5OepMssdDQqtF5QVBeW0+pa50sc5GXTv2LED3bt3x/3334+rr74aPXv2xKFDZ3tyi4uLMWPGDLjTokWL8Mgjj+D555/Hnj17MHLkSEyYMAGJibZTxOLj4zFx4kSxHW3/3HPPifnmixefW3Mom4tOo8aYB+fgu3Zvo9CoxvRvd2Dt0ZZXlKi1+Wt/GhbN/wR/KR7FK2Eb8dP/hiLUV/5QbKm6R/phr6+cLo2Tq1yeYp5RWI6wit5qzyD7A2B1QDS2W7rgmMqxL35VgbxcidnP9cuyeWnVOBk2TlyWDrbuzyQanXxm8QG0K9kLP0UpzBF9gLHu/T5wBRphbdN1AA5JbaE0lQHbKzoMmtHW07kYqKxYdzjuApeO7JOEbOdHskhphlxsq1ztB3g2fkmgmtMtLg1MwwLN69D8eivcpdRgQliRHJxp4wa6fP9UTG21NAC/mC5EjodrliKzpThJHoku8QgHvFw3BcEzpje2WbrigOcQl3+PU+Vy/yL5PaOKkJcgdcVnaJZO7vApTXFtZkF6SoIoVGemn8lOZnIQbbj8HeRTluKy6vApGel4Uf09blGtcPr18vX2EcGpygVTLig7703TTXjU7wMo2l/k0D7CfHWiX8ZolsTgg6PycjPECiaCT3ijV1aYpfkUB3XTUban8YMJ3mY56Pbwk4PWhmi95c9RTzuCbvrO9YAcGGs866iub61mb6nefuaKt4pGJYeAau9gtC3/AZ3138MoyZ036oqAXqn1hNYvTD6uKgXlDm75B8deHYhZ772Mj/874fYMpfMu6KaA9corr0ReXh4yMjIwduxYUTSNgtmm8sEHH+COO+7AnXfeiW7duuGjjz5CbGws5syZY3N7GtWOi4sT29H2dL/p06fjvffea7JjPtd5aNX47JahGNs9HAaTGad/eBQJi57kwNtB32+Ox96fX8Us9Ufii2JaZCJ8dS1vPmxN1Mvepd8IJFpCcVrdAShKd+n+0/LLxHI24rEa0VutjeiGaw0v4y3tAw49rqG8FAZJBXWQe1L6QwZfh2OWGCwv7wmL2X3Lrbnboh1J+OdQOpYrRuLM5EVQXTMPULfsjiKrBy7uhDmmy8Rl89YvAL19RWzc5cSp42ivTIcEBRA72GX7bRviJc7zSo3IczBdsqq9pYGYqp+BPYPeccHRATEhvhihOgS/9C1u+/44mFKI3ko58PNu57q2rVpMbXvoVXjSdA92KnvDXU5ll2OVuR9KY0e5dL9tYuNwneElvKO6w+XFBSlduyPkTkzvGNeNzhsC5SJ+ipzjcKXSVHlUvtAzxiV1BkKi2kEvaaCmqQcVnbnOKks7jjvUf2NS/g9Ov17qYPk7zqs0xSVBN4kOlD9zHEEB4YXeSbhH9QeK9zs+D744S66iXqzyb/R3Ek0L8FRLovOlNE+uX2EvvckMP0n+LvEKkIPWhqiC2qJ3+ZeY4v1Dg9sazJbKoFtXR9AtqeT3rcJcPb1cYy5DKPLgJcmvk0ZNBdXk94+pIiJXW+R9K7Ve8Kyo4u5rOht0mzZ8jC7mE1Dmx4s6Lgu22ddZI0kSikpKK0fUzzd2/5qnZcA+++wzsSyYr6+vuNymTRuMGTMG//77rwhu3YnS2ekYnnmm+uL248aNw+bNm23eZ8uWLeL2qsaPH4+5c+fCaDRCUzGPoSq9Xi9OVoWFcoEC2p5OTcH6OE31eK7oufn42l745LuDmJ76F3AUiP+uBDHXf9isa0i3JlSN9v0VRxG17VU8r14hrjMNuBPSuNdhaaIPJ2ffd+O6hWLs6ncBiwe26yLg5cL3b3JuCe7Sf4axMWa87RVBB2nX/fx0cr9iTrHBoef1omk6HtDfiKW9B7jl/+OgHp0xZNn7KCkxo09CLvrHOTdi2BxOZ5Vgxp/yKNOjl3REbJ92EC3ViPZqzs+87hHeKG43HqeSfkYHfRrMO76BZch9aA4FZUYEZW2nyXUwhvaEQu3dqHasj0YBTPE5iscMX0CxsDOMt1cvrtNYp3JNSJc6QdVpsEteN9/objAlKOFhKoQxNxFwsOpxffYlZGCaQi6OZQzr5Zb3XY9IXxxJK8S+pFyM6WJfWmlj5JUasKo4DqvwJHZfdrFLj71DiFxQ60RGEfR6g0sqjFsdSc1HD4UcAFmCO8HsouNWh3UGsiDWFzca9C6ZK141iNf7t3dJG8cGeuKMFIbOihSYMo/DSFkKTr73zNlyB1Kpdyx0Th6jR4gcdHub8mEsyQO0jq9NnpxdAAUsiPTTOfX8LtYewa2mn5B8tATGwZc7tA99rtzBUaINc6iNyjSBoPIP+oL0Rj2XrMLyypVPaATbnvtSXYVC+EBXbmlw+6IyI7wrgm611sPm9pJ1+oz57O8fOh9V+i/e81iMbRlTYDTKGT/UZ0N9nWV6A3y0CmgqRroVKh382/TDZP1riJcisbmkDB4KCzqWygOuSlq29jTwzV/r0SXbAGV5PpRn1sOs8oLRPw7KwDYinN+hHYx1iQYcTS/C/0wLcZ/qd6QrgpGha4NSvw6QKAuBDkCyYJ3/5TiSJxf2s0gS/Dw0aB/ijRcnOVdbwZ3sfW80agitvLz6XKunnnpKBOEU2NJyYe6UnZ0Ns9mM8PDq6SH0d3q67VE1ut7W9tTDQvuLjKw9Yvbmm2/aTJNfsWIFvLwc77VzxMqVK9GadAoLxOfZt+Mewzy0i1+IAx8eR0KXe2CxFnNgNunNwM/Hjbi7ZDbGquWlZg5E3YDT5pHAP+4pSuaO9x19XvroNMjRW/DxopXoE+y60ap1KQoUwQtJ5RYs/9f+48sX30lq5JWUY9myvxr1I9JkAXJK6CNSiYO7diDePbWF0M1XiZ16JT77cyuuald9Pc2Wjtpo3oFifIE5+Nb3ZkQWmLB8uTxC1Jo+8/roFPjCPBnvKL+Cfu2H+C8r5uwPliZ0IFeBoQo5/TlREYNDy5e7dP8UjrRTZiA/g16n5U697hmF8ujI8d2bkeaCYtf5uQoxD5SCkp1/fYtM/z5wtSNHkuChMKJU4YWVW48CiuOuf9/lKaCGhIQ9a/Bf6W4Y1K5b9YAcL6DPMBWCdRI2rJY7aF2FBrnUtIyQoQRLfvkenhU1MVxhTZIBEyoK7/23LwX6w655byfnWkQ2khZ6rFj6Pcp09qXyNvRd9mteZxyT7sHFKm+YXfD/sMgIFEkR6IwUHNiwDIlh5U6/9xS5clG2LJMPtjh5jIl5CuRL3mLd9w1/LkQRjfA7qOzoIRzVfYATx7tj+fLHHd5Ptkn+zV2efsLhz6tdqQaYTeMRrvKF0YF9FJg9xHnWmWPY34j7pxZLuANy0L1m2z6Ua+UOp/pki7eEGgWl+gafb365hKlQQSspsHbjVhg0tWsaGHPkkemyooJq+1NJ8u+MgpLyyus/VX8GLUxY828BfLy80c8svz+PnUpAcokWp1XtUGpS4IffVyC87DiuQhnyJB+Eh0ShU7YFfUoOYPDOGtOzKDlR7uPEDP0bOCzJU0H81CVQKSREIBsR+mwga5foOLN6orwrUlH9sychLRsDFK5Z7tIdSkvl+goNsftXBc3hphHl3r2rp0w98cQTIl3ghhtuQFOoWSyCHru+AhK2trd1vdWzzz6Lxx57rNpIN6WwU8eCn59czr8pekzog5hS+G2Nxrdk5gkT8MP3cbg66XX0Kt+JoFMfIfTOXyurSLPq0gvLcd+CnZhZ+gT6qU7CrNRCumIOuna7HE3dp+eK991exVHM35KIco8ATBzVidbocMmx7Vx2BEhMwoDuHTBxrP3zs/VGM3ofHI7uigSUdf8d3u2HNGpZHmzbCA+NEldfNsHpQjV18aAlqxZsRVTRIYwfdhNUga6fP+4ub/9zFE8a7xPLlgwJ+hHKiSsdSnNs7s88+l64Zo430nN/RYQpDxPjyiD1vq7Jj2Pf38cwLF7+8dT2opvRptN4l+5/f/lm4Ajgb87FxHEXU9lbh5cyvG7nq/BWmXHtqIegCHD+Pds7rwwHP4kVQcmAOC8ohru+CN/CI4sx23QZJvUIwcRJk93yvotMyseUeVdilGE/TLEfQurn2ueRsfEUQg/vQt/2nTFxomuWC6vq2PF38GzpO8guGAL/6/5y2X6PfPcrlAoJZWp/jLnsepelr0ck5iP+20h0USTj4l7RkDpe4vQ+c4r1eGTrOpxUhGPGTWPEtAFXfMZcdyATH+ivwYdjL8fYsACn3nu0pNlfO+dS/wuiewxF50uce591zChC8pehIui+sHcbSE589mSc3gVdmRHB/r7oMtHx4/q+uAg4AQQrC0V9JkfMT96G3/Pb4pNL+mBij8YXol2cLAeEoZ4S+jXiGHYeT4T2hDxl7OJJV1GVNLuyWIwH/odoRTYuGTIL6mC5+F5dn8G998yFr4dKZLzY+v/0Oyx4d7sJQdG9cEvFsdNn3pqj8vx0X/8AjLY+p923Q6MwI37wbMTEdUDu3kcBCejdb6CYOvhN0jbsSy5AbPcBiNorF7A+5jUAl02ZjOGj9Vi15BSOpXSGWaFCbvgIcTiaokT4lqWK64bHheP6rt3QLzYAsb7DkVWSi+zkEyhMPARL5hGo9PlylopCiSviOiAqMhpBXlqxH1rGjuo3XOrA69dUrFnRLgu6b7nlFlEJ/J57alc2fPLJJ8UHSl1zq10hJCQEKpWq1qh2ZmZmrdFsq4iICJvbq9VqBAfbTvvS6XTiVBN9KDb1j8HmeExn0dHeeMej+OW3aIzb/yiiiw8ia/YYBNz1BzSh8twrJqO1ge/4dgcyCvVY5zkMPbU50Nz0ExAnL+PQXJx5343vGYXAHR/ggeNLoAh9FMqxL7vkmIIytuB19b+IK6cfKN0b91wUFugUJuQV5SCgEc+rMC0eS7QvIUMbB612AtxldNcIzPH4DKPNO5G02YzYK15Ba6mwr976KUZp9sOs8oDuqs8BrbbVfuZNH9UV3/4yHvdp/oCHoaRZjmN/QipK4CGq9arbj6QGcen+IyNjUHjYE36KMmiKkoEwx9YfTi004C7VX+igTAOKpgKhdf84tFebEDV+V9BIyFaUpxyEn4ufe0GpEdvyfbEN1+PGy6sHOq583/WKCcJ3UhxGYT8MKQfgNdi1zyM3+Rh2eNyHgtRoaNSHXD73WhfWAUgAvPKPu/T/wIaiSPyt/wDvjw7HQCc/J6rqGhmAF0xXQAEJb4T2go8LjvlMnvwDOibQE75ejnVM2WIK6YajKQVILFGio3WZJgffe6mFJYhTyPOM/aK6QOnk824T6ocNUih6IgGm3ER4OLE/Xam8RrcyMNap95BXaFsRdPsYsqBRqx16r2cWyUXEooO8HToWUbk7C1Drcxt1/2K9GfNN49DWx4yLKpYCa0igjwrjlTtENlJhQQY8I+qumm+wyG3hqVFDU8f/p8LgvvjMrMUUnyjcUeXYlRXrdFOhNetzKlOoxTrdCotJXPf/9u4Cvq1y/QP478SbpO7eTjvphPnGmABjbDBcLjBch/w3uHCRi9uFi7sN58JwHWMwBebauXaV1TVp48n5f97zpl27WtJGuvb5fj5d0jRyenqWnOd9n/d5LpC/BkOdEZ8nZUvfzwrZiQsUf0B54HSEF/8tPcaSNkX6WUKkEpdfOx8A+2pdiyoOERGITe4DjGs5uOP7ahv+5+mx4fHiF1aE7JNPPmnz5yzVnFUL9xeVSiW1/jo+FYd9P3Fi6xVeJ0yY0OL+LE189OjRJ1wweyJhM4IXX3AJ1kz5n9S7OcpahFcX/Qyj5cRYox4IX27MxzVvLpUC7v5xelxw2zNQ3r4h6AF3V43JiESFMhFyiDDv9V3KZpwhB5crlqGPcZPXj61nBVTYh0pNmXePKz2IkbKDGAbfFuhprXJzafLp0vWQ3V+dEEUI2UzQ+58vwj8VX0rfy2c92+kArruYnZ2I3/VzMN7yKr6W+2+Qpb313JuL7ZhtexoV8/YCGs9O1LxtG3ZEdPecr+p8ql5hVR1ShHKftAtrwJZ+1Efyk0yx1Lftn5ico7wQY3q0FhFa/y15Yusyq0J5npKt0A+FZkt4Lr+oi/V5wM2wfs8uUYCWVSqua5Lz2QWs9++BCjPyxATEDnV3ufCRcK0S63TT8KNrEg6aO78OuanckirMlS/FWfr9rOAKfCXDhx0EjlSakOZO15e1MyPqKb1agQOK/ljrHIxKhHapPo3eygcD1O7ibJ0VGpcqHYtK0Q7Ue9+Slk0Gqoz5iIIB8aGd+z8vd7fL0njZLqvcrsEjjqvxaeJ9XhWPqxf4MWIxtt/m1GzngbNW1XYWhlLBQzw7Ww/UhMydXo6GlmLsvUqaMmP1s/g6cYODry/XqPmg00jsw5WK3xFfsAQZVt5SMHbETI9/N8L5puJEgLC07/fee09aP75nzx4sWLBAahfWMPvOUsPZjHwDdnteXp70OHZ/9jhWRI2lxBP/mzV9KvLO/RF3i3fglcL+OP+NNVJKTG/G+k3f/+UGyH+ch89kD2Nmfx2+mTcRqdE6QO9Zhcvu3r9d7DNduq6t3NmpD8rWhFj4h7gy0vt1ZmYlD17sRu9OIK2VvABLndodpPhR+sn/QJ2oQYytENZDfBS5u2InMg8v+huPOl6AQnDBMfgC4KRj77sn8rH7j8mDUY8QvPvnYenkMZA25lZJ4y2sYExcbNfXpbYmM1aHPJFnhomVvAhTZ1SX5EvZI06W2xrmux72isShqBL1qBIifD74tCuvFKfItmN8YgBOe5L4enRd9W7A5buuBDaHC5EG3k5Okeyf6uh9k+Okgl+SMt+04TpSWS9tO1uqk9qFitZtYQPXzMEyvoa2q2oL9+Bx5Ye4o/IJnw5s9ItU4Eb5Txix/RHA1bUCqYVllUgU3IGgD1qaMUujLsM/7P/GnqjmBYi9UVFvRYLIP/f1cV0bkEuIDEM53IOPho7XRB+PdWp4Wf4StmhuRlzxyk5tAzvn2Obqg8NK7zI1Wao4E+nlAJ9Zxv9/WE3tB92uqjx8pPwP7re/1uZ9dK46DBLyEGVxL6w+bqZbaFK3xCbw7XRYeUVziztQb1haIY8dIF3GGXZgiXMMtov9MXDAiT3QHgweffrMnDmzzQrhTRmNRjzzzDNSZXN/uOSSS6T2X4899hhGjBiB1atXS0UAWBV1pri4uFnP7szMTOnnK1eulO7/+OOP45VXXsEFF1zgl+0jLU0aOQRX33gn4sPUOFBWh3mvfY3Cz24D7M2L8vUGbMBh/qv/w5U7r8UF8j/RT1aMNyaapMqMPcmIwVnY40qT0v1wuHMfdE2xk7UIOw+YQ2K875JgU0VKl8467wYARPeHvE3neYuyzho3MA0rFCdL10tXvYvu7MO/czH7yFNIESpgC0uHYs5LfplxC4ZLxqQiVKPA4fI6bF31PdCFwNRb63MroYYN4/r4ru/y8VjAkwd+PJtLD3T6eewVfJa8LiQJkPuu4Fx8Sj+cZH0bT0U/5fNjqvbwJnysegYP5V8Lf4vPGCINoilZ252Kzu/n47GgMovlfrMT6rSR8Ffv+v0iX6PvKPFN0L2/xIhHFB/i36G/QGY51nbIVwbGqKUBldCcD3wyWOMo4wMbdaGZPj0OU2PDcafia4yr+rHLbcMOVTkx2vImPhz0js96tadE8ICvsNqzolCtKaqxIEngn7WKyK51NUqK0KBY5L+bs8b7oLuk1oIEgR9vyojODQ6q4vrhXNsT+I/ubq8eV2+oRjRqEaX1bpDPKucz3fb69oNuZ105pshzMNKxvc37JFWtx6/q+3BV+fPNbpextnVSS7Fj5572hqDbZoLLZsF/hNfwlOJdaHgvEoSl8mV9LOvgNvv/YeHAd6SBauIdjz4tL7roIlx88cVSq7A5c+ZI6dlJSUnQaDRS3+7du3fjr7/+kgLcs846C//973/hL/PmzZO+WvPhhx+2uI31Et+yZYvftod0bFhKBH66/WTM+2QTHi25DykH8lD+4jpEXvkxFAmer889kf207Si2fvcCXsJH0MjssGlioLp4IdBnKnqaU/rH4FvXMAyS5cO6dynU2Rd26fnKjJbGEX2du62JNxyaKIC1yzR5lx6mrOPr0nw5k9deaq1pyKVAzh+IK/wVsBgATWAKN3pjd5EBr/y6DW/LDXAKCqgu/ahbbmdXUiwvH5eO6L8fxahViwHDXOCctmcSfOnwgT3IUd8IQ9EowLWEHRR+WcpQoe2LbeY+iJTFobPJn6w3K+MI823/+gGJ7FgSsK/E973S1aXbpEtr/Ejw01r/yU6NxG4xHWOFfUDxdiDON2UxdxcbMEXGZ62ERP/MdCeEafCDnP1dN8GYl4PI1lfveeVwUSluZa0wWSwntuwO01X9YkPwoPJZyPJEoP6mLmeNqWv5YJsY7XnRTk9kxIZKmSas6JtQ1bXlmKzQZwXCoUgf6rOBAbaGnSmurO30cxRV12NQwwx8eNc+O2P1atzsuFaqTv9R/ER4+1ctq2WDVO7fJbRzg+cxelVj21Fv9C/+CZs1L+PgkVMBfOvx46zyUMABOMztF+ZyWvnAiE3Wds0BmbsvuZyl5zexVz4AFWYRutBja8Yd7qDbabPAaq7D+fK/pO/rVDwwj8vM5pdCDUJhwhlD/T8Z0RN59Kl+3XXX4fDhw3jwwQexd+9e3HTTTZg8eTLGjBkj9b1+9913pT7drI/2F198IVX7JqSpuFAN/nfjRKzrNx8VYhhiTQfgfGsqav5894RYw9pZNSYb/v3xUmi/uRwPCe9J7WqsGdOhum1tjwy4mbgwDfLCeZVw8dDyLv99+Wg1/xCXdWa0OoSPlMss3gXdWgsvwqiMCsz72aSps3DAlQyNaEXVmo/R3ZhsDtz++RZUO9VY2OcVyK7+GUjyz2xbMF0zKQO/gx+/ru2LgDrvagF0vj/3eillO1zp8EvA3eBIwhnSzM2apKs6/Rzaej7rJI/2TVprg6wEPoCTX2VCvdl32VDFtWb0sfF1iPo+Y+BvgxPDsMvFU2tN+bwNpC/k5+ciVqiFi526xQ32W00WU4R7bX2Zb9bW1xXyGXOTKhrQ+b53eWZiDApE95KMcj5L3ZUlYDEWnjGpTfRt+mxm9LHlHfZy3u6rKyn7TAZbmuYj6WEyrFfPw31bpgCWzgXepZU1+MM1Ekc0g4DQpC5tD5tJLQsdjD1iOopM3leQN1QclSrmO9n/F1YDoROi9erGOibenMvIzPx8Q2SD/l5wKPnf02Vuf/87LXwphaPdoJsH0rLjgu6Visn4l+NGFMVNabzNLvDf02k1w2rmx5ZDlEHjLiwdHnHs9xioKMHUgf5ZAtXTybwpZHbZZZfhhx9+QFVVlTTDXVRUJPXu3rFjB5577jkMHNh2pT1C2CzLdVdei40zf8Lf4jCoYUXEsn+i9O3zAENRj9tBK/aW4YyXVmPMgRdxqnwrHIISztMeh/rKb3rE+u32RAw6BWZRBY2lHOjiiVtZZTUiBfdavTDvP8Qd4elY78pCgdy7VLdIBw+29LG+nc1rS0qUDmuiz5Ou5+3ZgO7miR9zcKi8Xloq8vRFJ0FIn4CeKD5Mg7Th07DF1Q8ylw3Y+J7fX3PNwQqMk/H+5qq+x06E/CHTXcyps/U1WBXweCcfkNIm+LYjRZROhXN0O/GX+g44PrvUZ8+7Lb8Gw9w9XlVp/g+6dWoFdoROxn/tF2NPFJvp8g1bIZ+tr9OlAyrfr41ulDgCXzimYn24bwoKCuV8wMMaydeF+lq/OD0OinxA1l7KX6srwWymwLOcdEm+PaeN1KlQLOefYfXF+7tUmG5C9c94QPEp+tv4+4YvJMZEQgF3DYKaY0s1vZFnBG61z8cXwz8E3EFfl7YpnAeVxTV8rbE3TBV8cNCojOn0QCZ7T2JrpzcIV8J2YLnHj1PaeHq4TOdd0O1UuovYWduf6XZY+fu3Xc6zE1ojV/KAWeFOJ298DffYgaLJPnk8/nn0tXyCo7Enw+Z+bgtUraaQv6D9QHqPI97r9HB6eHi41JKLqoATb505YQSSb1uM97U8bSi+ZAVML41G5eGesQyg1GDBrZ9uxjUf8nZgn4ZeD0PyKVDc8ifkJ9/h11ms7mLSwBS86jgXT8tvgqjvWiEyYzn/8LcIIYDa+1Tm+tQpuMT2ED7Xez6zV291wOaSScdnRJJvZ/Pakz7tWpxufRZXV1wBs813BZi66oetBZixfT4eUnyMly4cLJ2I9GQ3TO6Ddx2zpevO9e8Cdu9P+Lyxal8Zxsvcg1OZk/36WqxQG5NbZgCc3neUKKg24VH7lbhW/gRUw3xfHyUmOkaqF6Cs8F0F872H85Ah48UYA5Wd4Uw/Ga87z8Vaa9crSzcUMNxcpcH7jpkwZ50Pf4pNz8K9jhvxpdj1ntds5jiyng94qBP9MzvP0pDz5TwjyeieVe+sQ6V16CvwSQAh1vcTSfV6ngHh7EK9CJa5cZqwATcoFiPW3PkuBMdLjgxBYUPGQCeD7qPu4Dg5wjet1oZoa3Gz/EdE7ljo9WPttfzvaFZ3fpIjTKOASnBAJ1hRV+V+D/GA2s5nquV67zI71iZcjuGWd7A8Y0H7d7Tx9HLWrrOjme7j08uVLgv0MEEpHAvGZUqtVBjT6nDBZubPbUXz9slL+j2Eo2I0iqY0XyNOPNfzz/5Jt8TWNl1x5/P4fMSn2Obqi/2OBJz2cSkWbcwPeNVgX2EFvz5euRO/PH8DTt33EOQyATdMzsTH889F2A0/nfAtlbwxOiMS78vOx9v1U7Df2LUAbZ89HkMt7+HD7I87tXatoXpolbuaqKcnNax102jhM+j8dKLYmslD+8AS2V9KN/5x+1F0B/tLjTjy3WOYKt+OuaoVmBDpmwrB3RkrJmXpeyYKXLGQs2UJ2z/322uxYOrwvhwp0HSxFi6pPLXdXzJj9HhD+RLeODILOLDU68ez1O8qhKE6+iQgwvdLLzRJfO2glnUsMPum6JbpCM8cMeoygBBeWNHfhibxqss7jnZ+fWxTJQYLNpqT8KTrKoTPfAD+lJXAZ9t8sbb+cHk9+oMXDQtJbtGt12cp8fVhfaXrztKuzfyWHD0CvWDhlfl9VBW8KTGKD8KoanlBvM7Ik9qF8QBQ5sMlHizobkjTt1Z0bvvKq2shwCU9ly/0U9fiXuUXGJD3P68fKxh5xoJDx1P6O3ts1SkipOuWGp7h44kQB/9/rwn1Lg1bpYtALfSotbZ/HuxqCLoVbWe8yJWqVme6F1jfwE7N9RhQ/FPjbWp3ezEWdNvdM91W9zrvBqf+YwGEBbswfpJv2/71JhR0k6Cmm1913iyoblyG56IfQbXFhX99swMXvfYHSr66KyBrKX110vzb9ny89ezdmLXiTFwr/CgVoVh6aQQemD1Y6tva27A2E+My+Qjv6v1d6/VaYjCjDlqo4ztX1CbaXQil2os1WUdr+HrSpAhdQDMT2EDN3PE8nf27VZuktiDBZLTY8fYH7+N24Su+fWe9ALhbh/R010/pj/edvA+pc83rPu3Xe3xF6iGm9dJ1MW0iO+uCP7G2YQ7IoYQDzgrv15UWVPGTPX+0fmL6piWhUIzh3/igX7fd6YK+Mod/k3QSAmVocjgSUIno/N+Akp1dfr49xTzdtF+svrGNj78MiA+FCnaEGfbDmM/7gndl0K6/jKf5CvH+G8AUYvisdIi7CFpn7aqR4wLrw1g2+AmfpEcfT+P+HAuzFEFwt27yVl6FASmC+3PVhwMDrJNKuZwHqPWlnduPF1S/j73qa5B92DfLctTRfGAv1FbmdX2YHfZkKTPEmNa1JR4WJR+osxnLPE7/17v4/1dNuPu9zItinozR0n5LOZeD99MWFRqv08tl7iUEQpPOE9ONP+Jl5WuILV4Fh6W+WRuxpn3EkyJ8M5jSW1HQTYJucEokPrztLPx79iCEqhU4pewzJOx6D9bns1H53b+Auq4Fbf4MtlfuLsCbLzyEId9Owx229xAjGGDUpsF56SL0HeaDsq8nsFMGxEqj8eot7wJFWzv9PIXVPF2ts2/2kRq5VBzmd9OFHs+cNawfa1hPFkiXjEnDNeqV+Nh4PUq/vx/BPL6f/WIJHjA/KxWjsWRfDtlJV6C3mNg3GjmxZ8MgalFrdgDuWRNfW7W/HNNkfK2ufEDn++N6KjFMg0KBL/kwdWJdqbH4gLSW9EyH5+sbvTEkKRx7XfxE21Xa9ZZVbLb2G/tEPIyboBvf+eJx3hqcFIbbFN/jKfuzsGzueqbEvsJyjBH2YkSc/0/bwkOUmK/7HUvU98K24pkuPdfhojLEwt3+KNY3Vdxbo3e3NNLbyjtdBIw5UGnHZnEgxCH+SeGPScrAbOtTuCp2EUShc4Mn1cW5UAlOqU5MZ+qctMes5WvjnVVHOlVsM8pZBrVgR2gYnx3uqvD41MZWVd52IFlu7o/HHFfCNaJrn1s2NZ9AEI2enYsazHZEgmeE6SK8S21PcRbiScVCTCt4o937rYy8EJmWT7Fy4MNt30kfjzccc7BIzpdKNZC7B3tkTVqG9bXsxjnyNdAbDzf26m4orkZ8h1bCk26BFWu4fnIfnDsyGV99V4ltB3IwQnYI6u1vwZrzIYxDrkDM9NuAqMCtr22Lw+nC77tLsXjZMjxY/QCmCjWsyw2Mqlgop9+H0DFXsiFG9HasdZhe/gMuqVkJR44Mik6upRxf+R0uUeRioJm9XXm/PjwqNAQK2KCEE3ZjOZQe9DTVH/wR36k+QJmVjZCPRSCxE960YSdDteMdxOUvhlh5CEI0T50MpIXLcjD38L8QJatDfcww6Ob0rnVcLK3wyqlDce6ix2BUpONPbQL8MQSzfG8ZDrjGIyMuHGkDzkAg2tPVszRrM+Co8H42S1WxS1pLWlnJUobv8vn29Y3VYYWQhtOwFfX52xHaxWz7rfnVyBfjcTh1CGR9/Zu6f/z/4yJtFmBbBkv+pi4fO3VHtuEr9WOoz2MzZ/7vH2+JzgLKAFl519K1d5Y7MMT6AZ47NRTnhPgmEGtNWlIi7rDdCkRm4BWlttMDjYfKebDUN1YPf8iMDcUuMQMxbPw3pXPPYSvlGSp12hREyHyb9eAMS5Vau8kN3vfFLqoxI1molK5rorvWo7tBfGQYysVwqWo/DEc9rn7PztMqWHabuzhmV7hCogE2cW3iv1tHqk02/OYaj1ShGmdFercEJ0qow6mKZSgztN+Sy2RzQoQMak07a7pD4/Gs41LEaFRo2mhZ3lAsr8l5qsvdXgwOC0pjx+Myy1sYnKDDZ15tPelIp4dMbTYbCgsLkZ+f3+yLkK6I0atxy9zLEHLLSryR9BS2u/pALVoQs/M9uF4ZiaJ3LoLNHpwCU2W1Zry/+C+c/MwK3PLZFvxWwj6UBRiUsTBOeQShd++AZvx1FHA3qSi7WzNCum7et6LT6c1jHZtxuWIZEux5nT75rQJfo1hX7VkhFHX1AYyUHUQyO+sMgrNmzMQq1wjI4UL5j+2MZPvJbzuLkbnq/zBAdhQmdSx0V34JKHtfWtms7ERYwvqgvN6O77ce9UtLwfW5VVjknAbxsi+BGN/2BW6L6B68VNZ63ytYaeCf866IdL8NwNaF81RhR3HX07K3FvBZ1pFpgVnL3ZQ9kQ80ait2dHl5gqKcp3lbowNTY0LlXlsfVpcLOLzrUXx8poEdCiT0HQ5/Ymn3P7om4deaFGn5RGfXzZ/rXIrLFcuRpvTNWvzjZbgLGVbU2dBBBnGbZDV8FtoZ7vv/g0JMP6xzDcLhEP739wZblpUsVPBvInwTdCeFh6BY5APlzhrPBwLK66zog6OIkxkQre3a/KKg4yniSov7d+tAtcmOZxz/wH+0d3ndq1yl5wNTGmf73SXM7vNgrart300pFxrrDTUlA/9e1iS9XJTzoFtwWGBxylCNMFjU3qXGEz8E3QcOHJB6dIeEhCA9PR2ZmZnSV0ZGhnRJiC8MTAzDvBtvRci8VXgz+Rmsdg2DDCLWFpgw/j/Lcf93O7D2YAWcees7VYHXU9V1Fvy+5Af8/Ny1sLyQjTPWX4kygwnROhVunDYIqmt+QNi/diN02oJeGZR0NFOo6HuKdF1fvdvr1DCmoMqMVPfaNU1sn06vkzYIvOq5qdqzIFpVz1OJhfBOTkV0UWyoGvuGzJeux+X9BNdRnn4cCLuKarHgy+34wjlNKiCjvXIRENb+qHtPxdawXXsy/1z7aPUeuA6u9PksN1v/xwpXpfuw325HNAl8Xb6e9aL3ojI729YwMz/xVcf6L/tCljgcW139cEjV9XRkc+5GzJUvxcmhgR9Ai8kYBpOohoqdQFce6PTzsMHHOBN/fEgqH8j0t+S0vtLSCmlWrJPbzra7oZo1K07oT8kRIQhRymF3ilKxv87WV5in+AFPKt6DyuCfehps3fRp2oN4QrEQ8SXLvH48KzQbYuL/B5Wxvm3Zx2gTBuBS24N4P7zp3KhnSiqq+Iy0D4Nu9llYCj67XefuZOIJ1jnmW9XD2KC6GbIu/N9jxPAUqeBvgTLT48HUpkVcOxN0h4imdtewn1L5FV5XvoTMqtVt3kcpiEgXSpDmLGj2XHL3Gm+ZvMn2NVRBd1hhsfOgXKOkFci+5vXwz9VXXw2FQoGff/4ZiYmJ0ok1If4yICEMA264GWWGq/HRytX4PqcMVXU2/G99PrZtWI3F6vthlmlRGTMW2qzpiOw3HkJCdqd7mBosdhzcsx3V2xcjpGgtBlp34HTBXcFVAMxCCN49MxQnT5oMtaL3FUjz1rBBWdi/J1maMcWRP4HB53j1+IKqepwilHW5YIxJHg527mip9ezEW2flVUpV7iIuwXDhWbPxy+5JmC38jcrv/4XYeUs6Vb3dG2UGC274aJOUumbuNxPqy+8EQvx7stzdXTImFR//sRmfGhcAn5mB+Tlez1605fedR3GF/Hek9fV96632JCQkwyCGIEwwA9VHPO6swNohprizP/Q+7tHdVFyfbJyX8ximqGLxUReeh538nmRcjhuUi2GVxtFORiANS4vBTjEDY4V9wNHNQCdbULEK6IMFHgSGpAUm6B6YEI59YgrGCPshlu6GED/E6+c4UFYnrU8NV7kQUdcXYOn2flw2MTbajNTyVTCvPQDM4YOW3jh4tAyT4E4hjvFfwcjRunJc4VqG/UbvZ//LjFY8ZbsEC2Vn4q9pU32+bSnuquNswNtb9aU8c8Yi00Gj8c1SAjZoXquIBZucNVfkg/cE6Fh5VTVGCO7Bl9CutS11JZ2Ec9c+junhcZjgwf1rDHWIQS1itN5n14To+WNYoUuW6t3WZE6mZTdOlm/AHsvpbT6X2lWPVeo73b/EVY1ZmA292AXFsfTyhoJsMqcF4cV/4QnFIlgso9kCP69/B+LDoHvbtm3YvHkzsrL89+ZJyPHiwjS4as4MXD7bhbWHK/Hz9mKIOzegUgxFtMuIlLKVAPtazd6bZahQp2HdwLvhyJiKuFANIsx5CK/KgaDSwmG3w2a1wGY1w1ZbAtFQjB8052J1ZSiOVJpwu/xb3KX8mr+wANQJOhTFnoKo0ecjZsRsnOrn6sI9ycS+MfjZNVQKui37V0DjZdBdUVKAEMEGFwTIwjsfAJtUkdIaVruh1KN1fVGOculvHxrHe6oGA+uFXTn2blg3bEBs+TpYt30F9ciL/fZ6LEBZ+OZ/oTAko09MH7x+2UlQhlBtglCNEmeOG4r9a1MxQdgNrH8TmPGET/oXWw6sxhPKD+DY9SMw+wCbekAg9InV4w/XKIQpgdMEz2cz2AzisVZFvuk/3ZohSTwzZVcRrwDcWdsKajBaxovFqftOQqANSwnH566+GCvbB1PuRmhHXNap59mRX4krBfcsX8IwBELfOB2+EVMxBvtRX7Ad+mEXef0c+4sNmC1fhwhXPeDk62v9aWxYNW6t+QC1u1M6FXTXFuzjRSMVYdBoveuv7BXWNswIhNs97/vc4EglSzsWoIlMhDLCt0XUmNQoPmFRUlXLe0F7MYFhdxdfqwtJgsaHA8TLIi7EwuJTsCDjNI+rutRV8GwAq6CBWuNpqN66KB1Pva50rxHviKpkCzZpbkFZCcuU864YpC40HC5RkI5DWAxtBt1KFx8UkWnaPh9VuKuXMy6HrbFw2joMx35nApKbtlJz31dwWhFatQtXKJZhrZU+/4MedA8ePBgVFZ6tayDEH+v9JvePlb5s5w7FtvzbsWT7GoiHliHFsB1DhMNSelOc9Qg+31SEtRu2S4+7TL4MTykXtvm8r9syccTF28kc1Q7CHtUYOFInInH4aYjpPx4D/NA6pDdgqWH5YaMA029wdCI111J2WLo0quIR3oW/gVUVLQXdogdt6GrqbVKrHyYyMbhLZi46/RR8vPVCXG7/Fiu3H8KsztWi61Cd1YG333oR95iex43qcJgvWoZwLX3gNrhmUiYe/Hs2Jsh3w7nxfchPvhPwoCBfe/7YU4rTxLXSdfmg2QELuJnMGB0utM+D4AD2RPTzuMhXQYURoxrWbEb6b0AqKyFMmuEy1hlRVnAQcamdm1XfkVuCmwX3unU/9z9vjU6tQFnYUMC0GLb8jehsk7XK3G3S4KNVoYc6KjBFFVkmV5luAGBZBltB55a3FBceQoRQD6cgh9yPlcsbaJMHA/lAqKUIsLNZQu8KaLnKeYs6U1g/nwaNxwthyzvygGhnOUQXS/X1/L02Twq64bflKGlRWin1/TLXcpjXPoqQKf/n8WPzTUr84hyLgYlD4MvVwLKYPthTFIICk+f7yVLFg+46VQzUXfxbNrQdZevwpTTtDp7PUc/PH6wq74P90BAV6qBBGMxwWQxSMbTWKJy8ralC3fZxoFQdC7rtdivU7vu+IVyMUruAz6OPZa8IjTPdVsBhbl5cjQQ26DYYjo02P/PMM7jnnnvw1FNPITs7G0pl8/8EYWF8hJqQQPT5HtsnBmP7zAEwR+rHyiqPbjp8EHUFOejj6gdZvQIVRhtgicN623CpKJtLUECUKeGUq2BXR8GlS8DsjPG4tu9IaYYlUsfaK/yT/oA+ouk/Fc5tTyOkLh9gH0YeVh9lnFX8hNmsT/U4raw1dWF9sb46C2ZZPDpKpC0tK0GWwEe01VHBSy9nWI/3/uc9gNM+mYDS/bFIKazBsBTfVgBmM64fvPVf3Fn7X8gFEfJBs5CSFvhq6d1ZQrgGocNmY/euLzGYFfRb9yYw/YEuPef3m/PxH/lG6bow5FwEOosiVKOQesHmVZo8Xm9bUZwLpbtVkSI00a/H/RWRe/DvuidR9/0w4PbOraU3HF4vbS8rBqj10RpTb7nSJuH2nNswOG06bunkcyiLt0iX5pjhUMsCt86yNmE8nj1QiUEJp+LsTjzeWcT7oxv1fRARgBP4xKR01Ig6KdBH5UEgYajHj2UZTmG1B6QMJ1mC96n03ohJyoRFVEIj2GGvyQPiPR+QqDp6EO8on4fSNtwvnTXYQJFdGQaZKMJUdhjeVKpZZcrEZ/b5+GayJ0nY3rU5bNrK0xPOWl6XxaJpPWj1RoxOjR9U/0Z/81Gg5A8gsf1sE1c9r19jZxl2nVjzXwmtFHSb66qgi239fiqRn6Mo2pnpViqPTVQ4bFY0/A90is0LrTFHMi/BdTkDMTEjFdfb/8d/j3Z6gBM/Bt0RERHN1m6zN6dTT23ebJ7dxu7jdAansjQhrOgRmyHJSmAz1ieheTIcK+h1L+2kIBgzKAOXbngQhohB+M2LgJsRGnojd7FScmHaubj7wBBcFp6GjlbBVVaUocAVC63chehuUBxv6pBUnDQsGz/nFOP2z7fi59smIjTENyewrIbBV28+gltrXpfS2ar6nouoi17z+9rxE9GNU/ripe3n4S3VS3CuexPyCbcCnWyBxFrZuA4uR4zSAKc6AvJMXnAwUNhndZ8YHXIKq3G0MA8DPQxOcgyhGGN5Aw+cEoFz/TwzH5aaBeVeJ/RVu3mxTC/bMLKCU2Flm6TrtqRx0AbpmO7Xpw/e2zYR5eXaTgXdLKX1p7qBMMquwr3jpyCQYtKH4r97lZhjTupU0K2t3itdinH+DWIb9E8IxUExGaOF/XCV7YXMi6C7uNaCDFce66cEfZr3lbu9kRGrR66YiEFCPoSKA14F3WLZHsyQb0ZlnbvWjB9Y9clS+ruj8ohXRRZLavnsa1KEbz83U0OBm+U/YuxBOyAu9OjzSajj5w5OfdeD7ii9CibYoRWssBhKoelgvFFm4UG3S+N90K1WyHCx/THUuZRYEjEEbYXUbAKJUbYXdCvksIoKqAUH7LZjqfEy0QEZ5FI2UQMWvBugR71TLlUwl8iDf/7TK4PuFSs61+6HEELGZkbjRmEQHNUiCqpMjWvGOsIG8l4wzcLL9sn4ZVrXUkNjQnmQWm7seE3WYWcsLre9jBmD4vBON/nzPXluNrbm1yC2agtML9wG3eXvQZYxocst8H5985+4zvKpNLtTMnAuEi55hVUk8tl29yRsQE8x5Gzs3fcNsmwFwNrXgOn/7tRz/bS9CBfLlkvX5SP+EZQ2gyfri/Cleh5sv0cBo/m6547kVppQjgiE9WEFdvwrte9QGPawGR8TULYbSPSu6NS+UiOGOvdIQVTogMAWUGuqoVXZjsJaKTBpeqLriZyjtVKf8b+izod2hO8LZ7UnOzm8sZBbZwaW0uyHpf2vSw9M8bf0KC02IgWjsR91BdsRNuxCjx+7v9SIgQJPSVZ4Eax3Rka0DsvEZAxCPqwle6X3FU8pqvmSK0ekH7OR2CC3EVAYCzx+SJnRAr3LgHqZXqqj40vx4VpcpfwCYN3/TP/1KFtOVc/Xy8vDur7uXaeSowY8i7e+sqTD5ThyS7V71CmqUwOiJk0cak12GG0i2orv1WymWwBU7RQ6Ze81ZiighgMOm+XY5w/mI15TjYNVPwNpk/nzuQsDWx2uxqBb7AaTDr0y6J4yJbCjq4SQnkOvVmBEagQ25VXj74MVuHSsZ2mebP0U60UpCFokJHatWnRsw5os47EPnrYUNYzWR3Z2BabvsfXVr/xjJMoXPo14eyFMn1wM7Y2/AfGd69l7oNSIn999BAscn0rfl438PyTMeZRmuDsw//QsPLf7QrwlexGVJXnuRjbeYYNJv63LwScynjKMk65EMITHZ0B9xA61tZS3DevgBIttN0tFbwga/G1kWhS2u/pgsnwnnIWbIfcy6F53qAJnyfjyFHl68Crw9ovTo6+6Fqc6VqNiyRbEz7rfq8dvd/cZH+YOgAMddMeiBgOrNqB+rxO6rOYZju1hgTqbyW3a8zsQNV/Ktf0By3LYj/LUdm+C7rtsj2FuXzPme3msdSaFu0SZBrjWwlie3+ZsZqsp8KY8KdhSxvX32/apYjOBAkBvLvJoDTNztNqM39V3Sx0R5BV/AXG+W8MfHxWOMjECcUINUJvfYdDN9tMK60AYXGditrttaVewQJi1z2QV1C21HRe/U9n4IJVCH93p4p2s1zdrudcatpRSAz6BoApp/+hxuMM8h/3YhEND9XJ5k/Tz6Lr9eErxLlCbDJnKfZ7kZU0E0jGPpjRycnLgcjVvrt6eXbt2weHgfeAIIWRSvxjMV3yNCcsuBFg6nQcaeq0mhGm63J4tQWXBevU8LCo/B3C2/97UsG4sMbx7feCMSo+E7ew3pH6hWqcB5nfOgCtvnVfPwU5GvtlciDmv/Y336ibgoCwTVVOeRtw5j1HA7WEApR9+LmZYn8GNtddI+9Nbfx+shLYyB07I4Ega3emBk65iA1msD7OEtQ3zoO/tda6vcb/yf0hlfV/9rG+sHntkPLAwHOAF57yxLrcKp1hfwk/ZrwLxgQn62pptmpJgw/3KzxG29Z12e++2pujAdlwsX4Fp0Tz4DqRInQqXh26WllRY/3rDq8fuLKiEg01zMwHc//YYnsqurtzj1eP2l9ahklUOyZwMqPXwt03xF2KY5V0sz3C3dPIwBT7VVSRdD032X2G6yATemUDNKmSbqjzbtnLWo9sANexdbtF1PJauflTkpdkcVR336jaYHVhsG4HHHXMRMfwsn2yDiRVjhWcdUDQO/n9VHdq5cnJnCX9LxewUh5e3vi02JyZYX8MQy0Kok9pfuvGlcDredcyCTRHaIuhu2qdbZ6vAZYoVGGtdI7UNYwSa6Q5O0D1y5EhUVrp7F3pgwoQJyM/3vIk9IaTnB93jhL1It+6D6/Aqjx6TW1yJj5VP43HFQsDRtXYzkZHRUt9MFRwQTe13Xzg9/2V8p3oIJ5n+RnczZ0x/7Ji6EJtd/RHiNMD1wWyYVzwPuDqupcHW7n7/8nzc/dVWKYNgRL8URP7fX4iaNi8g295T/POMLBQoMrA5rxo/5bhrDnhh4V+Hsdx1El4c9hMU576OYGFtw3JF98lx5aEO759bUY/z5X/iRvnPUJo863ff1b7LhhjeUUJRuNbr9dzrc6tghgbJY84G5F43avGp2AFjYRWVCLFXe7SvGzicLiQW/4Fnle9iWvF7CAZnPC8apSrf4dXjcorqcYbtWXw89U+gjQrM/qDLOAmXWB/E46ne7a99JXyN9IB4z4oKdlVSQiIM0EnBvjd9zzNl/D1HGeu/PuKpcZEoEd3rkWs8W9ddVXyosUd3Z2tdtCVWr0axux66sZSn17fnqHvgPFqngkbpm9oTVjV/fdHY/nsfW0Kyyj4I3zsnQtNBQNyWk8SdUssuddnWNruNsNa4drkO6iYVylvztvxyPOm4AhbNsYps8oaZ7iZ9uhUqnumkcNnwUcxdONn6EgqTz+zU9pO2efRJxEbzH3zwQWi1nqVb2mw2j+5HCOkdWHr5m8JQTMBu1O1ZhrCx13f4mLKC/bhQvgNW8yGgyYhsZ8SEaVGFUMTCAHN1CbTtjMQnWfZjpOwgDqu7Z1HIudNH4lvtZyhffBtmyjZAseox1G76FOp5q6DRHdc9wmnHka3LUL7mUwyrXILzBDu2KOWIm3oL5k3r5/X6UsIrmc+b2hfP/74fH/y8CmeU7IX69Ic9WgvPUlhX7CuXsjX/MWUYK4sbtF2aEaPDH2IChuMwLCV7oRnU/oxQXnkNxgjuE87ozrXw8hbrre2sEBBqKgAMRYCH6zPZeu4ak11ai9mwLjmYxvRNwLZVfaWBRzF/DYQYz/bf7mIDRop8xlbfPzjr0sMzToKrQICeLUNgLRf1cR49jq1hZwZlsF7FgTMgNR5Pi4NQUep5diZL1x1R+h1mK0oxUs6CTf9V5m/QP47Pph8o4y3APHHkaAmmCO6Z52j/relOi9biV+do6AQrzpFpPGpo1tDeU+rR7Y8BOHUi2CS6uTwPHZUnK66uQ7ZwGKFhmR6nx3fEwcqI1wKyDgYca0w2fOCYKV0/MLBzqe0OJR/4ES2tF8urt/JsPZ1a7lGBYcbmcLWSXn7sLytX86BbBRuqXDoUinGQh3hfCI74IOg+5ZRTsG/fPngz0x0SQgvwCSHH2rsZkyYBJV9CVfA3m4rqMEixlvL3HJMutct9NtkaukJESEG3oaII2rQRbc4ssd6pbM1ceHxwe3S35/zxA7E5YRH++/nzuMHyAdYYYnD3M2swNjMKGVEaXLf/FsjtdYi2FSIDDkgdlQXggGoQbpx1FlJH+G89YG9wwyl98NPmQ3i3/h6o1xqA6HRg9LUdPu7ZX3ZglLAPsYOnSEFvsGstFCnTAdcamAp3dniiXHv0IBSCCzaZBio/tgtrakT/NCxcMwsmdRz+T65mh7BHNuwvwreqh1AcfhKUrilBr8LL2vy9j4EYh70w7v8TYR6u4990uAwXy/j7oIylPQfBoIwk7F+dgiyhACjYAHQwONNQVKvEYIZMEDA4MbBtZIcm8UGWwxX1MNkc0Ko6Ps09UFqHWcIaTJDvhmhlAdNEv29n/3i9VJF7WtEeIO8ZIH2CR+3C6kU1RHUY9J0o0uXNzPJ/hOuljKhRygx48kkoq+b1ExzhXes00harLlkqpOaq6TiLtqY0Dz+p/w1HtQIQy30SdDvD07G1sB8silS09xtW1vNJx0itsjHg9ZZL5c62sBxr19xUvaEarylfBoRQwHVau+dSsXIDZDDAbmXL9cKbBd0KxbHJDKWKT6qqRBssdh6gq5VUVDUoQffKlZ3rkUkIIQ2SB09EXbEGekctULqjw2rEDe1mXD5qN2OUs0Io+TBVt50SXFprQgL4UprIpO7dq3pURhSG/PMxfPX3pfh67V7U1zqlWdREVOJhzc7G+1WJeuwJPwURE67EkPEzae22D7CUxScuGou3Fp6Nfys+g/PX+yHPmAzEtD2YsXp/Ofod+gj3qr9AncCqFfMidsFUH94fqAZkFfz/WntsZbzCeZ02DVEBqnA/Oj0K14lzYat3YY5JjT4ejlMU7VqNq2QHUW+tYb1w0B0GHatjxgJVP0B+ZLXHs2/l+zdAL1hgUYRDExectf8siP3F1R9ZsgJYDq/tMCOC2Xm0Fr+p/gWrMgy6uiyWsoBAiQ1VY5K+GFMty1C1dCe0Z/2rw8fsPFqNme6ie0JiYCqt94vVo0p2COPE7TDnb0GIB0H3akMcXrEuxFuz0nGGH7eNFQ5Li9JKGSOstkqmBwOEofU8DV3hr7T3iBQp6FYaj3Z4V3NFnnRpVMUh0kfvVdbkiThvawRmRySivb9UhcGEWFQjSteFgUk1D7plttaDbpuxEmfJ18PqVHU4efGC9VH01+Rib9FHQJ9zIbpcUAo8qJY1TS9X8/dJlWjH7Nr/YYqiEpFWltXStSK2pLngLnQihPQaUwYlYf3vg3CqfCusB1ZA3U7QzVK0UmzudjNpfE1hV5lUUYAFsNa0XQilrCgPyYJTKgCkCO96q5FABH9zp2bj8lOGSqmom45Uoaa6EotqnkWIVofY9EEYMjgbk0K6lp5PWmJZBb+OvQV/bdyGk7EL1i+ugvqG3xpPmJqqNdvxzne/YaHiG+l7/ZBuslYuIRu/VoyBNnQUOupRoqp1tyqKCkxqOROikmNkWoS0PnvNoUppHXpH2OxmZMlfUsUaV/rkbjPIpB9wCqxrldBZSngxyQ6CE7asT1vE17Kbk8ZBE6RWfqxzQqE+W6oIbs5d61Hq8IHDuZguK4Q0oabrXDGprhgXacQN5YtRte8Q4EHQXXp4p1R12y5TQxnrvwJlTYVqFCiU8c8YY8FOdJSLwY6Hg6V1ECFDRrr/s7DSo7U4XFqNivx9wIBj64Hb+rxOchbx9nwp/tl/tuTxOHPf0zhp4DA82cF9xWo+G27SJnWYiu6pOA/bjtZXFGKj5lbYjcrOz7KreXYIy1Zrjd3El26YZVq0v6IbcAo8sHba+Qy8w+nECudoaV33GPfsNqPS8CNQDRtONy9GvKIcW5xXe7/tpF2UO0AICQg2Wr5bw2cRjLv/aPe+rLiMlM7IAstk3wTdNjWvPuo0th10G4oPSpdV8lhW2hMnCrbmbWhyOK6elIn5Z43GJVfchDnnX4EJo0YhjAJuv7lv1hB8HH8vKsVQqCt2wfLpZS2K/rGiXv/+4i/cW/9fqAU77BnTgBGXoTtISB+IW+wL8IH8gnbvx9YDhtXz2SNNwkAEughjilAG15ZP2BmtR9XhT8Um6bo+eza6iwlZKdjgGggzVHCWdZxZsLfEiGw7L16mHxjY/tzHk6ePky5DK3cAjo5r9tTlbpQua3SZgCbwa+o1KfxzJqzuEGDvuE2k6ygvWGWIGBzQonuVKj6LKLA+9B0oMVhgtDqkOhyezDx31ejQKuxRX43Zay7usOL+kUoT/nINxQphHNTJ/mm3Fhcbhz1iOg4ZO/5clrtnw12hvpulbQy6DZZ294elpkS6rJeHd3rATxbiTgO3t76m2+oOui2yjutsNQTdLvfnkkOU4Sb7nbjefjcU2mNLP5Rq/lzsMyrUxV9XpqU13b5GQTchJCBYypqszxQp3fmQvf3+lYeKypAplDTOxvmCMbQf1ruyUCpre9Te0pCWpgnMmlVyYmNpw09edQbuUT8orbXUFKyG4a0zGtvsGCx23PPBElydezeGyo7AoYmC8txXus3s6+BEPiu/t7j1k7sGh8rrkAxeQCg0KdBBdzReV76CK8v+C9eeXzq8f862jegvOwqnoIAwYAa6i5FpkXhCcStGWN7BFl3HRdFW7C1BtoxnFyh90Gu4K/oOHI7bbbfhlsi3WfWldu/L6mKENFRdTh6FYEjNHIhyMQwK0QEUb++w2nRkDV+OI0/h1fIDpTaErw4ON+ztsAPF/hIjvlY9goXa16Ayl/t922KT+0GACI2rnhfQa8eRinq84zwbbyY8CiT5Jz0/JZLPxBZW88rk7dGy/uLs7xmV5rPXjwvT4HvVv/Fr3UVA6bHlW8draCkmZdZ1UkMwrHK0PtPtNPO0c5u848EX9j7IuNyDZY4m7Z8V8mOfQ6rQWEywvIqxltehZSmBbD+GBT5LpaejoJsQEjBZwydgjPVN3GW6qt0ex2WFh1GBcNQrIj2ultuRvIwLcYntIfyhP6fN+1TXWZDvioVZ759iMKTnYWtIn7z1Kjyqf1Dqe72j1IbZ7+Tgqvc34Lqn3sXj+VdilOwAbMowKK76AYjw3YlgVw1MYCd3IgTDUdQe5Wu222qndKX9XtwU+wmErFkBL0K2EmOk68bt37d7X/aeojn0q3S9NmFCUGZZ28JmKLMGDoIVKizf23HLtRX7KjDF+iJWjXgO8FFdi84a0ycaP7kmYnmJBvW29oPDnUUGjHDxiuth/Tpep+wPQ1MisNXF6yvY89a1e9/cijoMAc9wCuvLZ/QDRdAnwCSqoXJZgEq+DW0pLDiC0bL9OMWxptUlLL7WNykKBaL7s7ei7fcG5kglr8CeGe2/GfiUSC0ukK3GTXVvwFG4pd1BlAg7//+ljZVKiPrsfV4NBzSCHZZ26sK43C3FbJrOB6zW+FFSy64Ho59v9edOMx8kdSg8CLplx810u46ddymaLFlRq5QoRrS0tK5BaGT7kyPEexR0E0ICZkK/GCgUSmm0ek87s2t/10RirPUN/HHaEp+9doyep4dV1LW9JutnYQpOsb2MfWM7WjVGSPM2Yg/cdjPeH/ox/um6DbtK6rFqfzlMdhEhgg318aOhuu5XINE3SyV8WcH8ntClWKu5HbbfH2s31ZmVv49NDny6MKsAbMzkZaN0hX8B1rbfN1jv9Il2HmSFDj8X3c30LB7ErNhTCthYNeHW1Zrs0u9SBy36Trnco3Z0/pQcESJ9sYBma35Nu/fdfKAQo2Q8SJP1nYZgYLOiB1R8bbHhwJp277s1rwqJAi+eKUsO7Ex3SqiA3SIf4HUVbWv3vsZ8PmNfq0kFmqzF9Ze+sXocFnnGl6m4/eUQ5SVHEYdqpEeH+LWi+mzFBsyV/w7j4Q1t3o99vieCL0MJ9WEHEvZeWSnw/uPGiraLuQkmnoXgDOl80K3Vh0otu0rtre9P0cpnuh2KjmtcuNzp5aKTz3S7DKU4qL5CWjrQZKIbCrlMGhgMF/gAikEMQYTO/8dZb9Opd/JPPvkEkyZNQlJSEvLyeDrmSy+9hB9++MHX20cI6UFY+5apA1l6t4hN61e1ujbKYndiewE/sRucmez7oNvY9hq/wmp+IpzsTmUjxFPhIUrMv+h0fHfP+XjrilF4/JwhePr6c+Ca+wN0N/8BJAztljvTGcVnBOUVbbcF3VdiaDIzHnijR09AriseCtEG8eCyNu/3zaZ8bHP1hVmmh3JIx1W2A23KgFhMlW3Hq9W3oP67O9q836oD5WATUgPi9dIMX3cwIV2Pa+W/InHx1S3qFjRVs+9PqAUH6tQJQFQfBGspky1xtHRdXdL2rCiz4UgNJlpfxZvDvwWiAtuxIiEE2CdkSkuuKip54N8WVylf9+2ICczyDtZms1SVKl03Fra/5nxg8ffYoLkVZ+c97de6JTVq9yBAKV920ZqiGjM+cZ6ORfLZkMf5dl/VKfnMb3sz3SozD/iFLmTohap5SrjRwvtxt+AeeHSqPAi63TPdot3OH+OwS60fWSG145c53av8Aq8oX5WuG6CTClmSIAfdb775Ju68807MmjULNTU1cDp5qlFERIQUeBNCSHtmDY3HMtU/ceX2KyCW8EJBTW3Jr4bN4ZAKl/SL6/hDxVOJCiPWq+fh8/LzWl0/xwpeFdVYmq0fI6Qzs94zhyZg7oQMDOubAlnfqd1mDXdrtCm8ZkJY/eE2i05lHf0abytfwARr+7OG/jI1Kx4rBZ5iXr3pq1bvY7Y58dOOUjzquAo5/9gIhCagu4nQqpCWFC+tOVfu/wWwt74+dffG5fhR9QDuCW97gCHQxvSNwy2KH9G3+k8gn1dVPx6bCc8pseIP50hY+p0Z1OM+bsB4qWiUw2FvrLHQGlYZn2VxZA3ODnhGgUwAfo6/BSdZ38bq8Dlt3o/Vhkg08UExXVpgWpox5nDeqcBZ3nZ6OfvcDK3j7cI0sf6tql6v40tzxMpDbd6noNqMT52n4+vY24DwFJ++vlXNZ6/thraDbo2NH2uKsK4E3XL8S/E5bjO9AVhbruv+PfQ8ZFvew4as+zt8rn26UfjMcSqqtPxv42hY291K86qLhGXIlvG/ZZ3M/0sYeiOv32FeffVVvPvuu3jggQcglx8bBRk9ejR27Gh5Ak0IIU1NH5SAXHfvx8pN37bYOZv2F2CT+ha8o3kZgr3tFExvRcUkIApGaGCDq5UPzXKjGcvkt+Nb1cNIUPAUK0J6uuSMAahoKDrVyiAYS3UeYt2GM+SbkIK2Tzb93RqvIpP1NgfCc39lbQZa3Gfp7hLUWR3SgNmYvt23EOKICTNQKMZA5TTBta/l8hnWkigmbzGGyXIxVtF2cBFo0wclYpWLV6au28XXzR9ve2ENVln7Y4HsPkRe8CKCaWS/FGkG+2TnW3BqWq/CXFxrRn5VvRT8jk4PTqXmQSks80tATmHbafs7C2sxSuCBr7ZP4NbJy2J4FkxIbdvHIVsqliLywmWRqf7tJe+I5JkIGsPhdou6MRl+WF9uD3EXYXWv227NWscAfO+cCGUXqriHhqhwrXwJLhKXQjS3HDAy2kQYoYVS3/Exuy5yDh5wXIfCCF7U0MUGodi+FFrOYtvAW4veZJuPZ3Udt9ojAQi6c3NzMXLkyBa3q9Vq1NfTiSohpH2hGiWOxJ8mXVfsXMSGypv9vH7vMkQLRvRzHgaUvkutTIjUoRi8omhtccsP7ZKiAqTKyjFcdggKLV+7RUhPNziJFZ3iM1r2/PUtfr6v1IjBQl6zVkzBMGr8NKnlFpu9rDu0rsVs21/LfsJoYS8uGJkspaJ2V2dmJ2GJwKuX16z9qMXPf9iwHxfJVkjXw8Zeju6CFZLKj5ooXbfv+73V+yzO4YMh0wfFSetDg2lgQijM6ljUWZ3YU8yXRxxvw8EyrFTdiY9C32xskxRo2cl8yYa0pKqN4qL7Dh1EmqwcLghACk+bD4TQtGz87ByP37Rnt7ltu4tqMUAolK4r4trvPd9V6nj+/OHmgjarvVcXH8Yw4RD6R7Tf5qxT3CnjclPrQTdbGve+dTrm22+DbtCpXerhbnR3brfV8/ZgTbHBRWlzNB23t1PJeZhnd7qaBd3OJgXTGtgEHnSXixGoD/VdETrShaA7MzMT27a1LPjw66+/YvBg/45yEUJ6hpSJl/BCHdYi2A6tavZhklHlTmHtP8On6YmsIFOFPL7xg/l4hhI+ml8lj+mwLQ4hPUVqVAj2KxuKTrVMG96bV4Q+sob2fcErBDd1QBwWRvwfJrD1t2WDmv1s8ZZDuK32eXytfgw3hixHd8bWSRqzLoZTFBB1dAVwdEuzwQPT+g8RLphg0GUAA2aiO4nKPkPa7kjW/7oqt9nP2LYf2b4KqUIpZmUHP9OABf2jMvhM4PqDpa0GjUW7/0aGrBSjnduCVuk+OyUcdyu+wBvlV8Gx9fNW71NQkIcdrgxU6QcEdDvTUlJwm/0OvGKe2eZncVHuHul4tbOALZa/j/hLfGp/WEUFlKIdqOWB/vEyixfjR/WDmJ3/nM9fX4jMwBZXPxQoWg9IK+ttjYFuw7rsztCpFDCKPOg2GVrOdE+v+QZPK95Fcm379QqYEMGOCBhZ9C5972gn6Ha4g27WqztCS+dA3SLovvvuu3Hrrbdi0aJFUnuODRs24Mknn8T9998v/YwQQjpy2rAMLFPw3rNFK95tvH31vjKcIuODevqhvm9NZGDFfaR+3M1PGJm6Eh6IG933IaQ3YEWnHEk89VBZvLnFz4v2b5Iu69RxgC54fVvZ7PV5M05FFcLw4d9HpNRgabusDtQseQrpsjIYVfHQjb0C3d2ZUyfjexef7a7+5eHG2z9fdxDnWnhBWvXJtwW9avnxpowYiL9dvCCgef0HzX62taAGd1jfxp/qBZjm+BPdweT+sXhK8S4uWTkNKMlp9jN2/qrMXy1dNyROBGTBKRqVFhkCjUKGZKECht1/tHqfX8tjcLbtKRw69+eAZwuwWLuo1tJm1w+7u31XTWh/vw9W940PR57IB86dlS0/w5mIen67MsH3BefElDE43/YY3tbf3OrPK41mqYp7nE4mva925b3OLPAsP0tddYufD7duxj8UKxBp7Xi5z6zy97BNcxNG5r7bvF93K2u6pYETAA8pPsZI165Obz9pm9fv6Ndccw0efvhh3HPPPTCZTLjsssvw1ltv4eWXX8all17q7dMRQnoh1p4CI+dK1xOLfoezplCaKfnp9z+QLFTCLqiBDH5S6kt2PV9L7qouaPEzl7s4iy2UenST3iVu4AQsdJyJD3XXNJsRZIWx5EU8EHfFBb/6+hlD4jE0OUzqFf3mGy+g4PMFWPLyPFxq/076ufLs5wLSw7irshLCkJ99O+yiHCFF62CsLEJBZT30v90ppRFblJFQj+o+qeUNMmN0WBXG19aLWz5uVsV8/bo/pXXo7GRe1W86uoNZ2QmIEQzQi3Wo2/FLi0GCYVYeMEYMnRHUQa+axEnSdRUbBDhuRp511CiutUjrzoem8uVRgRKmUaJfjA7pQglyt7SeQaKv2ildOhM6v4bZU6lRWsxz3o1hlndRFDW2xc+r621IcfF2XuEpg/2yxKKh7kJrDJUlUhX3VbZLWZnwLr1WQyEzW13LmW61i9e6UWg77iYhNgyEOPkMt12uxWpnNnLkLfePQ8aD7kGyAgx0tN8mjnROp4ZRb7jhBqlVWFlZGUpKSlBQUIDrrrsOR4+23buuq6qrqzF37lyEh4dLX+w6q57enquvvlp6Q2v6NX78eL9tIyHEc9Onn4Et4kCoYcOyr9/CT9uP4tIaPhorsv6uSj9UEA/nLVCUxpapaTp3cRaZn9elEdLdjBqQhscdc/FG+TDYnMdO+ncXGTDeyQMT3SBehyGY2Gc4a8c2J+wAHrM+i9R97+NC0yKpBU5l1hXQZLddAbq7uW7OdHwsPxc32RZg6hu7MO/lzzETa+GEDKqL3w9IL+bOGH7qpVIhuHX2fjDVlje2aQrd9Zl0vTplGqDjrZWCLTE8BAcjJ0vXzTubzxL/uXYdxsr2Seuk1Vm8D3ywJGdPg0VUQm8rByoONPvZyt1HEQUDRqVHSm28Au2i6MNYpb4T/f7+Z4uf1Zrt+Nk0GO86ZiE0mw/G+HvJgDymr9TO6mBZy6reuRV16Cvwom7qBN+nurOOKky5wdxqUN3Qv7teFgbIu/a3Mit4QO2sbxl0h7iDbrXWg6UGch5ICy4+w20MzcSV9vvwn5A7W9zVIeO/n3T/kMAO8PQWXcpdiomJQVxcnBR433777ejXjxdj8Qc2o87Wki9ZskT6YtdZ4N2RmTNnori4uPFr8eLFfttGQojnwrUq1M14Htfb7sKNByfgp6/exxR5jrSuSHXG437ZlYr4LKx3ZWGfkNEi1TDOyltlhKYM8ctrE9Jd9Y/TI0qngsXuwo6jxwaz1x4qhxVK2KGEfEBwA5MGrG/1PTdfj/ei78YGxSgcUg5A4WlvIvrS13EiYbOIJ139PAqiJkprQXfYEnFP2H9Qe9rzkPXvHjPFrZk9Ig3X6d/AtZYFeHcrT/H/8tuvcZnwm3Q9ZkrrqbfBEjH8LLhEAbGGXUDJzsaiUpF73YMESacAEXwwNlimDEnBJhcf7K3f0zzFvGzrL9ignocn8EZQti26P2/VF2ktBMzNU533Fhuw1jUEH+iuhy7b98vBWtPQRvRQecugu/hovrS+XCo454ee6/FhGryrfA5rnJfB1koxwfoqnu5tUnU9YLW6g27HcTPdLPtIBx50a/SeBN18pltwz3Q73IOqilaWrnwYezeKRb7tCl1wqvn3dB4H3WxW+fLLL0dsbCySkpLwyiuvwOVy4aGHHkKfPn2wbt06vP/++37ZyD179kiB9nvvvYcJEyZIX6xt2c8//4x9+3jvwrawquoJCQmNX1FRNHpDSHdxyqTJGHn6ZdL15eJIfBo5D85THwFi/TPbrO07EZfYHsLzrn80u73EYEGBKwZFYjRiMnnfYkJ6CzaDPCY9AsOFg3D99lBjKuLaw1W43n43PpuyEoj2/UlsZ6VE6XH97f/G2H8vR98HNiLlZP4ecqIZmRaJX+dPxrMXDMPH147FS3deh6iTr0V3xmYbr5/GU1Nf/GM/7n3lfVya9yDkgojqfudD6B/8jIimpo0eisWucdL1ql/5YO4fOXk428UrxEdMvrlbzMjv1/Oq5IZdfPCCMVrsGFC2RMrkiI8PTq2RoX0zcMTlXkd9tHkR5ZxCXll7cFLHac6+kh1uxiOKD3HS5pYtreqP7pEuq1WJgFLj89eO1Cohk8mgFhyodRdebcpaw4Nuu6brtS9+j70Sk60vYnNK88lFVsNCDz7YFRIa4fFMN1zuAmouHnS31l3ApEloLOCm1HePbJWexuP8B1YobfXq1bjqqqukAHjBggXSpcVikSqXT5kyxW8buXbtWimlfNw4/sbJsDRxdtuaNWswcGDbBRNWrlwpzcZHRERI28iKvrHv22K1WqWvBgYDbzVht9ulr0BoeJ1AvR4hwTzubjw5HadnxUjrpfTqmX7dhsQwVWM6pMVqa/zg2Vdci5vsd6JPjBa/RWbS/70Ao/e84DtzUDQmHXoOsUUGmHfPRE3KVKw9XCn9bGTfRNgdXVuj2B11h+OOzXycN4IHVI4TZB+fMyweh8sy8cXq7Tiv4m0kyKpRo06C/tznu917Z4xWgT0DbsGsg+sRlbcEpVt/xeM/2THbMQeXR+9Dcp/pcAVhm48/9sS+pwE7P0Jc6WrYK48AYclYveMwpgt8eYd+5EVB2bfpkWqsEPogA6Wo2L0KUenHaq3s27kJE2RHMCklKWDbxtZ1z1YshatGgN1U26ytqLOcT8LV6TMR5qftMagTWUNr1JceQsRxr6Ew5PPtCEvp8v4Q9YkoEF0oM8uaPVdtnRlJ7qBbUOk6fB2XwMM8wWmT7qsvWIEc9Z04VDcQdnvzrAqlTEC4wKucq3QR3e7/cnfm6b7yOOj+5Zdf8MEHH+C0007DvHnzpFTyAQMG4KWXXoK/sfT11gLlhtT2tpx55pm46KKLkJ6eLvUXf/DBBzF9+nRs3rxZmgFvzdNPP41HH320xe1Lly6FVhvYNVa//956L0xCeuJxx8eo/YsN8soFubQe68vvf0K4u8/l6mIWfMuhc9bREpQgove84HG5gN8xHpdhKfYtfg1vKeswwXkQh/UjkLvlLxzpvq2vu4yOO++xFbM3ZVQivyYb5vCJMMWNhn1Z96hafrzkEAFLxPGYJazF19/8D0WOS/GLdjYGZs7E9iXHZpaDeeyZrcAvzrE4LCYhbMlfCNeFYvfO9ZgtWFEqi8O6bcXA9uAsjzykHIwzHWth2/E9FosjpdusTmBC8ce4QPUnduzciMV1gekaUFgnokyMQJxQg7+/extV+mOTbr9UJOCAfS6GyMKh8NNS0gqRp3TX5u3E9uNeQ2PMB8tsP1ona/Ezb1WVsOE4Gbbu3o/FpmNFzcrqbLhB4D23f1u5Fk55+zP6tnI+cGqtN0jnNpaCvRgrmKF01rc410kp2IZ4gS8v2rHvMPJLW+9vT1pihcV9GnQXFRU19uFm6eQajQbXX389uuKRRx5pNcBtauPGjdJla+X32TrM9sryX3LJJY3Xhw4ditGjR0sBOBtAOP/881t9zH333Yc777yz2Ux3amoqZsyYgbCwsICNmLA34tNPPx1KJfXKI4HRW467uB1nYoJjE/bFvIJ+p/A0800/7QSOFGHC0D6YNYMKqQVabzn2ursfZC5g61IMNm/EzfXlGKE6iNxB/4eU2Q+iJ6LjrvdYndEHi3+6G+84zoJOLcd7142Tahl0p2Pvpk8TsXxfOU53xOHceDUu23K3lAqhHHEJZp05O2jb+qEyDs717yPVnouESdlSQdLluwoxfjvvbJA1+1ZkpfMK7P5mtjmx9j/9cYZ8I4bGAvppsxpvv2v9cvzlSsXKSyYjOcIPhVgBfFt7FDgMxMiMGDzr2Dp2h9OFnM1PSUF31ugpCBvTtTXutb/9idFlHyBdHosZs55pvH1rfjWyd7yH/uEuLDrrvDb7pzdY9qsJ3248BEf0cJw3axY2/1oKVACCQo1ZTbafScr7Cu5JdEw7/UwkxMZ26XfoTQzurGifBd1s/XbTkyG5XA6dToeuuO222zpsM5aRkYGcnByUlpa2+Fl5eTni4/laE08kJiZKQfeBA82rQzbFZsBbmwVnv3ugTwaD8ZqE9PTjTq4Kgcwpwl6Z3/h7Tj30LG5Xr0GedQGUSiqkFiw9/djr7k4/fTaWbx2D6diIEbKDcECOzHHnsD8MejI67nq+U0dnwzZiMbINFqkKOCsc2N2OvftnD8LK/eX4fU8ZLjn4HDLkpahVJyPq1AVB/T945oTh2LRuIMYJe1Gf8xMipv8firYvk4qW1Skioe8zOWB9ztm+KgrNBkwbYc5dj8gZfL9syjfA4RKRGK5BegzrL+6f1BxtfF8p6A61FDf7rCqtM+F35yiUIQqzM0+CrIt/rySFEacpfkRpTRKUyhcab7c4BRihhSkkFEpVx8dwdcIk3GcPw+nh8bhYqYRMdEq3i4KixWetQ8XblK11DsbImDgolYGvln+i8vS8xeM9ymaVWQuuhoCUreW++eabWwTe3377rVfVz9lXR1jhtNraWmzYsAFjx/LefOvXr5dumzhxosevV1lZKbU3Y8E3IaR3qgvtC5hXQ162o/G2KFOulK5WH+lBNVBCenBHgYF3fI9dvz6Bvoc/heWU+xCRMirYm0WIT6gUMmlNcHfVLy4UN57SF++v2ossWT7qEYKQq78Megs21jHg45i5eK+kEic5p+MauxOReUukn9Wkz4Q+QAF3A3n6WGDP+9CXb+F9zQUB+/buwiXyFVAnTfdbwM1EJvGCkmGuGsBWzxY/S98frTbjPedspEdocXbi0C6/jjqMx0ZaZ/MZ1Horr/ug97B9nFIua6zYz4juIpmuVv5mooL/3zgspGCCigJuf/B4r7ICak1dcUVg1m8wgwYNklp/sf7gb7/9tnTbjTfeiLPOOqtZEbWsrCxpTfZ5552Huro6KX39ggsukILsI0eOSMXgWJDPfk4I6Z2UGeOBsg8QU80rseZVGJHuKpDSwhL6DAv25hESVMlReiRf/h9AfBoaP568EkJauvfMLNw2rQ8MO2SQJfeDKpEv6wy2/hPPwTtf52D35nKYi77CDc6/pM/M2LEXBXxb0oZMhH23HGGOKqAmH4hMh/zgEjyjfBdFtTkA/Ne+LCkhAWucg2GQheEMW71UzIwprOZ52b5Ka9eG84EWnVgPuJyNmQRCxV48pXgXMitr0dzxpKNSBqhhg8zO1xyL7v7iLqGVmVkFn1QNVZwYBR17dNDNiqgF02effYY77rhDWlvNzJkzB6+99lqz+7D2YWz2uyH9fceOHfj444+ldmcs8J42bRoWLVqE0FCeQkEI6X36jJgK13oB8c4SmKoKsXPzDswW6mAWQhCS3PURakJ6BAq4CQkKvUYF/Zg53Wrvz8pOxDNL9qG0xoiB9QsRKjejJnY0Ivr5r3NRW07ql4TdYjoSUAVFUS4iwtOQXLVeGgSQ9Z3q19dOjgzBqY5/SxPsmxGOhhyEivJSJKMcqRG+yaQNjeTFo2UQAUstoHX3z645gssUK5BrKfFse8tXY5/mFhwsZ7VqNh6b6XZXNW8q2sKrr89xLffJ70BaOmHyB1h/7U8//bTDFPgGISEh+O234FalJIR0PymJ8TgoS0N/MQ9Htq6AY+8m6fai6PHo6x7pJYQQQgjH1sF/e8tEvPb5d5hcvhNH9COQcf33gDzwYUSYRokno5/BhmIbXrb3R/+iaozGbulnMcP4xJy/qBVyJIZpUFRrQX6VCdF6fs4Qm78Yf2uew+HiyQB+7vLrRIRqpZ7ZoYIZzvoqyN1Bt8vibmMs96wIoMx9TiN38dnremUkNrkGoEKd2uK+Ia66Lm83aR9P9ieEkF6CrfcqixghXa/b/xcyqv6WrquzzgjylhFCCCHdU1q0Fv+55VLUX/wl0ucvBdTByxod3i+FfZrjs/X5WLnsZ4QJJtQLeiiSeUszf2J1AeRworQor/E2hdHdozs8zSevERGiQi146np9TXnj7aKVB91OpWeFrGVKXmxNLvIZ7kPR03Ch7REsjr2xxX2PjH4QG1wD8UnaEz75HUhLFHQTQnodV7/T8Y3zZPxSEo5sHJRuSxzdvdL5CCGEkO5EJpcjcchkqeVUMF0+Lh1alRyOI+swL/d26TZz+rSAVFE/XbkDOerrMWwNf10m1FQoXSqjMnxW9M8APqhRX3ss6BasRunSqfRspluu4EG3Ajzotjt5RrBC3rJex6TRJ8F+5WLM+cfNPvgNyAmdXk4IIb6SMeECzP0zCjGoRaxiDsZHGjE6Ipl2MCGEENLNZcTo8OSZ6Zi05Cbp+2LtACRe+GJAXjssqT90+VaoDHsAhw1VViDKXiJNY0al9PfZ6zwRchcKau14OXIMGlaKuyw86JZpQj1ukcoo3TPdTpc76Ja1DLrlMgGT+nXcUYp0Hs10E0J6HZYedufpAzBkQD8cHLoAEXM/CfYmEUIIIcRD547Pwo6+N+LPyPMROW8poI8NyL4bNGQkqkU9lLDDUZyDjQdL0F84Kv0sLDnLZ69Tp89EgRiPKuuxUE208nXXCq1n7U0VKt4GTCVapcsRBR9jvXoezix/z2fbSTxHM92EkF7pjlN9NyJNCCGEkMDWZzn1ygcCvssHJ4fjb6E/TsFWlO76E2WVkdAKVhgU0QiL9V3QHaHlqeHVJlvjbWpbtXSp0vPCah1RaPhMt8qdXq501CFeqEGui7c4I4FFQTchhBBCCCGEdIClYVdFDAdqtsJ0eB30tXx9e03SFIT5sNXiKOzBeMUfiMrNA0bPk267x3ETNNYL8fHw6R49h0obgaXOUbDJNDiL3eDiwbcoo/AvGGivE0IIIYQQQogH1Jnjga0fIrJiEz6wzEexTMQ/xlzq032X5dyPMxQ/Ylcpm+meB4vdiQoLSzWPRVRMgkfPoQqNxo32u8CWcM8WRQju1mGBKDhHWqI13YQQQgghhBDigczhp8ApCohxVcAhyvBVxHWIzPZt21FBGyldKqw10mVlPU8zV8oFhIcoPe4rzrD6aVLlcnfQLco8ezzxLZrpJoQQQgghhBAPDEhLwtvqq6A2lWC3mIHrs+J8vt8UumjpUmVzB93VNXhO+RbqlTEQXDNYP7AOn0Ot5HOrrK+41e44NtMtp6A7GCjoJoQQQgghhBAPyGQCrvrn8zhQYsRXLhHDUjyrJu4NRSivxq53VEmXxopCXChfDYtL7XF6uFohQ476OoQJZlRWbW6SXk7hXzDQXieEEEIIIYQQD2lVCgxP4yng/qCKTpcuo5wVgNMBS1WR9L1BEQWNhwXbWIV3l3slsc1cj2p5DHa70mFRUz/uYKA13YQQQgghhBDSTcQkpsEqKiGHC6KhELbaEun2eiVPO/eUTeCtx+w2M36OugqzbE/jYPK5ftlm0j4KugkhhBBCCCGkm0iJ0uOoyGeka4sOQTTwoNuq4WnnnrKDr9+2W8xwsIpqLM2ZlTMnAUdBNyGEEEIIIYR0ExqlHA9r/oUJlldxIGQYZKYy6XanzruibTaB9xF32ExwNgTdcgq6g4GCbkIIIYQQQgjpRsS4wShGNHKrLFBbeNAtC4336jkc7vRyp9WMuWX/xQrVAmSWLffL9pL2UdBNCCGEEEIIId1IerRWusyrrIfWxquYK8ITvXoOu4zPdDttJkQ4KpApK4VaNPtha0lHqHo5IYQQQgghhHQjw3TVSFF8jvR9EbjduQAqawU+GHSaV89xQD0YxVY1dIpI6EVqGRZMFHQTQgghhBBCSDeSqbXiEsVPqKyOQpn5TACxiIr2rpDa11E34s/KCrwYMRwD3UG3IOfF1UhgUXo5IYQQQgghhHQjMakDpctosQpq2CCXCYjU8jXanlIr5NKlxe6CTHRK12UKCrqDgWa6CSGEEEIIIaQbSU5KQp2ogV6w4DvVw9ilGweZbJZXz6FW8vlVq91JQXeQ0Uw3IYQQQgghhHQjaqUCtdBJ1wfL8jBTf8jr57ik4nXsVV+FwQffgZzSy4OKgm5CCCGEEEII6WYOqIdKlwWyFIRetcjrxytkgEawQ3RYUCrEINcVDyj1fthS0hFKLyeEEEIIIYSQbibuwufwzdpfMf3cawB9mNePF+Ua6VJwWHC/5gEcqTfh84RRfthS0hEKugkhhBBCCCGkmxk8YID01WkKdWPQ7XC6pOusIBsJPEovJ4QQQgghhJCeRhFyLOh2idJ1pYzCv2CgmW5CCCGEEEII6WEEJZ/pljmteM3+ELSqOsjr3meNyIK9ab0OBd2EEEIIIYQQ0sMIypDGoDtTzEekzIhD7n7dJLAov4AQQgghhBBCehibLhHrXINQqEiDwt0yTK5UBnuzeiWa6SaEEEIIIYSQHqY6eRrm2yJxcmgMptd8I90mV6iCvVm9Es10E0IIIYQQQkgPo1HyUM9id0IBmukOJgq6CSGEEEIIIaSHUSvk0qXF7oBK4Gu55XKa6Q4GSi8nhBBCCCGEkB4muiYHm9U3oabmWLVyuYLWdAcDBd2EEEIIIYQQ0sOolApEC0bAJcdRMRoKOKFS8TZiJLBOmPTyJ598EhMnToRWq0VERIRHjxFFEY888giSkpIQEhKCqVOnYteuXX7fVkIIIYQQQggJJoU6pDEmmmR9FeOsb0CpCaU/ShCcMEG3zWbDRRddhFtuucXjxzz77LN44YUX8Nprr2Hjxo1ISEjA6aefDqPR6NdtJYQQQgghhJBgUqq10qUatsbb5DIhiFvUe50wQfejjz6KBQsWIDs726P7sxGdl156CQ888ADOP/98DB06FB999BFMJhP+97//+X17CSGEEEIIISRYVBoKuruLHrumOzc3FyUlJZgxY0bjbWq1GlOmTMGaNWtw0003tfo4q9UqfTUwGAzSpd1ul74CoeF1AvV6hNBxR4KJ3vMIHXekN6H3PBIoMjkvmsYql/+oegAmaOBwTIMg0Gy3r3gar/XYoJsF3Ex8fHyz29n3eXl5bT7u6aeflmbVj7d06VJpPXkg/f777wF9PULouCPBRO95hI470pvQex7xN7vdigvd14fJcmERlfiN4gufYlnU3T7oZkXOWgtwm2JrsUePHt3p1zh+JIelnbc3unPffffhzjvvbDbTnZqaKs2Yh4WFIVAjJuyNmK0/VyqprD8JDDruSLDQsUfouCO9Cb3nkUBx2O3IyclElpAvzXbXQk/xhY81ZEV366D7tttuw6WXXtrufTIyMjr13KxoWsOMd2JiYuPtZWVlLWa/m2Ip6OzreCz4DXQAHIzXJISOOxIsdOwROu5Ib0LveSQQx9h5jqcwB6vxoupNFMsSEUXxhU95GqsFNeiOiYmRvvwhMzNTCrzZjPHIkSMbK6CvWrUKzzzzjF9ekxBCCCGEEEK6C41ChkxXsXS9Stn2xCPxrxOmenl+fj62bdsmXTqdTuk6+6qrq2u8T1ZWFr777jvpOkshnz9/Pp566inptp07d+Lqq6+W1mVfdtllQfxNCCGEEEIIIcT/NEo5+gi81pVRzTOBSeCdMIXUHnroIanlV4OG2esVK1Zg6tSp0vV9+/ahtra28T733HMPzGYz5s2bh+rqaowbN04qiBYaSk3hCSGEEEIIIT3bQteDGCHfI123hVDQHSwnTND94YcfSl/tYUXSmmKz3axYG/sihBBCCCGEkN4kCk0Kfeko6A6WEya9nBBCCCGEEEKI50win2O90vYvyMMo6A4WCroJIYQQQgghpAcyuXh1bTXskMvabptM/IuCbkIIIfP6V0IAAAxgSURBVIQQQgjpgayiqjHoJsFDQTchhBBCCCGE9EAT5Luly+tUS4O9Kb0aBd2EEEIIIYQQ0gMdFfg6blvcsGBvSq9GQTchhBBCCCGE9EDKa37EpsH3Ysjc54O9Kb3aCdMyjBBCCCGEEEKI5+LSBiIu7T7Y7bSmO5hoppsQQgghhBBCCPETCroJIYQQQgghhBA/oaCbEEIIIYQQQgjxEwq6CSGEEEIIIYQQP6GgmxBCCCGEEEII8RMKugkhhBBCCCGEED+hlmEdEEVRujQYDAgUVtLfZDJJr6lUKgP2uqR3o+OO0LFHehN6zyN07JHehN7z/KMhRmyIGdtCQXcHjEajdJmamuqrvw0hhBBCCCGEkB4UM4aHh7f5c0HsKCzv5VwuF4qKihAaGgpBEAI2YsKC/IKCAoSFhQXkNQmh444ECx17hI470pvQex6h467nYKE0C7iTkpIgk7W9cptmujvAdl5KSgqCgQXcFHQTOu5Ib0HveYSOO9Kb0HseoeOuZ2hvhrsBFVIjhBBCCCGEEEL8hIJuQgghhBBCCCHETyjo7obUajUefvhh6ZIQOu5IT0fveYSOO9Kb0HseoeOu96FCaoQQQgghhBBCiJ/QTDchhBBCCCGEEOInFHQTQgghhBBCCCF+QkE3IYQQQgghhBDiJxR0dxPV1dWYO3eu1OeNfbHrNTU17T7m6quvhiAIzb7Gjx8fsG0mvfO4a+qmm26SjruXXnrJr9tJepbOHHePPPIIsrKyoNPpEBkZidNOOw3r168P2DaT3nns2e12/Otf/0J2drZ07CUlJeHKK69EUVFRQLeb9L73vG+//RZnnHEGYmJipM/Zbdu2BWx7yYnrjTfeQGZmJjQaDUaNGoU///yz3fuvWrVKuh+7f58+ffDWW28FbFt7Gwq6u4nLLrtMekNdsmSJ9MWuszfljsycORPFxcWNX4sXLw7I9pLefdwx33//vRT0sJNQQvx93A0YMACvvfYaduzYgb/++gsZGRmYMWMGysvLaecTvx17JpMJW7ZswYMPPihdskBo//79mDNnDu114rfjjqmvr8ekSZPwn//8h/Y08ciiRYswf/58PPDAA9i6dSsmT56MM888E/n5+a3ePzc3F7NmzZLux+5///3344477sA333xDe9wfRBJ0u3fvFtmfYt26dY23rV27Vrpt7969bT7uqquuEs8555wAbSXpaTp73DGFhYVicnKyuHPnTjE9PV188cUXA7DFpLcfd03V1tZKj/njjz/8tKWkp/HVsbdhwwbpMXl5eX7aUtKTdPW4y83Nle67detWP28pOdGNHTtWvPnmm5vdlpWVJd57772t3v+ee+6Rft7UTTfdJI4fP96v29lb0Ux3N7B27Vop3WjcuHGNt7E0cXbbmjVr2n3sypUrERcXJ80C3XDDDSgrKwvAFpPefNy5XC5phP7uu+/GkCFDArS1pKfoyvtdA5vNhnfeeUd6zPDhw/24taQn8cWxx9TW1krpvhEREX7aUtKT+Oq4I6Sjz8XNmzdLGWBNse/bOs7YsXn8/dmShk2bNklLa4hvUdDdDZSUlEiB8/HYbexnbWEpI5999hmWL1+O559/Hhs3bsT06dNhtVr9vMWkNx93zzzzDBQKhZSCREigjjvm559/hl6vl9aevfjii/j999+l9Y6E+PvYa2CxWHDvvfdK6cJhYWG040lAjjtCOlJRUQGn04n4+Phmt7Pv2zrO2O2t3d/hcEjPR3yLgm4/YoV/ji90dvwXG01i2PXjiaLY6u0NLrnkEsyePRtDhw7F2WefjV9//VVaa/bLL7/489civfi4Y6OoL7/8Mj788MN2j03S+/j7/Y6ZNm2atBaSjdqzehYXX3wxZfeQgBx7DJv5ufTSS6VsH1asiPRugTruCPHG8cdUR8dZa/dv7XbSdQofPAdpw2233SZ9QLeHFQPKyclBaWlpi5+xAkHHj0C1JzExEenp6Thw4AD9TXoxfx53rAomW8KQlpbWeBsbWb3rrrukCuZHjhzxwW9ATkSBeL9j1aP79esnfbH0zP79+2PhwoW47777urz95MQViGOPBdxskIcVHmLZZTTLTQJ9jkdIe1jWl1wubzGrzc7Z2jrOEhISWr0/y2aMjo6mHe5jFHT7+T+AJ6mPEyZMkNaIbdiwAWPHjpVuY1Wh2W0TJ070+PUqKytRUFAgBd+k9/LnccfWcrNWTcev/2G3X3PNNT76DciJKNDvdw0j8rSchvj72GsIuNmA9ooVK+hklATtPY+QtqhUKqn1F1t2dd555zXezr4/55xz2jw2f/rpp2a3LV26FKNHj4ZSqaSd7WvBruRGuJkzZ4rDhg2TKlqyr+zsbPGss85qtnsGDhwofvvtt9J1o9Eo3nXXXeKaNWukypYrVqwQJ0yYIFWUNhgMtFuJX4671lD1cuLv466urk687777pPseOXJE3Lx5s3jdddeJarVaqqBPiL+OPbvdLs6ZM0dMSUkRt23bJhYXFzd+Wa1W2vHEL8cdU1lZKVUs/+WXX6Tq5V988YX0PTv2CGkNO0aUSqW4cOFCqWr+/PnzRZ1OJ31uMqyK+dy5cxvvf/jwYVGr1YoLFiyQ7s8exx7/9ddf0w72Awq6uwn25nr55ZeLoaGh0he7Xl1d3ew+7E33gw8+kK6bTCZxxowZYmxsrPQfJC0tTWohlp+fH6TfgPSG4641FHQTfx93ZrNZPO+888SkpCRRpVKJiYmJUiDEWjcR4s9jr6FdU2tfbLCbEH8cdwy73tpx9/DDD9NOJ216/fXXpfMy9ll50kkniatWrWr8GYsTpkyZ0uz+K1euFEeOHCndPyMjQ3zzzTdp7/qJwP7x+fQ5IYQQQgghhBBCqHo5IYQQQgghhBDiL9QyjBBCCCGEEEII8RMKugkhhBBCCCGEED+hoJsQQgghhBBCCPETCroJIYQQQgghhBA/oaCbEEIIIYQQQgjxEwq6CSGEEEIIIYQQP6GgmxBCCCGEEEII8RMKugkhhBBCCCGEED+hoJsQQgghhBBCCPETCroJIYQQ0qbKykrExcXhyJEjHu2lCy+8EC+88ALtUUIIIcRNEEVRbPiGEEIIIb3X/PnzpeD6+++/b7ztn//8J6qrq7Fw4UKPniMnJwfTpk1Dbm4uwsLC/Li1hBBCyImBZroJIYQQItm4cSPGjh3buDfMZrMUbF9//fUe76Fhw4YhIyMDn332Ge1VQgghhIJuQgghhNjtdqhUKqxZswYPPPAABEHAuHHj8Ouvv0KhUGDChAmNO8nlcuGpp55C//79odFoEB8fj7lz5zbbiXPmzMHnn39OO5YQQgihoJsQQgghcrkcf/31l7Qjtm3bhuLiYvz2229YvXo1Ro8e3WwHPf300/jf//6Hd955B/v27cO3336LqVOnNrsPmy3fsGEDrFYr7VxCCCG9nqLX7wFCCCGkl5PJZCgqKkJ0dDSGDx/eeDtb352UlNTsviwYnz17trRum0lPT8ekSZOa3Sc5OVkKuEtKSqSfE0IIIb0ZrekmhBBCCLZu3dos4G5Y081SyI9PHX/uuecwY8YMvPXWW6iqqmqx90JCQqRLk8lEe5YQQkivR0E3IYQQQqS08uOD7piYGKlyeVOsmvmePXtw2mmn4dVXX0W/fv2kSuVNNQTisbGxtGcJIYT0ehR0E0IIIQQ7duyQKo83NXLkSOzevbvF3hkwYADuuecebNmyRZrNPv4+O3fuREpKihS0E0IIIb0drekmhBBCiFSVnPXYZmu7dTodwsPDccYZZ+C+++6TZrsjIyPx7LPPStXKx4wZIxVfe++996TbJ06c2GwP/vnnn1L6OSGEEEJoTTchhBBCADzxxBNYtGiRVATtsccek/ZJdna2VL38yy+/lL63WCxSu7BRo0bh5JNPxoEDB7B8+XIp8G7A7vPdd9/hhhtuoP1KCCGEABBEURRpTxBCCCGkNYsXL5bWcbOUcVblvCOvv/46fvjhByxdupR2KCGEEELp5YQQQghpz6xZs6QZ7aNHjyI1NbXDnaVUKqUCa4QQQgjhaKabEEIIIYQQQgjxE6peTgghhBBCCCGE+AkF3YQQQgghhBBCiJ9Q0E0IIYQQQgghhPgJBd2EEEIIIYQQQoifUNBNCCGEEEIIIYT4CQXdhBBCCCGEEEKIn1DQTQghhBBCCCGE+AkF3YQQQgghhBBCiJ9Q0E0IIYQQQgghhPgJBd2EEEIIIYQQQgj84/8BWG6gN4vtO3oAAAAASUVORK5CYII=", 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" ] @@ -533,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "b445d311", "metadata": { "collapsed": true, @@ -547,12 +542,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "dict_keys([(4, 4), (4, -4)])\n" + "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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", 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" ] @@ -596,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 10, "id": "e950c6ea-b5a3-49cb-aa9e-f154615fa72d", "metadata": { "scrolled": true @@ -607,83 +602,83 @@ "output_type": "stream", "text": [ "================ Python ================\n", - "Orbital evolution took: 0.8285874569992302 seconds\n", - "Modes generation took: 0.9951321380031004 seconds\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", - "0.04529686788536343. The merger-ringdown attachment with a quasicircular\n", + "0.057552566100066385. The merger-ringdown attachment with a quasicircular\n", "model might be affected.\n", - "Generating MR waveform from 26.534760077516626Hz...\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 -101.3546327642494Hz to 232.91499581554135Hz\n", - "MERGER mode (2, -2) goes from -1445.6670577642733Hz to 1960.0435620551568Hz\n", - "INSPIRAL mode (2, -1) goes from -40.93098520474169Hz to 48.64173777218727Hz\n", - "MERGER mode (2, -1) goes from -1494.450018807084Hz to 638.6670205710793Hz\n", - "INSPIRAL mode (2, 1) goes from -48.64173777218727Hz to 40.93098520474169Hz\n", - "MERGER mode (2, 1) goes from -904.0487073879406Hz to 1570.650805140606Hz\n", - "INSPIRAL mode (2, 2) goes from -232.91499581554135Hz to 101.3546327642494Hz\n", - "MERGER mode (2, 2) goes from -765.0123834329463Hz to 1422.1370130546898Hz\n", - "INSPIRAL mode (3, -3) goes from -148.29287498947824Hz to 252.27611774527918Hz\n", - "MERGER mode (3, -3) goes from -1838.6690771814121Hz to 578.5158946004476Hz\n", - "INSPIRAL mode (3, -2) goes from -186.96965685318918Hz to 152.28707890607078Hz\n", - "MERGER mode (3, -2) goes from -1907.947484730364Hz to 848.0514925929518Hz\n", - "INSPIRAL mode (3, -1) goes from -96.76611359952858Hz to 1006.143153104066Hz\n", - "MERGER mode (3, -1) goes from -1735.3304783691326Hz to 1461.0162129238377Hz\n", - "INSPIRAL mode (3, 1) goes from -1006.143153104066Hz to 96.76611359953785Hz\n", - "MERGER mode (3, 1) goes from -756.169729782954Hz to 1785.4736608872824Hz\n", - "INSPIRAL mode (3, 2) goes from -152.28707890607058Hz to 186.96965685318918Hz\n", - "MERGER mode (3, 2) goes from -1809.12115116626Hz to 1800.3300719358306Hz\n", - "INSPIRAL mode (3, 3) goes from -252.27611774527932Hz to 148.29287498949677Hz\n", - "MERGER mode (3, 3) goes from -1274.726524678106Hz to 1304.1247413937763Hz\n", - "INSPIRAL mode (4, 4) goes from -391.1483343239296Hz to 292.58362783222134Hz\n", - "MERGER mode (4, 4) goes from -1228.5894136503114Hz to 1539.0787139291294Hz\n", - "INSPIRAL mode (4, -4) goes from -292.58362783222134Hz to 391.1483343239296Hz\n", - "MERGER mode (4, -4) goes from -950.8725340695437Hz to 1285.7310793342444Hz\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: 0.010596448999422137 seconds\n", - "Modes generation took: 0.12539378100336762 seconds\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", - "0.04327354478674823. The merger-ringdown attachment with a quasicircular\n", + "0.058448599740953164. The merger-ringdown attachment with a quasicircular\n", "model might be affected.\n", - "Generating MR waveform from 26.536872604149245Hz...\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 -101.13788122025537Hz to -9.344887798394275Hz\n", - "MERGER mode (2, -2) goes from -1602.2350110337395Hz to 815.9996534588564Hz\n", - "INSPIRAL mode (2, -1) goes from -40.35333777290539Hz to -4.213193305246234Hz\n", - "MERGER mode (2, -1) goes from -1818.9985631079078Hz to 611.6671267875454Hz\n", - "INSPIRAL mode (2, 1) goes from 4.213193305246234Hz to 40.35333777290539Hz\n", - "MERGER mode (2, 1) goes from -950.5339786859888Hz to 1673.0240311976638Hz\n", - "INSPIRAL mode (2, 2) goes from 9.344887798394275Hz to 101.13788122025537Hz\n", - "MERGER mode (2, 2) goes from -836.9570900554431Hz to 1636.8518858814962Hz\n", - "INSPIRAL mode (3, -3) goes from -146.3772907860718Hz to -13.353196079332903Hz\n", - "MERGER mode (3, -3) goes from -1258.1511597478336Hz to 613.3064561359943Hz\n", - "INSPIRAL mode (3, -2) goes from -205.0465309682471Hz to -9.04060303994033Hz\n", - "MERGER mode (3, -2) goes from -1310.1967127427145Hz to 936.6346853426041Hz\n", - "INSPIRAL mode (3, -1) goes from -108.25866887482478Hz to -8.752817115557857Hz\n", - "MERGER mode (3, -1) goes from -1312.746914232343Hz to 1880.0058565994848Hz\n", - "INSPIRAL mode (3, 1) goes from 8.752817115557784Hz to 108.25866887482478Hz\n", - "MERGER mode (3, 1) goes from -1763.1509420447535Hz to 1866.5824036765525Hz\n", - "INSPIRAL mode (3, 2) goes from 9.04060303994033Hz to 205.0465309682471Hz\n", - "MERGER mode (3, 2) goes from -733.1580996279768Hz to 1243.2247832772853Hz\n", - "INSPIRAL mode (3, 3) goes from 13.353196079332758Hz to 146.37729078605327Hz\n", - "MERGER mode (3, 3) goes from -1528.9189421382907Hz to 509.8709756688243Hz\n", - "INSPIRAL mode (4, 4) goes from 18.728407634539536Hz to 302.89715013651397Hz\n", - "MERGER mode (4, 4) goes from -1340.1955570834773Hz to 1547.4317942803186Hz\n", - "INSPIRAL mode (4, -4) goes from -302.89715013651397Hz to -18.728407634539536Hz\n", - "MERGER mode (4, -4) goes from -977.315461742174Hz to 1237.2988196264664Hz\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 = gp.get_imr_esigma_waveform_py(\n", + "hp_py, hc_py = genpy.get_imr_esigma_waveform_py(\n", " mass1=m1,\n", " mass2=m2,\n", " spin1z=spin1z,\n", @@ -721,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "04ba5692-e543-4c24-9110-7406324cee82", "metadata": {}, "outputs": [ @@ -729,13 +724,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2476\n", - "2476\n" + "529\n", + "529\n" ] }, { "data": { - "image/png": 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", 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", 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", 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"text/plain": [ "
" ] @@ -768,6 +763,9 @@ "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", @@ -784,8 +782,12 @@ "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(\"$h_+$\")\n", + "plt.ylabel(\"strain\")\n", "plt.legend(ncol=4,loc='lower left')\n", "plt.grid()\n", "# plt.xlim(0.7,0.74)\n", @@ -796,6 +798,7 @@ "### 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", @@ -827,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "ac35d7b2-0dea-410f-8f59-dfb2c6227345", "metadata": {}, "outputs": [ @@ -835,7 +838,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The mismatch is: 0.0501%\n" + "The mismatch is: 0.2074%\n" ] } ], @@ -843,7 +846,7 @@ "from pycbc.filter import match\n", "from pycbc.psd import aLIGOZeroDetHighPower\n", "\n", - "f_low = 20.5\n", + "f_low = 20.\n", "\n", "tlen = max(len(hp_c), len(hp_py))\n", "hp_c.resize(tlen)\n", @@ -871,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 16, "id": "ca70e4cd-94b4-4ef9-bf3c-6054f4e724c3", "metadata": {}, "outputs": [ @@ -911,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 31, "id": "73c89a53-73d7-4b80-a8df-52a9be1e6443", "metadata": { "scrolled": true @@ -919,9 +922,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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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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yToMurK1m1U6vsZQvBtaeuiJeSem5JmPv3o+X6OcHuqhKZIi7hwYA8HEeH1QLCAhQ1apVj3tSYdULe/LJJ3Xttdea+z744AOTgTVx4sTj1gkDAAC+ac2Mr/Si60UNCampOg2Wu3s4cDNr5oK1OJ1O+YoFW/brPz8naPnOg+Z2WJBDt3Wqpbu61FXlCJpyWIG15/u30Ma9GVq3N13DP1+mT+7q6LNTXwEAnsHjg2obNmxQXFycgoODzfTOZ599VnXr1tWWLVvMNNDevXsffq71nO7du2vOnDknDKrl5uaapVhxEf38/HyzlGTdtoJ3VrF9azkbxRlhxa/jb0pr+63vtV7DOiYlsxU9XfF76uj3lr9g+/33+HPs/ffYu+v4u5Z9ar7ujemm2i6n8l1lG0zx12PrLYYNG2YW6zwvKsq7s7e2JmfquV/W6tfVReVPygU5zBTPu7rWVcVyQe4enkcpFxyg8be2Ud9XZmnOphR9tnCHbu5AR1AAgJ8G1awg2ocffmimdlpdKq3pnZ07d9bq1asP11WzMtNKsm5v27bthK85evToY+q0WaxOl2FhYcfNksvIyFBeXt45bUt6err82bluv7X/s7OzNXPmTBUUFMjbTJ06Vf6M7fff48+x999jfz6Pf0FOuvplzpds0o6QeKVMmlTmPzMrK6vMfwb8m1UjbNzvGzV+xiblOwtlt0k3daipv13SkMy0k7A6nP69dyM98/MaPfvzGvVoVFmxUaHn78ABAPyKRwfV+vTpc3i9efPm6tSpk+rVq2emeV5wwQXm/qNTuq1sppOleT/++OMaMWLE4dvWFcwaNWqYjLfIyMgjnpuTk6MdO3YoPDxcISFnV5PBGo8VULJqvPlj+nlpbb91LEJDQ9WtW7ezPhbuYGUyWP9UWrX+AgMD5W/Yfv89/hx7/z327jj+i794VkE2pzbY66n/LXefl7+3xZnuQFlYvG2/Hv16pTYmZZjb3RpW1j8vb6KGMf5dM/h0Dbqwjn5asUfLdhwwWX4v39Ta3UMCAPgojw6qHa1cuXImuGZNCb366qvNfVbGWmxs7OHnJCUlHZO9VpI1RdRajmad9B994m/V4bBOzO12u1nORvGUx+LX8Teltf3W91qvcbzj5A28ddylhe333+PPsfffY3++jn+hy6XYTV+a9eRGN6lB0PmZDufPxxVlJyffaYJAH8zdKquCRnR4kJ6+qpn6NKvqlxdnz5bDbtMzVzfTFa/O1vfLduvurnX9tokDAKBseVWUx6qFtmbNGhNEq1OnjpmaWXJqiTVFcMaMGWaKKAAA8H3rF09TLdcOZRUGK/7Su9w9HOCsbUxK19Xj/tSEOUUBtevaVtdvI7qrb/NYAmpnwQqiXdUqzqxbgUoAAPwuqPb3v//dBMmspgTz58/XddddZ6ZbDBw40JxcDB8+3DQu+Pbbb7Vq1Srdcccdpi7agAED3D10j2Bl8T3wwAOmsYOVnWdNc+3Xr5+mTZvm7qEBAFAqtiz8xXxdVf4iRZWvyF6FV/pmyU71e/VPrU1MV6VyQXp/UHu9eH1LlQ+jEcG5sGqrBTnsmr0xWXM2Jpfa8QIAwCumf+7cuVM333yzkpOTVblyZVNHbd68eapVq5Z5/JFHHjHF64cOHarU1FTT2MBqOGDV7/J3W7du1YUXXqjy5cvr+eefV4sWLUyNm8mTJ5tuWGvXcsUOAODd0nLyNXxPL1UvaKSxl7R393CAM+Z0Feq/v67VWzM3m9sX1q+k/93QSlUivad+rCerUTFMN3WooQ/nbtObMzerc/1odw8JAOBjPDqo9tlnn530cStbbeTIkWbBkaxAo7V/FixYYGrRFWvatKnuvPNOdhcAwOtZtZJy8l2yVWmsps0oRI6/jBs3zixWfVxPlZ6Trwc/Xao/1u0ztx+4qL7p7Gm32nyi1AzuUkcfz9umGev3aV1iuhpV5eI7AMBPpn96tLzMEy/5OUc+Nz/rJM/NPvXrnqH9+/fr119/NRlpJQNqxazsNQAAvJnVoOCnuSvN+s0dalJzCkewzoESEhK0cOFCj9wz+zPzNODt+SagFhxg16s3t9bDvRsRUCsDtSqV02XNqpr1d2YVZQQCAOAXmWoe7dmiwqfH1aC3dEtRJzJL1JttZCs4KnhWrFYXadDPf90e21zKSjnyOSMPntHQNm7cqMLCQjVu3PiMvg8AAG+xZtHv+vDAQH0f1E29W588sx3wJIkHc3Tbu/O1ISlDFa36aXe0V8saXPAsS1b3z0krE/Xdsl165LLGqhwRXKY/DwDgP8hU80FWQM1C63UAgK/KnPW6gm0FqlspROXL8Q8yvMOeg9m6/s05JqBWNTJEXwy5gIDaedC6ZgWzn/Odhfp+2a7z8SMBAH6CTLWz9cTuEz9mcxxx8+CQJYqMiJDdfpwYpu2o+4YXTWU5Fw0aNDABtTVr1ujqq68+59cDAMCTJO3erpZp0yWbVLHHMHcPBzjtKZ+3vjNfO/Znq2bFMH1yV0dTSB/nx/Vtq2v5jgP6ctFOU2eNi88AgNJAptrZCip34iXwqI5NgWEneW7oqV/3DFWsWFGXXnqpKdCbmXlsTbYDBw6c8WsCAOAp1v86TkE2p9YHNlbdll3cPRzglDJyCzTo/QXatC9TsVEhmng3AbXzrV/LOFO/bt3edK3cdWalVQAAOBGCaj7q9ddfNx2vOnTooK+//lobNmwwmWuvvPKKOnXq5O7hAQBwVnLzclV/e1Hd0syWd7AX4fEKnC7d9/FiLd95UBXCAvXR4A6qXoEMtfMtKjRQlzYtaljw1eKd5/3nAwB8E0E1H1WnTh0tWbJEPXv21MMPP6xmzZqpV69emjZtmsaPH+/u4QEAcFaWTf1UVZWi/YpUs14D2YvweP/9da1mbUhWWJBDEwZ1UP0qEe4ekt+6vl118/WH5buV73S5ezgAAB9ATTUfFhsbq9dee80sAAD4gsBlH5qvm2r0V/tgsn3g2ayi+G/P2mLWX7y+JU0J3KxzvWhFhwcpOSNP8zfvV5cG0e4eEgDAy5GpBgAAvIJVZHxw+t163nmz6l72gLuHAw9m1ZWNj49X+/bt3TaGNXvS9OjXK8z6fT3qqW/zWLeNBUUcdpt6xceY9V9X72G3AADOGUE1AADgFd7/c4tSFandTYeoUrV67h4OPNiwYcOUkJCghQsXuuXn5+Q7df/EJcrJd6lrg2j9vXcjt4wDxyquqzZ59V65XIXsIgDAOSGoBgAAPN6e1Az9tKIos+TOLnXcPRzgpJ77Za3p9Fk5Ilgv39TaZEjBc6aARgQHaF96rpbuSHX3cAAAXo6gGgAA8Hhrv3lWnwX8W4PjtqpF9fLuHg5wQnM2JmvCnK1m/fnrWqhiuSD2lgcJCrDr4iZVzPqvqxLdPRwAgJcjqAYAADxaZla24nd8qnb29bqmHhk/8FzWtM8nv1tl1gd0rKmejYqCN/DMKaDT1iS5eygAAC9HUO00FBZSb8HdOAYA4L8W/zJBMdqv/bbyatLrTncPBzihN2Zs0pbkTFWJCNZjfRqzpzxU5/rRZkru5uRM7UzNcvdwAABejKDaSQQGBpqvWVn8sXW34mNQfEwAAP7B6XSp8up3zfr2ujfLERTi7iEBx7UtJVOv/7HJrD/VL16RIZyzeKqo0EC1qlE0jXzWhmR3DwcA4MUC3D0AT+ZwOFS+fHklJRWlhoeFhclmO7NpJy6XS3l5ecrJyZHd7n8xzHPdfitDzQqoWcfAOhbWMQEA+I9Fs39VR9cG5SpQDa94yN3DAU7o+cnrlOcs6vZ5efNY9pSH69agshZvS9WsDft0c4ea7h4OAMBLEVQ7hapVi2ouFAfWziYolJ2drdDQ0DMOyPmC0tp+K6BWfCwAAP7DNWec+bquch+1qECgAp5p+Y4D+nnFHlmnOk/0beKX53zepmvDaP3vt/WavSFZTlchHVoBAGeFoNopWCdFsbGxqlKlivLz8894B1vfM3PmTHXr1s0vpy6WxvZb30eGGgD4n4SEleqQ86dkk+L6jHD3cIATXkAc/csas35N62pqEhvJnvICLapFKTIkQGk5BVq+84Da1Kzg7iEBALwQQbXTZAV1ziawY31PQUGBQkJC/DKo5u/bDwA4e28ty1F+/gO6vuoe9ajbml0Jj7Rga6rmbd6voAC7Hu7dyN3DwWkKcNh1Yf1o/bIqUbPWJxNUAwCcFf8r8gUAADze7gPZ+nHVPv3sukDR/V9y93CAE3pj5hbz9YZ21VWtfCh7ysu6gFrmb0lx91AAAF6KoBoAAPA4H8zZauocdapbSc2qRbl7OPAy48aNU3x8vNq3b1+mP2dHhjR7Y4qpxzWkW70y/VkofR3rVDRfl2xPVV6Bi10MADhjBNUAAIBHScvKVq/5g3Sf4wfd0ynG3cOBFxo2bJgSEhK0cOHCMv05v+0qOpXu1yJWNSqGlenPQumrXzlc5cMClZPv0qrdB9nFAIAzRlANAAB4lPk/T1A72xrdG/Szujeo7O7hAMeVkpmnValFXT7v61GfveSF7Hab2tcuylZbuGW/u4cDAPBCBNUAAIDHyMkrULWEt836zvq3yh4S7u4hAcdVqVyQ/tnaqWeuilejqhHsJS/V4VBQbQFBNQDAWSCoBgAAPMac375VfOEm5ShIDa4Y4e7hACdVIVi6sV119pIX63CortqibalyuQrdPRwAgJchqAYAADyC1Zig3OLXzfqm6tcoKKqKu4cEwMc1jYtUWJBDB7PztT4p3d3DAQB4GYJqAADAI8ydM0MdnUvklE11rnjE3cMB4AcCHHa1rVXBrC/cmuru4QAAvAxBNQAA4HaFhYXKnzXWrG+odInCqlL4HcD50bpGefN12fYD7HIAwBkJOLOnAwAAlL65m1L0QlovZQTmqWsfstQAnD+tah4Kqu0gUw0AcGbIVAMAAG43fsYmJRTW1sK2z6t8/Q7uHg4AP9KyelFQbdO+TFNbDQCA00VQDQAAuNWqXQc1a0OyHHab7u5al6MB4LyqFB6smhXDzPqKnUwBBQCcPoJqAADArTZ88396LuAtDWrsVI1D/9gCwPnUirpqAICzQFANAAC4zfrte9QzeaJuCpiuwXXJEAHg5qDaDn4PAQBOH40KAACA2yT8MEYNbZnaG1hdsZ0HcCQAuLlZwQHTjdhms3EkPIzTVaik9BylZRcot8CpkECHKpULUsVyQRwvAG5DUA0AALjFpl1J6rLvM8kmFVw4QrI7OBIA3CI+NlKBDptSMvO0MzWbqehuZgU2t6Zkmc7QC7fu17rEdG3al6HcAtcxzy0fFqg2NSvokiYxurxFrKJCA90yZgD+iaAaAABwixU/vKJrbGlKDqiqal1v5yig1IwbN84sTqeTvYrTYmU9NYmN1IqdB7Vy10GCam4KpC3feVCTVu4xixXcPFqA3WaCZkEBduXkO3UgO18HsvL1+9oks4yetEYDO9fWvT3qKTyYf3UBlD1+0wAAgPNuy9796pT4sclSy7ngIclBZgFKz7Bhw8ySlpamqKgodi1OS9O4KBNUszoS920ey147T3YdyNbnC7brm6W7jgikBTnsZlpup7qV1LxalOpXCTfBTqtTdLHsPKc2JmVo5oZ9+m7pLm1IytBrf2zUD8t36383tlLbWhU4jgDKFEE1AABw3i3+7jVdZ0vVfkdlVe8xmCMAwO2aVYs0X61MNZQtl6vQZJZ9Mn+bpq/fp8LCovvDghy6qHEVXdEiVt0bVlFo0MnLAliPN68eZZb7utfTlIRE/d9Pa7R9f5ZueHOuXry+ha5pXZ3DCaDMEFQDAADn1ZbkTD27rbF22q/VNV0uUMWAYI4AALdrFleU1bh6dxrNCsqINWXz26W79PbMzdqcnHn4/s71KunmDjVNXbRTBdJOxG636bJmsepcP1pPfLNSP63YoxFfLFdegUs3tq9ZilsBAH8hqAYAAM6rMVPXa7+rnJY3uE/DL+nA3gfgERpVjTA1u/Zn5mnPwRzFlQ9195B8xsHsfH08b5ve/3OrkjNyzX2RIQG6sX0NE0yrWzm81H5WZEigXrmptSqEBemjedv02DcrVSUyRD0bVSm1nwEAxQiqAQCA82b1zhT9uHyXrGJqf7+0EXsegEc1K2gQE6E1e9JMXTWCaucuI7dA78/eordmbVZ6ToG5Ly4qRIO71jUBtbJqJmBlrT19VVMVuFz6dMEODf9smX56oAsNKACUOoJqAADgvFn11XP6Juh3Ta/1kCkKDgCepFlcZFFQbXeaejet6u7heC2rgcBH87Zq/PRNSs3KN/c1jAnXfT3q6YoWcQp02Mt8DDabTSOvbKo1e9K1bMcBDf1kib4d2lkB5+FnA/AfBNUAAMB5sXj9NvVKnaiK9gzVaFiUsQAAnqRZtSh9uXinyVTDmbPql326YLvpwLkvvWiaZ93ochreq6GuaB5rMsjOp+AAh8bf2kaXjZ1lGlB8OHeb7uxS57yOAYBvI6gGAADKXGFhoTb98Lza2jKUFFxTVTrfzl4H4LEdQAmqnfnv+KkJezX6l7WmGY2leoVQDb+koa5uFefW7LDYqFA9elljPfHtSr00ZZ36No9V1agQt40HgG8hqAYAAMrc7JXr1Sf9a6uUmgIv+Zfk4BQEgOdpEhspm01KSs9VUlqOKXCPk0vYnaZnfk7QnE0p5nZ0eLCGX9JAN7SroaAAz5hqeVP7Gvpy8Q4t3X5Ao39Zo5dvau3uIQHwEZ7xWw4AAPisAqdLe396VhG2bCWGNVSFtte5e0gAcFxhQQGqd6gT5erdaeylk0jLk578brUuf3WWCahZAbShPepp+j966NYLanlMQM1iTTv9v6uamfUflu/WxqR0dw8JgI/wnN90AADAJ/00Y46uzP3RrIf3HWX9d+PuIQHASZsVWJgCenxOV6E+WbBDzy5z6IvFu1RYKF3RIlbTRnTXI5c1LrOOnqVRL693fIwZ76u/b3T3cAD4CM/8jQcAAHzCwex8HZj1loJsTu2q1FnVmvZx95AA4JTBl++W7daq3TQrOJoVaHzyu1VavuOA1V9TTeMiNOrKZmpXu6JXvKsevLiBpiTsNdlqD1zUQPWrFGUl4vzV3ktMyzEddvem5SotO9/U24sICTDHonHVCJMtCngT3rEAAKDMvDptg97Nvl57y9fRw9ffKFOsCAA8PKhmWbWL6Z/FMnILNGbKek2Ys0WuQqlcsEOXxebpP4MuUEhwkLzp2PaKjzFNFd6euVn/va6Fu4fk8w5k5em3NUn6LWGvFm9PPdwV9niCA+y6qHEV3dShpro1iJaNcwZ4AYJqAACgTFgd4CbM2apC2XXBtfcroGpl9jQAjxd/aPrnrgPZSs3MU4Vy3hM0Kgt/rEvSE9+s1J6DOea2NdXzsUsbaPHs3+Wwe9+FkiHd6pqg2vfLd+mJvk0UFRbo7iH5ZC3VaWuT9OmC7Zq1IdlMGS5mvWfqVw433WGjQgNV4CrU/sw8rU1MV3JGrn5ZlWiWC+pWNFmQjapGuHVb/In1+2793nSlZuUr3+lSpXJBqhVdTnFRIQQ4T4KgGgAAKHVWzZpPvv5awa4odW1UU90bElAD4B0iQwJVq1KYtqVkmWYFXRpEyx+l5eTrPz+t0eeLdpjbNSuG6f+ubmZ+n+fn58tbta1VwUwztII4VkfQu7rWdfeQfOo989Hcbfpw7lYzvbOYtb97N61qss+axkUpNMhx3Kmh1uft6yU79cn87Zq3eb+uGjdbz17TXNe2qX6et8Q/WPt8yfZU/bh8j6avS9LWlKzjPq9udDn1aV5VAzvXVpUIOiIfjaAaAAAodWv2pulv+/6l+4KD5epW1KQAALxF07jIQ0G1g34ZVJu1YZ8e/WqFdh/MMbP2B3Wuo39c2ui4wRBvY00pvL1TbT3x7Up9PG+b7rywjukOirOXkpGr9/7cog/nbFN6boG5z8pyuq5ddd3YrobqHuqoe6rjYk3PtRYr0GllR85Yv08jvlhuPot/69WQQ1RKrMzB75bu0lszN2vd3iM74daoGGoCZwF2m5mqu31/ljYnZ2rcH5v03uyturtbXQ3pUotjUQJBNQAAUKoOZOUrftfnKm/P1L7w6qpauzF7GIBXsbJpJq1MNJkz/lY77dlJazRx/vbD2WkvXNdCHetWki+5qlWcRk9aYzJz/tyUrK4NyKY+2/fL+9O3mPp02flOc1+DKuG6r0c9XdEiTkEBZ9ftu1r5UL1/R3uNnbZBr0zboJenbVBYkENDutc7q9fDX5lpP6/cozFT12vzvkxzX0igXX2bx+rSplXVuV4lRYQEHnOMf1+bpPdmb9GyHQfM8Zi9YZ+u8r9rDSdEUA0AAJSqb779TEPss+SSTeWvHyc5ON0A4H2ZahZ/6gC6YucBPfjp0sNTwAZ2qqVH+zT2yW6M5YIDdFXrOH08b7u+WbKLoNoZsuptzU606en/zVZKZp65r0X1KA3rWV+9msSUSuaf9RojejU0zQtemLxOo39Zq2oVQk2wDmdu874MPfntKs3dnGJulw8L1JBu9TSgY01T2+5EwoMDdGXLOPVrEaufVuwxGZ5Lth/Qxj0OXdgtS/WrFjV28We+9xsSAAC4zdx1u3Tx5hcku7S3wQDF1mrP0QDglZlqxQ1XMnMLTBDGV7lchXp39hY9P3mt8p2Fpij5ize0VOd6vp2Kck3r6iao9uuqRD1ztW8f49I0bc1e/efnBG1OtqYC56l2pTA9elljXdasapkUs7cCdQez881Uxce/XqkW1cqrZqWwUv85vtw04o0Zm/TKtI3Kc7pMZpoVTLura51jstJOxjq2/VrGmeDp4AkLtXFfpm5/f5G+vK+zySz0Z2eXjwkAAHCcAsWbv3hc9e27lWqLUvSVT7OPAHilyhHBiokMNk1X1uzx3SmgVs2kQRMW6j+T1piAWp9mVfXLQ918PqBmaVOzvJneak1btLqB4uR2pmbp7g8XafAHi7Q5OUvlAgr11OWNNXVEd/VpHlum3SEfubSR2tWqYOq13f/pEpMph9M7Zje9NU8vTllvAmrdGlbWlOHdTX26MwmolVSrUjl9dGc7VQkpNDUXB72/QNl5RVN//ZXXBNVGjx5tPqjDhw8/Yk7wyJEjFRcXp9DQUPXo0UOrV6926zgBAPBXH372mW4u+MGsr6p1pxTClAC4x7hx4xQfH6/27cmUxLlnq/lqXbX5m1PU5+VZphi8NcXO6rL4+i1tFBV2dv9sexvrf8urW1cz698t2+Xu4XisvAKXxk/fpF5jZprgo1XA/q4utfVUa6duu6CmAh1lH1IIcNj1ys2tzTTFFTsPmg6jOLlfVu4xn+9F21LNFM4xN7TUB4Pal0qWX3R4sIbFO1U5PEjr92Zo5A/+HYPxiqDawoUL9dZbb6lFixZH3P/8889rzJgxeu2118xzqlatql69eik9/cgOFgAAoGxZJ9rvrg3QL64OSqrXX/srtGaXw22GDRumhIQEc34InK1mh+qqWR1AfYmVmGBN9xzwznwlZ+SqYUy4fnygi6mtVJbZRp7o6lZF9blmbUg2+wJHWro9VZe/Mkv//XWtyejrULuifn6wqx69tKFCzvNs2bjyoXqsT1Hjo/9NXW+yLHH86dwvTF6r+z5ZovScApOR+ctDXXVtm+ql+vkuHyy9dH1z0x3480U79L0fB6Y9PqiWkZGhW265RW+//bYqVKhwxB+DsWPH6sknn9S1116rZs2a6YMPPlBWVpYmTpzo1jEDAOBP9hzM1iNfLVeqIrXigrGqcP1r7h4SAJyz+EOZaqt2+U6mWlZegR76bJn+76cEOV2Fpgvm98O6qGFMhPxR3crhal4tyuwLpoD+JSffqdG/rFH/8XO0ISlDFcsF6cXrW+rzIReoUVX3vVduaFfD1PSypoFagT4cW4bjrg8Xadwfm8ztu7vW0RdDOqlGxbKpQdepbiU90LO+Wbd+p1i17/yR3RuuNF5++eW65JJLjrh/y5YtSkxMVO/evQ/fFxwcrO7du2vOnDluGCkAAP7Hqmvynw9/VmpWnumW97fejSSHf0wdAuAfHUA3JKUrt8D7awZtS8nUta/P0Q/Ld5spfP/uF6+xN7ZSaJBVcN5/Xdo0xnydvDrR3UPxCMt2HNAVr87WmzM2y1VoNXSopmkjuuu6tqWb6XQ2HHabRl3Z1Kx/vWSnNiZluHU8nmRrcqauHvenfl+bZKZzW5/tJy+PN1Nny9L9FzVQvcrllJyRZzII/ZFHtzj57LPPtGTJkuOm7lsBNUtMTNEvwWLW7W3bTjzHOjc31yzF0tKKrjzl5+ebpbQVv2ZZvLY3YPs5/iXfB/7Gn9///rzt/rb9b3//m55LHqYrglurwbXvyyGXX2y/L28bgCLVK4SaGk5W9sWGvRlqVs1760Qu3Lpf93y4SKlZ+aYeklU7rUOdiu4elke4tGlVU8h9zsYUpefkn3UBd1/IThv72wa9NXOTCaZZzTqsOnu94o/8f9vdWtesoN7xMZqSsFev/b5BY2+i3MTyHQd054SFSsnMM91737ytnZpXPz+/r4IC7Bp5ZVPd9u4CfTh3q27qUEONqxZdkPAXHhtU27Fjhx566CFNmTJFISEhJ3ze0dFya1roySLoVsODUaNGHXO/9XPCwsquNe/UqVPlz9h+jr8/8+f3vz9vuz9s/5rkfF21bZTC7TlqHJyqFYvmapXN4Rfbb5WbAODbrP8prGy1OZtSTF01bw2qWbWO/vHlCtP9z5o699Zt7VQ16sT/X/mb+lXCVTe6nDYnZ+qPdft0ZcuiOmv+ZMPedD3w6VKtTUw/XGvOCpSUDwuSJ3rw4gYmqGZlXVrr1jRef/XHuiQN/XiJqXnXrFqk3rujvapEnN/Pd9cGlYs6B69KNNlqVlDPn3hsUG3x4sVKSkpS27ZtD9/ndDo1c+ZM05hg3bp1hzPWYmNjDz/H+p6js9dKevzxxzVixIgjMtVq1KhhppFGRkaWyZVs658Kq4FCYKD/XfVg+zn+vP/98/PPZ9/3P/trdqfJ9t5ANbFvV0ZABVUb8pWqRVT1m+NfnOkOwLdZgbSioJr3featZINXf9+oMYemZFnTHMfe2Nrvp3seL3jau2lVvTFjk5kC6k9BNes98tnCHRr142rl5LsUHR6k/1zT3GTvefrn8pImVfTbmiS9Pn2Tqffmj75ctEOPfbPS1ATs2iBa429tazp9usPDvRvq19WJmrx6r9YmpvlVtprHBtUuvvhirVy58oj7Bg0apMaNG+vRRx9V3bp1TbdP66S9deuilM+8vDzNmDFD//3vf0/4ulbdNWs5mnXSX5Yn/mX9+p6O7ef48/73z88/n33f/Ownpedo7of/1BDbLDllV8hNExRQsYZfHX9f3S4Ax6+rtmrXQa+rd/nY1ytN3SnLPd3q6rHLGstu96/unqfLCjhaQbXpa5PMNMiQQN+vM3cwK1+Pf7tCk1YWlVWygjIv3dDyvGc5na1hPeuboNoPy3abrqDWtGZ/Yk21fOr71Wb92tbV9Fz/FmYqprvUrxKhvs1i9fPKPXrt9416bUAb+QuPDapFRESYjp4llStXTpUqVTp8//Dhw/Xss8+qQYMGZrHWrSmcAwYMcNOoAQDwbdY/GxPeflmPOIs6bef1fl6h9Xu4e1gAUKZBtTV70k02iFUo3dNl5zk19JPFZiqjNd6nr2qqWzrWcvewPFrL6uUVExmsvWm5mrMpWRc19qw6YqVt8bb9evDTZdp1INs0rXjkska6q0tdrwq6WrXVWtYob+qJfb5whwmy+Yt3Zm3WMz+vMeuDu9TRPy9v4vYmEpb7L6pvgmrWMmJfht9My/X47p8n88gjj5jA2tChQ9WuXTvt2rXL1EazAnIAAKD0Mx/+9tGfuuvgK+b2wRaDFdr5bnYzAJ9VJzpcoYEOU69oS3KmPF1aTr4GvrfABNRCAu16Z2A7AmqnwQom9Y4vmvI4edVe+fJ0Tysgc8Ob80xArValMH19X2fd062eVwXUig3sVBQs/njeNhU4XfIH46dvOhxQG9qjnscE1CxNYiN1ceMqKiy0jsl2+QuvCqpNnz5dY8eOPXzbevOMHDlSe/bsUU5Ojpn6eXR2GwAAOHcuV6Ee/mK5flmfrgddDyulTj9FXfU8uxaAT7MyvZrEFl2wt5oVeLJ96bm66c15WrB1vyJCAvTR4I7q2aiKu4flNYrriP22Zq/JSvQ1mbkFphmBFZCxts+qHffTA11Mtpe3urxFrCqVC9KegzmmcYGve2XaBv3317Vm/aGLG+gflzbymIBasVsPBTq/WrzDzG7wB14VVAMAAO4JqP3ru5Wmy5Y1TWTwLbeq0sCPJYfHVpEAgFJT3PXTk5sVJKXl6Ka35iphT5opNv/ZPReofe2K7h6WV+lYt6IJRqZk5mnZjlT5EivL8prX/9RPK/aYv+Ojrmyql29qpYgQ764PGhzg0E0dimq6Wg0XfD2gVtx0xAqm/a1XQ48LqFm6Nais6hVClZZToB+X75Y/IKgGAABOGlAb8/kvum7ZIDWy7zBFjHs2JvMBgP/w9GYFVvOYm9+ep037MhUbFaIvhnRS07iiQCBOX6DDrh6HMvusAvi+YmrCXl356myt35uhyhHBJuA6sHNtjwzInI0b2hUF1WZv2KfEgznyRdaU3eKA2uN9Gnt0/TiH3aYBHWua9U/m+8cUUIJqAADguKzpIS988qNuXTtUre0b9UnMRF3VMo69BcCvFAeorEw1qyaVp035HPD2/MMBNStg4i/FwcvCJU0OBdV8YCqh9V4d+9t63f3hIqXnFqh97Qr6+YEuaudjGYy1KpVThzoVZc3YLe5260s+W7D9cA21Eb0aakj3evKGQGegw6ZlOw5ow950+TqCagAA4BhWHYzRE77SnRuHqaotVWmRDRQ9+CuroCl7C4BfaRgTYf5BPJidb4q7e4rkjFyTobYxKeNwQM0KMODs9WhYxUyP3JCUoW0pnt+Y4mR/wx/6bJnG/rbB3L6jc21NvPsCVYkMkS+6rm118/XrxTs9LvB9LqyyG49/u9Ks39Otrh64yHMz1EqKDg9W94aVzbo/TAElqAYAAI5wICtP/339LT247QFVtqXpYPl4RQ6ZLIUz7ROA/wkKsKtBlaJmBat2pXlMl8/b311AQK2URYUFHq5F561TQK3sRSvYWlwH9blrm2vklU3N9FZf1bd5rOnSuzk5U0u2+0Y9PCtbcsTny0wnzVs61jTTPr1pym6/QzMbflyxx6cCncfju58sAABwxranZOmNV57V4/ufVKQtWwerdFDUkElSuUrsTQB+q/mhZgUrdh7wiCykuz5YdLgpgZWBRIZa6bkkPsZrp4CuS0zX1eP+1NLtBxQZEqAP7+ygmzoU1bfyZeHBAerTrKh76/fLvD8zatHW/Ro2cYkKXIW6ulWc/u+qZl4VULNc0iRGIYF20yTDUy5GlBWCagAAwJi5fp/6vTpL7TOnK8jmVFr9KxV1z09SaAX2EAC/1qpmefPVCla4U4HTpfsnLtWCLfsVERygD+7soDrRTPksi7pqC7bu18GsfHmLORuT1X/8HDNFuXalMH037EJ1rh8tf9GvVVFm1KSVe8znxFtt2pehuz5cpNwCly5uXEUvXN9Sdrt3BdQs5YIDdHGTogD1jyu8P9B5MgTVAADwc1Za/vjpm3TH+wt0MKdA71V5TOld/qXIAR9IAcHuHh4AuF2bmkUXF5bvPGCauLjrd/Vj36zUb2v2KjjArncGtqPLZxmwsv4axoSb4zx9vXdMAf1pxW7d8f5CZeQWqGOdivp26IV+17CiS/1olQ8LVHJGnuZt3i9vZHXyHfjeAh3IylfLGuX16oDWXj1tt1+LokDnzz4+BdR7jxAAADhnVuHt0e99Ludvo+QqLNRN7Wvovft6KeKSv0t2ThMAwFK/SriZYpaV59R6N3Wzs4rOf7V4pxx2m14b0EYd6zItv6wUZ9h4Q121CX9u0QOfLlWe06W+zavqw8EdVKFckPyNFXzq0yzWa4vjZ+YWaPCERdqZmq1alcL07sB2CgsKkDfr0aiymQJqZU+u8+EuoJwtAwDgpxZuSdaElx7Rw9uH6f6A7zWx/WY917+FggMc7h4aAHgUK5DVqkbRFFB3FEL/dulOvTytqJPjM1c3U69Ddb9QdvWgLNPXJSnfQ6cSWpk/L0xeq5E/Jphi9rd3qqVXb27j13/D+7UsCqr9smqP8go887ideFr3Eq3cdVAVywVpwqAOpoOmtwsJdKhzvaIpyNO8IEB9tgiqAQDgZ6yTt7d+nqPs96/WQwXvKdhWoIM1L1Hnvre5e2gA4LFau6mumlU/7dGvVpr1Id3r6mY/KDzvblYA1WoCkZ5ToIVbPG8qoTU19YlvV2rcH5vM7Yd7NdSoK5ua4K8/61inkipHBCstp0CzNuyTtxj542r9sW7f4WndvlQnsWfjohqFf6wlqAYAAHzA2j0H9eLY53XdguvVzb5S+bYg5Vz6gqIGfUVDAgA4jbpq5zNTzeqcd89Hi8zUPqu74aOXNj5vP9ufWcGpiw4FA6au2etxF8b+/uVyfbpgh6wY2uhrm+uBixt4XXfIsjpulzf3rimgH83dqo/nbZd1+F65ufXh3zO+4qJDnyPr92ZqZp58EZlqAAD4AWsaxJip6zX39Xv0WPpoVbRl6EBUYwUOna2QTvfInM0BAE6oePrn5n2ZppB4WbOKzt/94aLDRcvH3NDKK7sAen9dtb0eU2Td+ltu1U/7dukuBdhtJghD5uKR+rUsKo4/NWGvcvKd8vSOrdb0Xcs/Lm2kS5tWla+pVj5UjWIiZPV3melF2YNngqAaAAA+btmOA7ri1Vl6ZdoGTXG2VYEClNFxhMo/MFOq3MjdwwMAr2AVf697aFrWkh1lOwXUCuI8+tUKbUzKUExksN6+va1Cg/y3VpY7dG0QraAAu3bsz9aGpAx3D8cEiO77eLF+WZWoIIddr9/SRlcc6q6Iv7SpWd4EcjLznB495XBrcqbu+2SJmcp7Tetquq97Pfmqnoey1X734ONxLgiqAQDgo/Zn5mnsJ9/pzTf+p/V7M1SpXJBuuelWOUasVHiff0sB3l8EFwDOp+KOm3M3pZTpz3l39hb9vHKPAh02vX5LW1WJCCnTn4djWZ0Xu9SPPpz15E7ZeU6TtThtbZKpu/X2wHbq7YNZTaXBmgZ7RYtDU0BXeOYU0LScfA3+YKHpwG5lwFpTeH15+m73hpUP/970lKzP0kRQDQAAH2Nd9fxi5gpNeeFWPbD+Dr0YMF53NrVr6oju5qq2LZIr2wBwNi6sfyiotrnsitfP25yi0b+sNev/uiJebWv5Vo0lb+wCak0BdWeGmlVXb9aGZIUFOfT+oPaHgxQ4vuIMPiszKjO3wOPO0R78dKk27ctUbFSI3rq9remS6etNXoIC7EpKzzV1In0NQTUAAHzI4i37NP7FJ3TJtD66SZPlsBUqp/ZFeurKFqZNOwDg7HU6lKm2bm+G0sugrFriwRzdP/GvKWG3XVCr9H8ITtvFTaocLqOwLz33vO+53IKiKZ9WQC000KEJgzqoc72i7DmcWLNqkapVKUw5+S63BkSP5+Xf1mv6un0KCbTr7dvb+UUWakigQ60P1aScu7lss3zdgaAaAAA+YMf+LL367nsKef9i3Z/1umlEkFqunpy3fq9Kgz6Toqq5e4gA4PUqhQerSWykWd9w0FbqReiHTVyi5Iw8Na4aoWev8e0pYd4gJjJELapHyZqxNiUh8bz+bPN++GSp/jgUgHnvjvbqUKfieR2Dt7I+N/0OZav9tGKPPMXva/fqld83mvXnrm2hZtWi5C861Su6IDGvDLN83YWgGgAAXuxAVp6e+SlBN7z0ve7Z/g81tW9Tlj1cGRc9qwojFshRv4e7hwgAPuXCQ/8cri/loNqzk9Zo8bZURYQE6M3baEzgKYrrc32/9PzV5ypwuvTQZ0tNlpU1be6d29sfDkrg9FzRsui4zVi3z9Qw84SLn3/7fLlZv71TLV3d2r8udl5wKMvXmt7ua3XVCKoBAOCFrBorH/y2SN2e/0PvzN6iPc5I/RrRXynxAxX28HKFdxsmOQLcPUzgnKSnp6t9+/Zq1aqVmjdvrrfffps9CrfrXL/0g2rfLt2pCXO2mvWxN7ZSrUpFXUbhfv1axslKGFywdb92pmaV+c+zpv7+7Yvlh7t8vnVbW3VpwJTPM9UoJkL1q4Qrz+nS1NV73X7Odu/Hiw83Jvjn5fHyN61qFNVVs6ZRW/XkfAlBNQAAvIjLVaif56/Wt8/doZtmXaY6uWvNiaNVuPjKh99UpRtekcpx8g3fEBYWphkzZmjZsmWaP3++Ro8erZQU36vHAu/SoU4lBdhtSsm1aXMp/HO4Zk+aHv9mpVl/8KL6uvhQcXx4htioUHU8NO3yh+W7yzyg9o8vl+vH5bsPdX5tox6Niuq64ey7gP7k5i6g//5+tVbvTjO1ba1jagWX/E1IoENtapY/nK3mS/zvaAIA4KXmrd2hD198SF0nXaKbnT8o2Jav/zbZokkPdVXPRlWovQOf43A4TGDNkpOTI6fT6XPTRuB9woMDDgdZflubdE6vZWWuWBksVkH1bg0r66FLGpbSKFGarm5VrcyngFoXzZ78dqW+WbpLDrtNr97cRpfEE2AtjS6gVqOH1Mw8ucPnC7fr80U7ZLdJr9zUWnHlQ+XPFyQsS7alypcQVAMAwMOt252q914ZpVqfdtMdWR8o0pal5HINlXvTl2p82//MyTfgDjNnzlS/fv0UF2dNj7Lpu+++O+Y5r7/+uurUqaOQkBC1bdtWs2bNOqOfceDAAbVs2VLVq1fXI488ouhoMjHhfr3ii7KHpq5JOqcgysNfLNO2lCxVKx+ql29sxe9zD9WneayZirlub7pW7jxY6q9vXSwY+eNqfbawKPjy8k2tdFmzqqX+c/yNNf3TaixS4CrU5NXnt9GEZdWug/rX96vN+sO9G/n9NN7WhzqALtt5QL6EoBoAAB4q8WCOHvlqufa9cYXu3D9Gsbb9Sg2KVXrf1xX98HwFN+5tzW9w9zDhxzIzM03A67XXXjvu459//rmGDx+uJ598UkuXLlXXrl3Vp08fbd++/fBzrEBbs2bNjll27y7KCClfvryWL1+uLVu2aOLEidq71721cQDLJY0ry6ZCLdtx8KzrbI37Y6N+W5NkpoK9cWtbVSgXxM71UFGhgerTvCjI9cHcotp3pRlQ+8/Pa/Th3G3mT/pLN7Q8nGGFc/fXFNA9572RlJWFanVxvbhxFd3XvZ78XctDQTVr2vzBLPc3jygtVDAGAMDDWF2q3py+Ue/+udVMCXI4OqpVwHbldvqbKvUcJgUEu3uIgGEFyKzlRMaMGaPBgwfrrrvuMrfHjh2ryZMna/z48aY+mmXx4sWntTdjYmLUokULkx13/fXXH/c5ubm5ZimWlpZmvubn55ulNBW/Xmm/rrfw9+2vGOpQ/chCbUiz6etFOzS0R90z+v7f1+3TmN/Wm/WRVzRR45gwr9qX/nj8b+lQXd8v223qqg3vUavUtn/MbxtMwyHLM1fG64pmMR6/X73p+F8WX1kvTF6nOZuSlZiaoUrhwWW+/VYWqtW9dWdqtmpUCNV/r20qp7NATqd8wtke/4ggm9kfO1KztXhbsrrW9+zM89PdPoJqAAB4COtq5k/TflfFuaO1M6+Dclxd1K5WBV132eMKj/23wkOi3D1E4LTl5eWZgNljjz12xP29e/fWnDlzTus1rKy00NBQRUZGmgCZFVC77777Tvh8K1A3atSoY+6fMmXK4dpspW3q1KnyZ/68/e0r27QhTfrozw2qmbnWTNs7HYlZ0phVDhUW2nRhjEvl9i7XpEnL5Y386fhb5RxrlHNoR6ZLz305U72qnfv2T95p06QdDrPev7ZT4UkrNGnSCnkLbzn+RcfNppe++F1dqhaW+fZbx3XGDocCbYW6qUa6/vzDO/bT+Tj+le127ZBdX/2+UOnrPbtGalbW6WUhE1QDAMDNrKkf0+YvVe7UZ3RVwe9y2ApVP3iXLu//gHo1jaUBAbxScnKyaSxgZZiVZN1OTDy92jY7d+40mW7WZ8Ra7r//fpOtdiKPP/64RowYcfi2FYirUaOGCeRZgbnSvoJt/UPRq1cvBQYGyt+w/fnK/XWqftwZoOScAoU3aK8eDSufcr+lZeer/5vzlevMUvvaFfTWwLZe2QnQX49/btwuPfrNai06EKaesZnqc+nZb/87s7dq0o6ibMVHL22ou7rUlrfwtuO/J2qrnvt1vTYUVNSzfTuW6fbP3piiX+YVZWA/fXUzXdemqMmFLzmX47+3/DYt+WWdssNi1LdvG3my4mz3UyGoBgCAG63avFPrvhqlyzO/VYgtX7JJ22MuUdy1z6p6DDVV4P2sBgYlWcGxo+87Eave2rJly077ZwUHB5vlaNZJf1n941eWr+0N/Hn7gx3SDe2q6d0/t+md2dt0SfzJL4Lk5Ds19NPl2pqSpbioEI2/ta3KhXr3dH5/O/5Xta6hMb9tVGJaruYm2XTlWW7/a79v0ItTigJqI3o11H09G8gbecvx79+2pl6cssHUQNyyP0cNYyLKZPt3HcjWiC9XmKzGm9rX0M0dvSdQer6Of9vaRR1AV+5KU0BAgEdfOD7dbfO+yyIAAPiAfem5ev+98ar8wYXqn/WFCajtjGyt7Nt/Vc37vlZATCN3DxE4J1aXTofDcUxWWlJS0jHZa4C3uv2CmqYr5Pwt+zV9/b4TPq/A6TI1lqznhQcH6O2B7RRdCrWdcH6FBDp0f8/6Zn3yTrvScwrO6PutiwrP/7r2cEBt+CUN9MBFRa+HslM5IlgXNynq2Pv5wh1l8jNyC5wa+skSpWblq1m1SI28smmZ/Bxv1zQuUgF2m5Iz8kzNOV9AUA0AgPNcN+2dWZt10YvTNWVjumJsB7QvME6pV05Q9b/9odC6nTge8AlBQUEm0+zomivW7c6dO7ttXEBpiisfqoGdi4rWP/X9KqXnHFvYOjvPqSEfLdbk1XtNAO7t29upaRw1Mr3Vje1rqk6lMKXn2zR22sbT/j4rsPqv71fp9embzO0n+jbW8EsaenSmji+5sX0N8/WbJTtNAKy0jfwhQct3HDCdYsff0tYEYHEsa780iS0qx7Bi50H5AoJqAACcJ0vXrNe/x4zVMz+vUXpugTJiO2tTz/Gq/OgyVWhzjTVPjmMBr5KRkWGmZxZP0dyyZYtZ3759u7lt1Td755139N5772nNmjX629/+Zh679957y3Rc48aNU3x8vNq3b1+mPwewPHBxA1UrH6od+7N1/8SlZppnsa3Jmbrp7XmatjZJwQF2vX5LG3WqVzT9Cd7JqoH3rysam/UP523XrA0nzlAsdjA7X4MmLNTH87abP/X/d3Uz3dOt3nkYLYp1a1BZVSNDTCbZlNV7S3XHfL5wuz5dUHRsX76plWpULJvGOL6UrWZZm3h6Ncs8HTXVAAAoY2nZeZo6cYwu3v6K/qkCrQgbq9su66Lr29WQw96F/Q+vtWjRIvXs2fPw7eImAQMHDtSECRN04403KiUlRU8//bT27NmjZs2aadKkSapVqyizp6wMGzbMLFaR4agoMoJQtiJDAvXqgNa65e35mrF+n3r/b6Z6xccoMS1HU1YnKt9ZqPJhgXp3YDu1rVWRw+EDutaPNp1b/9xr17BPlujzIZ0OZ98cbf7mFI34YrmptxUa6NDYm1rp0qZVz/uY/V2Aw26y1V6etsHMGLiiRek0grKy0/71/WqzPuKShurRqGiaKU6sUdWimnZr9qTLFxBUAwCgDM2cO1flpjys/oWrTROCXSEN9NmAFoqoWZP9Dq/Xo0cPUyPoZIYOHWoWwJe1qVlBEwa114OfLdX2/Vl6d/aWw491bRCtZ69pTvaKj7m6lks5wRW1ePsB3fDmXD1zdTP1axEnu70oULMxKUOv/r5BPyzfbQrX16oUpnED2qhZNQL97nJbp1p6Y8YmLd950NQ3vKDuuWWNpmTm6b6PF5vSHpc0idGwQ/X2cHKNq5KpBgAATiE736Wf3nhCfZPfV7AtXzkKUmKbh1X78r9LDq5pAYCv6Vi3kqY93EOTVuzRur3pJoOtR6PKalmjvLuHhjIQ5JDeuKW1hn66TAu3puqhz5bp2UlrVDc6XEnpOdq0L/Pwc29oV11P9WtqmlTAfazmIP3bVtfE+dv11szN5xRUK3BJD362XLsP5qhOdDmNubHl4YAqTq7xoUw1q1GBVYcyIsTzO8ieDJ9qAABK2byNSaq78nl1sCWY7LTNUReo2q3jVbtyXfY1APgwK2hyw6GC6PB91rTej+/qqDdnbNbbszZrb1quWSxWfOWixjGmwyfZaZ7j7q51Tf2z39cmKWF3muIP1fc6E1aG9meb7FqYnGo+82/e1tYE0XF6KpQLMvXtrCny6/eme/20eIJqAACUEiv9/8Up68yJ9d8d9dUsYLOSuz6tuhfdQxMCAAB8UHCAQw9e3ED3dKurpdsPmCy1yNBAtaxeXhXLBbl7eDiKlVV2efNY/bRij57+abU+vfuCM66t9tofm7Uw2S6H3WaajzSMKcq8wulrHBthgmpWXTWCagAAQEn7D+jxiX9q2s6inTG3wrW67cYnVbN6UYcwAADgu0ICHXR29RKP9WmsqQl7NW/zfv2yKlF9m8ee9vdaWW6v/LHJrI/q10TdGlYuw5H6dl216ev2aV2i9zcrsLt7AAAAeLtlK1co+dWL9EDSP1UxuFCv3dRS19e3KzSmnruHBvilcePGKT4+Xu3bt3f3UAAAHqZ6hTAN6V50jvafn9eYul6n44tFO/TEtyvN+sVxLt3YrnqZjtMf6qqtTUyTtyOoBgDAOZj646eq9VUfxRduUh3HPn1/c4wubRrDPgXcaNiwYUpISNDChQs5DgCAY9zXvZ6qVwjVrgPZeuzrlXK5Ck9aQ+2tmZv06NcrTCfX2y+oqX41XezVc5z+aVm7J/2UXcQ9HUE1AADOgnXy9dP7o9Vz0VBVsGVoW0gjBQ2dpRqNyYwBAADwZKFBDr1yc2sF2G36eaVVXy3huIG1g9n5+vuXK/TspLUmoHZH59r6Z99GOsMybDiK1SU30GFTem6BCWx6M4JqAACcoZy8Ak1+dZiu2PacAmwurYu5XDUfnqHQynXYlwAAAF6gTc0K+m//FmZ9wpytuu29+VqyPVUFTpf2pefqo7lbdfFL0/X1kp2mm+vIfvH6d7/4M25sgGMFBdhVs2KYWd+SnClvRvdPAADOQGZugX57daiuyvjc3F7b6D41vmk03T0BAAC8TP+21RXgsOkfX63QnxtT9OfGOcc8p17lcvrPNc11Qd1Kbhmjr6oTHa5N+zK1NTlTXRt4b8MHgmoAAJymtJx83fn+QiWndFDHoN+U2fkfanzpfew/AAAAL3VVq2pqVaO8Xv5tgyavTlRmntNM77Q6VN7Qrrpu6VjLZFahdNWJLs5Uy/LqXUtQDQCA02DV1Lj93flavvOgIkNqaO/AP9Wyzum3YAdwfrt/WovT6WS3AwBOqValchpzYytTV21/Vp7CghwKCyJcUtaZapYtyRnyZoRbAQA4hezcfC185VZF7p6lCmGBmnj3BQTUAA9G908AwNmw222KDg8moHYe1D6UqbY1hUw1AAB8Vn6BU3++NliXZP+qzkF/aPstc9W4WpS7hwUAAAB4dQdQy/b9Wcp3uhTo8M6cL+8cNQAA50FhYaF+e2OELkn/3tze0+2/alyvHvseAAAAOAcxkcEKDXTI6SrUztRseSuCagAAnMDkT19Vn+QJZn19m6dU76JB7CsAAADgHNlsNtWOLuf1ddUIqgEAcBx/zvhVPdc9bdYT6t6phlc+zH4CAAAASkkdH+gASlANAICjrF2/Tg1+H6JgW77WRXVR/K0vsY8AAACAUlSHTDUAAHzLwax83ffNVk13ttSOwNqqN2Si1QrK3cMCAAAAfErtSkXTP7eSqQYAgG80JvjHV8u15UCBXosYrsih0xQQRqdPwNuMGzdO8fHxat++vbuHAgAATpmplilvxaV3AAAO+XrqDE1N2KMgh13jbmmrqArR7BvACw0bNkwJCQlauHChu4cCAABOoNahTLXdB7OVV+CSNwpw9wAAAPAEa9eu0cV/3qIJgXW1+5Jxal6dDDUAAACgrESHByk4wK7cApcSD+aoZqWixgXehEw1AIDfy8nLV+aX96iCLUO1wnJ104WN/H6fAAAAAGXJZrOpWoVQs74z1Ts7gBJUAwD4vd8/Gq22zhXKUZAq3PqBbAHBfr9PAAAAgLJWrfyhoNqBbK/c2Uz/BAD4tSVLF6rn9lclm7S9zaNqWKOJu4cEnJOZM2eWyh7s1q0bRwIAAJSp6hWKpnzuSiWoVurGjx9vlq1bt5rbTZs21VNPPaU+ffoc7tI2atQovfXWW0pNTVXHjh1NtyfreQAAnEpGdq4CfrhfobY8bSrXRg2vGMFOg9fr0aOHmU5xtqzzK+v7nU5nqY4LAADgaNUPT/88/aBadp5TgQ6bAhzun3zp0Zlq1atX13PPPaf69eub2x988IGuuuoqLV261ATOnn/+eY0ZM0YTJkxQw4YN9cwzz6hXr15at26dIiIi3D18AICHm/nJaPUtXKtMharqwPcku/v/MAPn6vfffz+noBoAAMD5DqrtOnD6NdXen7NFY6as1x2da+ufV8TLnTw6qNavX78jbv/nP/8xmWvz5s1TfHy8xo4dqyeffFLXXnvt4aBbTEyMJk6cqCFDhrhp1AAAb7BsxwG9uzlK9QOqyd7xbtWvUsfdQwJKLVMNAADAq2qqpZ5+ptrW5EwVuAoVHuL+kJb7R3CarCkIX375pTIzM9WpUydt2bJFiYmJ6t279+HnBAcHq3v37pozZ84Jg2q5ublmKZaWlma+5ufnm6W0Fb9mWby2N2D7Of4l3wf+xp/f/56+7flOlx77arnWuhrqzcbv67lerUp1rJ6+/WXNH7bfk7etTZs2evbZZ3XZZZeZ2x9++KGpj1a7dm13Dw0AAOAIVaNCzNektNzDJShOZWtKUVZbnehycjePD6qtXLnSBNFycnIUHh6ub7/91mSpWYEzi5WZVpJ1e9u2bSd8vdGjR5s6bEebMmWKwsKKCuSVhalTp8qfsf0cf3/mz+9/T932aTudWrs3WOUCCtUmKFGTfvnVr7b/fPHl7c/K8ty278uXL9f+/fsP3x40aJA++ugjvwqqWTV2rYW6cAAAeLYqEUVBtTynS/sz81QpPPi0MtUstSoRVDulRo0aadmyZTpw4IC+/vprDRw4UDNmzDj8+NFRzFNFNh9//HGNGDHiiEy1GjVqmIy3yMhIlcWVbOufCqvWW2BgoPwN28/x5/3vn59/T/7sb9+TpBZLLlY1R3fVveIfurptLb/a/vPBH7a/ONPdE8XGxmrz5s1HnBv5m2HDhpnFOk5RUVHuHg4AADiBoAC7osODlJyRp8S0nFMG1TJzC5SUXjT7sA5BtVMLCgo63KigXbt2WrhwoV5++WU9+uij5j5rCqh18lgsKSnpmOy1kqwpotZyNOukvyxP/Mv69T0d28/x5/3vn59/T/vsW8GFDV88octse3RHyAxVafM/2fjd7zfHvzR58nZZFwqtOrRLlixR+fLlzX1Wp/TffvvthN9jXZB89913z+MoAQAA/poCagXV9qblqGncyS+GbU0pylKrEBaoqDD3n495/PTP4/1DZNVEq1OnjqpWrWquhLdu3do8lpeXZ7LY/vvf/7p7mAAAD/T777+qd/r3kpXQfMUY2YLKbto/4C4vvfSS+Tp58mRz8dEKmM2cOdMsJ0JQDQAAuEvVyBCt2pWmPQdzTvncrclFJThqe0A9NY8Pqj3xxBPq06ePmZ6Znp6uzz77TNOnT9evv/5qTv6GDx9uCvE2aNDALNa6VRdtwIAB7h46AMDD7E/PUrVZj8luK9T6Kn3UsHVfdw8JKBMVKlTQe++9d/i23W7Xxx9/zPkRAADwSDGRRXXV9p5GUG1HalFQrVZFz7g47tFBtb179+q2227Tnj17TD2MFi1amICaVaPF8sgjjyg7O1tDhw5VamqqOnbsaBoOREREuHvoAAAPM+vj/9NV2qp0W7jq3Pqyu4cDlJlXXnnFdP5s2LChuf3vf//bnEMBAAB4othDHUCtmmqnYnUJLRmIczePDqqdqraHla02cuRIswAAcCKLlq9Qr8R3zbTP1M7/VM3IE9feBLzd3/72N0VHRx8Oqj399NMmo79Zs2buHhoAAMAxigNkpzP9Mym96DmVI07dJfR8sLt7AAAAlKWcfKd+nvSDHHJqW7kWqnnxEHY4fFp4eLgyM4uK+Ppr908AAOB9QbV9h7p6nkxx588qZKoBAFD2xv2xUe8fbK2V4WM04ZYOVoEpdjt8WvPmzfXaa6+ZbujF3T/Xrl170kYFlm7dup2nEQIAAPwlOrwo68zqAHoqycVBNQ/JVPPo6Z8AAJyLDXvT9caMTWZ98FW9FR4Xyw6Fz3vuued09dVX65prrjlcLuM///mPWY7HymSznuN0Os/zSAEAAKToiCCzG/Zn5srpKpTDbjtlppqnTP8kqAYA8EkuV6Fmf/x/indVU+UmnXVZs6ruHhJwXnTp0kWbNm3SggULTLOnO+64Q/fcc486derEEQAAAB6nYliQbDbJVWgF1vJOGDDLyitQRm6BWSdTDQCAMvTL5J90e9pbGhgk7es602TiAP7C6ppe3C3d6v7Zt29fXXnlle4eFgAAwDECHHYTWEvJzFNyRu4Jg2rFNddCAx0KD/aMHLFzHkVCQoJWr16tpKQk8w9L5cqVTXepJk2alM4IAQA4Q9sSU9Ro3qNy2Aq1uWpf1a3bgn0Iv7Vlyxb5m3HjxpmFKa0AAHhPXbWUQ0G1UzcpCPaYC+ZnFVRbs2aNxo8fry+++EL79u07orNU8YZZrdxvuOEG3XfffYqPjy/NMQMAcNJpn0s/ekRX23bpgL2Cat/6GnsL8DPDhg0zS1pamsnaAwAAnl9Xbd3ek3cALX6s8qHGBl4XVNu6daseeeQRff311woNDVXXrl1NfY569eqpUqVKJrC2f/9+bdy4UfPmzdP777+v119/Xf3799fzzz+v2rVrl92WAAAg6cdJ36tfxteSTcrvM0b28ErsF/gVu91ulqysLAUFBZn1U13NtR4vKCiqUQIAAOC+DqC5J3yOlclmqRRe1NjA64JqjRs3NtM633vvPRMoCw8PP+nzMzIy9NVXX+nll18235ednX2u4wUA4ITWbNut5gsfM9M+t8RdoTrtr2Vvwe/cfvvtJkjmcDiOuA0AAOD5QbW8Ez4n9VBQrWI5Lw2qffLJJyaYdrqsoJvVccpavvnmm7MZHwAApyUzt0BTP/mfHrTt0X5HtGrf+ip7Dn5pwoQJJ70NAADgaYqbEySfZPqn1RnUUiHMS4NqZxJQO9q115ItAAAoO//6bpW+SeuuvPBCDbnuctnCKrK7AQAAAC9Q8VD2WfEUz+NJzfK8TDX7uXzzo48+auqnAQDgTl8s3KFvlu6S3WZTt1ueUETjnhwQAAAAwEtUOJR9duBQ4MxbMtXOKaj2wgsvaMGCBaU3GgAAztCS9duU/cPfFa4sjejVUB3qkKEG/2Y1JrDqqZ3JEhBwVg3hAQAASkWFsEDzNTUr36sy1cr0DGrixIkmm23Hjh1l+WMAAH5qZ0q6Mj4dpIGOxWofvk+Ne0xz95AAtzteY4IlS5Zo5cqVatiwoWkeZVmzZo3Wr1+v5s2bq02bNm4aLQAAgFT+UPZZceDseFIziwJuFbw5qPbKK69o6tSp6tChg7mdlpZ2wuc6nU7t3r373EYIAMAJGhPMeWu4bihcrFwFqe6Nz8lup8MhcHRjgmnTpunLL780HdmPrnFr3Wc1lPrf//7HjgMAAG7PVEvPKVCB06UAh/2E0z8revP0z/Lly2vRokX697//ba6CDhs2TNHR0erVq5fJSvv888+1YcMGFRQUaM6cOeYxAABKU06+U5+OH6Ubcr8ytzMve1khtYsu9gA40r/+9S/dfffdx20add1112nw4MH65z//yW4DAABuExVaFFSzHMg+dgpodp5T2flOs16h3F/P9bpMNWtKgbVs3bpVdevW1RVXXGFqd1jTCqwroZaSUw5uu+220h0xAMCv5Ttd+vCtF3RX6quSTdrb+kHFXDDA3cMCPNby5cvNuduJWNNB33nnnfM6JgAAgJKszLTIkACl5RSYZgXR4cFHPF48LTTQYVN4sOfUgj3rkdSuXVtXXXWVhgwZossuu8zcl5KSosWLF2vp0qXavHmzCbo9+OCDpTleAICfB9Tefe8N3ZX0X9lthUpsdJuqXvm0u4cFeLTw8HDNnj1b995773EfnzlzpnkOAACAO1UoF2SCasdrVlCy8+fRtWPd6ZzCe99+++0RtytVqqTevXubBQCA0p7yef/EJdq62aGrg6JUWLuHYm98xUqPZkcDJ2FN+3zrrbdUq1Yt/eMf/zClPCwHDhzQ888/b0p3WBdJAQAA3N2sYFtKllIPBdBKOnAo0GYF1TyJ5+TMAQBwAuk5+brrg0Wav2W/ggNqauOV36tLq6aS/YxLgwJ+57nnnjNlOkaPHq3//ve/iomJMVd4ExMT5XK5TPMp6zkAAACe0KzgwHEy1dJyiu6LDPWsMNYZ/Tfy22+/nfUPOpfvBQD4rx0pmfpy7AiFbZumiOAAfXhnB3Vp21JyeNYfVMBTRUVF6c8//9Qbb7xhSnZERkYqIiLCrFsZbNbUUOs+XzJu3DjFx8erffv27h4KAAA4TcVZaAeyj81USzvUvCAyxHOaFFjO6D+SPn36qHPnzho+fLhpUBAYePKNyc/P108//aSxY8dq7ty5yss7dscAAHAi89btUOqnQ3Sn/tSAoCBtu/F3NapbiR0GnKGAgADdc889ZvEHVnd6a0lLSzNBRQAA4PnKH8pUO15NtfScAvM1skSXUK8Lqi1btkwPP/yw+vfvrwoVKujiiy9Wx44dVa9ePVWsWFGFhYVKTU3Vxo0btWDBAv3+++/mtlVjzfpeAABOh8tVqE+nzFbLOQ/oAvsWFcih3Iv+T42aNGcHAgAAAD6ofGjQKad/RoR41myVMxpN06ZN9euvv2revHkaP368aVTw1VdfHdN5wQquWdMIrMK49913H6n3AIDTtjctR19NGKPbUl5RpD1bGY4oBd78saLqd2MvAgAAAD4q4lDALCO3KCvN56Z/FrvgggvM8t5775nCt6tXr9a+fftMcK1y5cpq1qyZWrduLTsFpAEAp8m6IPP14p1y/PSAhukPySYlV2ilSrd/KFuFWuxHAAAAwIeFHwqqWU3KjpZ2ePqnF2eqHc3hcJgsNIrAAgDOxZbkTD357UrN2ZSifwREyBlg18H2f1P0ZU/QkAAAAADwA5GHg2rHZqoVB9o8LVPtjLp/Hs/zzz+vunXrmqYFVapUUd++ffX++++bJgUAAJxMZm6B3v/hNw0d+6kJqIUE2hV00WMqvHOyKl7+FAE1AAAAwE9EHAqYZRwnqJaWXXDEc3wiU23UqFFmCQ0NNdNBs7KyNGPGDFN37YUXXtD333+vBg0alN5oAQA+ocDp0vd/LlfB9P/qVucUtbbX0pj64/TMNa1Us1KYu4cHAAAAwE011dKPO/0z3yOnf55RptoXX3yhtWvXyuVymdtvv/22OnXqpMTERM2aNUuLFy82tdU++OAD7d+/Xz179lRSUlJZjR0A4GWcrkJNWrRBHz1/v3pP66MbXb8o0OZUXGx1fTCgMQE1AAAAwE+FB594+qdPNCq46aabTDOC4OBgxcfHm2DaxRdfrPXr16t58+YKCgpSWFiYbrvtNl1yySVq166dRo4cqddff73stgAA4PHynS5Nmr9aB/54TVfl/ajytsyiRgQR8Yq6crSqNOjh7iECPuPOO+884++xzu/efffdMhkPAADAGU3/zCuQy1Uou912+LHiQFtxNpunOKPRTJ06VStWrDDLypUrTcbaRx99pI8//lgBAQFq2rSp2rRpY4JpVvdP66TOOkEjqAYA/slqh/3Dwl16a+ZmNUuboTeDPjPBtNTQmgq+5AlFt75RolM0UKomTJhwxt9DUA0AALhbxKGAWWGhlJlXcDjIZs12Sc8t7v7pxZlqVlaatRSrWLGibr75ZrVq1cpM/VyyZIk++eQTvffee+bkrJgVXLMCbs2aNTNLtWrVSncrAAAeZVtyplZs2KjpS5bp29x25r68chdqU6XViut8syq0uFqyO9w9TMAnFZfpAAAA8CbBAXYFOmzKdxaazLTjNS7w6ky1o1lTPGfOnKmXXnpJd999t7mvoKBAq1ev1qJFi0wNNiu77bPPPlNOTo553Aq2OZ3O0hk9AMCjpnjOXLVVW2d+rI77vta/7Fu1T5FKiO6sWy5soBva1VBIYG93DxMAAACAB7LZbCaQtj8zz8x4ObpJQUigXcEBDt8Jqj399NNmqmfXrl1N0wIrY82aBtqyZUuz/PjjjyabzWpWsHHjRjNlNCEhofRGDwBwu4170zVn+s+KXPuFLnbN0cW2bNMGJ1eByq/bS7/0by17eLS7hwnA+rxu3Ki9e/eamQNRUVHsEwAA4FEiQgJMUK1kB9DiAFtxIwNPck4jaty4sb755hvdeOONatu2rRo1amRqqoWGhmr27NmmgcE999wju92uhg0bmqV///6lN3oAgFtYf9h+XrFbXyzaqc4739XDgV8VPWDVSwuuJmer27Ugq7p6XXmj7IGeVfcA8Ec//fSTHnroIW3dutXctmYSXHTRRebCZ+fOnfXcc8/puuuuc/cwAQCAnws/FDhLKzHlMyuvaD0syMeCapbevXtrzZo1euGFF/T1119r4sSJ5n4rkDZgwACNGTOmNMYJAHCznHynZq3YoD3zv9KPuyO1sKCeuT/b0U7DbD8ppVZfxXS7UxXqdFF+QYHyJ01y95ABSJo+fbquueYaM6Ng4MCBpjN7sSpVqqhevXqmVAdBNQAA4G4Rh2qmlayjlpVXVEIsLMizpn5aSiXMV7VqVVNXzVr27dunlJQUxcXFKTIysjReHgDgxjppc9ds1fY5X6v6rknqruUKsjlVztZFKZX/oevb1lD/NhcrJOR2VQsqx3ECPJBVrsMqyzF//nylpqYeEVSzdOrUSR9++KHbxgcAAFCsuDmB1aigWGaujwfVSqpcubJZAADeyeUq1IItydo8c6KqbPtZXQqXqJst30zttCSF1lPnVhfr2t7dj+j0DMAzWc2jRo0aZWYRHE/16tWVmJh43scFAABwtIhD0z9L1lTLzi8KsJXztZpqAADfUFhYqBU7UvXDikT9tGK39qblaErQm2po32WCacnBNZTX5BpV7TRAVWKauHu4AM6A1XU9ODj4hI8nJycrKCiIfQoAANyu3KHAWeahKZ8lM9VCA/0gUw0A4D3W7dqvlbN+ULmN36tV/jJ9kvuSchRs0q5Xxd6o0AqZir3wFkXHtbB6XLt7uADOQpMmTTRr1iwNHTr0uI9b3dqt6aEAAADuFnZoiqdVz7lY9qEAG5lqAAC325p0UEtn/qTgdd/pgrw5us6WUfSATfp73R2q1eUmdWsYreCAS909VAClYPDgwXrwwQd18cUX68orrzT3WVO309PT9dhjj2nevHnUVAMAAB4h5FA2WnHHT0vmofVQf6ipBgDwPLsPZOvnFXu0Y+GPeiDtRV1jSyt6wCalOcorpWYfxV44QHfV7WK1b3b3cAGUovvuu09//vmn7rnnHo0YMcIE1G644QbTtMDlcmnQoEG65ZZbfGqfjxs3zizW1FcAAOA9Qg8FzrLzXMdmqhFUAwCcL/szcjVv1mTN2Jiqz3dFm/tq2iL0dHCaMuwRSqp+qap0GqDIht0V6eAaC+DLPv74Y1177bX65JNPtHbtWlNH8cILL9Rtt92m/v37y9cMGzbMLGlpaYqKinL3cAAAwDlM//wrU83z/mfxvBEBAM5avtOluYuXKHXuR2q5/1f1tSXK4WynL2wj1L52RV3ZspkOlv9BUQ06K9xR1K4agO/KycnRF198oUaNGpmgmrUAAAB40/TPLDLVAABlac3WnVr/xyeqvu07dVNC0Z02KVshql29mubc2FOx5cMOPbsWBwPwE1bXz7vuukuvvPKKOnbs6O7hAAAAnFRxh8/sEplqWYe6fxZnsXkSMtUAwEtZtQV+XL5bH83bpn/tG6Gr7OvM/S7ZtC2yrYLb3aq4jterUXC4u4cKwE2s+mk1a9Y00yABAAA8XVhxTbX8v2qqZR0KsIUx/RMAcK627tmnVb++q2e2NVFiTtG1kV8DO6lmYLYym9ygWj0Gqk7FmuxoAMbAgQNNTbXhw4crKCiIvQIAADw/Uy2vxPTP3KJ1MtUAAGfFKiq+ZMVKJf82Rp3SftUVtmz9mT9YsyteoVs71tL1bXqqYniolZbCHgZwhM6dO+ubb75Rq1atNHToUNWvX19hYcXTwf/SrVs39hwAAHCrkKDjTP88VFMtLNjzJlt63ogAAIe5XIWaN2+W8maMUZecGQqwuUyttL0Bcbr1gnj9p3dP2e0E0gCcWK9evQ6vP/jgg2ZK6NFBe+s+p/Ovk1cAAAC3Tv/MKzH981DWGplqAIDTDqZNWrZFlSbdo84FC4vutEmbItopoucIxbTqoxi7nb0J4JTef/999hIAAPDe6Z95NCoAAJymWRv26b+/rtWqXWl6N7BALrtN6ytdpJi+j6le/Q7sRwBnXFMNAADA27p/Fh7Kpv8rqOZ5ky09b0QA4Kc2b9mizV/9U0+k9FGSKig8OEA72v1bWW2rqnFcE3cPDwAAAADOS001V6GU53QpyGE/PP2z3KHHPAlBNQBws+zcfM387AV13Pya6toytS8wW+s7/J/u71lflcKD3T08AAAAADivmWqW7DynCgOLAmyWUIJqAICSFsyboXKT/65LC9ebmmnbg+rpoisf1M3NmrKjAJyViy666Jz3nDXVYtq0aRwBAABwXgU67Ap02JTvLDw0BfT4ATdPQaYaALhBdk6eZn3wT/Xc/Y4CbU5lKlQ7Wv1Njfs9LDn41Qzg7LlcrmM6fJ4pq4YJAACAO4QEOpTvLDCZavZD5zQOu00BDs9r1MZ/bgBwnq3adVDzP3hcg/M+Mdlpa8p3V61bx6lxdA2OBYBzNn36dPYiAADwWmFBDqXnFJgGBVbmmsWqreaJPHNUh4wePVrt27dXRESEqlSpoquvvlrr1q075krqyJEjFRcXp9DQUPXo0UOrV69225gB4GS+W7pL/cfP0ctpPbTJVkPrL/ivmjz0vcIIqAEAAACAiqd55uQ7lVvgMutBAZ4ZvvLMUR0yY8YMDRs2TPPmzdPUqVNVUFCg3r17KzMz8/Bznn/+eY0ZM0avvfaaFi5cqKpVq6pXr15KT09369gBoKSCAqcmfvKuhn++1PxhaNOotiqOWKiGl91rFS9iZwEAAACAiqZ/WqyaankeHlTz6Omfv/766xG333//fZOxtnjxYnXr1s1kqY0dO1ZPPvmkrr32WvOcDz74QDExMZo4caKGDBnippEDwF9ycvO06K17NSDtB611DFRU92EafklDUxcAAAAAAHDk9E+LNf0zIsTl0dM/PTqodrSDBw+arxUrVjRft2zZosTERJO9Viw4OFjdu3fXnDlzjhtUy83NNUuxtLQ08zU/P98spa34Ncvitb0B28/xL/k+8DfWdufk5WvlqzeoZ+5suQptur51rBr3rCuXs0Aup3wWn30++yXfB77Il7cNAADAnUKD/pr+WZypFkym2rmxstJGjBihLl26qFmzZuY+K6BmsTLTSrJub9u27YR12kaNGnXM/VOmTFFYWJjKijV91Z+x/Rx/f81Qq5swVu21SnmFDk2ueq9cgfHaPGmS/AWffT77viorK8vdQwAAAPDpmmrZeUz/LDX333+/VqxYodmzZx/z2NFt460A3IlayT/++OMmOFcyU61GjRom2y0yMlJlcSXb+qfSqvMWGBgof8P2c/z99f2fnpGhzeOuVTutUqZCtPeyt3VZuz7yF3z2+ez7+me/ONMdAAAApau442e+06U8Z9H0HmqqnYMHHnhAP/zwg2bOnKnq1asfvt9qSlCcsRYbG3v4/qSkpGOy10pOD7WWYw5aYGCZnviX9et7Oraf4+9P7/+s3Hytf+MWXVCwRFmFwUq68iPVbfvXNHV/wmefz76vfvZ9dbsAAAA8JaiW5yz8q1GBh9ZU88xRlcg4szLUvvnmG/3++++qU6fOEY9bt63AWsnpRXl5eaZraOfOnd0wYgD+rsDp0r2fLNWUjLrKUaCmVn9I1Vv0dPewAAAAAMDrMtVy6f559oYNG2a6eH7//feKiIg4XEMtKipKoaGhZorn8OHD9eyzz6pBgwZmsdat2mgDBgwopcMJAKfv6Z8SNHP9Pi0MvEJX9r9b2ryV3QcAAAAApykowHY4YYGg2jkYP368+dqjR48j7n///fd1xx13mPVHHnlE2dnZGjp0qFJTU9WxY0fTdMAKwgHA+fTLz9/om7lO2WxhGntTKzVtWEnbCKoBAAAAwGkLsHvP9M8Aefj0z1OxstVGjhxpFgBwlyXzp6v7gnv1VVAVzev6vi5tWtUU6wcAAAAAnGWjAqZ/AoBvS0zaq8q/DFGYLVeKiNXAi1q5e0gAAAAA4JUCD03/zC+wun8eylQL8MxMNc8cFQB4iYICpza/N1g1lKi99iqqfc+nsjnoCggAAAAAZ6N4qmeB66/pn8EE1QDA90z7eLQ658xSfqFDzmvfU0hktLuHBAAAAAA+UFPN5fE11TxzVADgBRYvmKUeW/5n1je2fERxzbq6e0gAAAAA4HPTP4MDHfJEBNUA4CwczMqXfnlMwbYCrYm8UE2ueZT9CABlJCsrS7Vq1dLf//539jEAAD4u6HiNCshUAwDfMfLH1Rqafa+mBXRTrTvesVoRu3tIAOCz/vOf/6hjx47uHgYAADiv3T8Llevh3T89c1QA4MF+WblH3y7dpX22iqpw+4cKqxjn7iEBgM/asGGD1q5dq759+7p7KAAA4DwIcNiOzVQjqAYA3i9p/3798M3HZv2+HvXUpmYFdw8JANxm5syZ6tevn+Li4mSz2fTdd98d85zXX39dderUUUhIiNq2batZs2ad0c+wpnyOHj26FEcNAAC8I1PNdbimmqdO/wxw9wAAwFsUFhZq+YSHNb7wG31e/jpdc/Hb7h4SALhVZmamWrZsqUGDBql///7HPP75559r+PDhJrB24YUX6s0331SfPn2UkJCgmjVrmudYgbbc3NxjvnfKlClauHChGjZsaJY5c+acl20CAACeUlOtUHkFTo/OVCOoBgCnafqv36hX2jdmvfNFV3rsL3YAOF+sAJm1nMiYMWM0ePBg3XXXXeb22LFjNXnyZI0fP/5w9tnixYtP+P3z5s3TZ599pi+//FIZGRnKz89XZGSknnrqqeM+3wrOlQzQpaWlma/W91lLaSp+vdJ+XW/B9nP8S74P/A3vf97/Jd8H/uZ8vP/tKspOyy1wqjhBzWErPK/7/HR/FkE1ADgNO/fsVcN5j0o2KSHuWsV3uIr9BgAnkZeXZwJmjz322BH39+7d+7SzzqzAW3HwbcKECVq1atUJA2rFzx81atRxs97CwsLK5HhNnTpV/ozt5/j7M97/vP/9WVm+/1fst2qqObQveb+CHIUmzJawcoUmJS7X+ew8fjoIqgHAKbhchVr/4QO6yLZPex1V1ei2l9lnAHAKycnJcjqdiomJOeJ+63ZiYmKZ7L/HH39cI0aMOCJTrUaNGiaQZ2W4lfYVbOsfil69eikwMFD+hu3n+PP+5/PP7z9+/weW0d+/sPX79O66pQqPjFJIoF06eEAd2rXRZU2PPKcoS8XZ7qdCUA0ATmHqdxN0afZkuQptcl35uhyhpfuPGQD4MquBwdH1KY++73Tccccdp3xOcHCwWY5mnfSX1Yl/Wb62N2D7Of68//n8+yt+/5Xd77/QoCDztcBVaOqqWcKCz+/v29P9WRQEAoCTWL8jUW2W/9usr6s7ULEtL2Z/AcBpiI6OlsPhOCYrLSkp6ZjsNQAAgGKBjqKLb1bnz9yCQ90/PbSetWeOCgA8gFUY86FvNuiJ/MFaE9JajQc85+4hAYDXCAoKMp09j665Yt3u3Lmz28YFAAA8W8Ch7gQFVvdP56GgWnHHAg/D9E8AOIGxv23Qmj1pSgzrpEpDH5EtMJR9BQAlWB05N27cePj2li1btGzZMlWsWFE1a9Y09c1uu+02tWvXTp06ddJbb72l7du369577y3T/Thu3DizWDXdAACAdwk6FEDLd7pMCR5PzlQjqAYAx7FkxQr9OGOpNYFJo69triqRBNQA4GiLFi1Sz549D98ubhIwcOBA063zxhtvVEpKip5++mnt2bNHzZo106RJk1SrVq0y3ZnDhg0zi1VkOCoqigMHAIAXCQywHQ6qFbgIqgGAV9l3IF2B396pH4P26LM6z+qyZrHuHhIAeKQePXqYxgMnM3ToULMAAACcjsBDmWp5Ba7DzY2CPTRTzTNHBQBu4nQVauE7D6p54QYF2Ap1R9+uHAsAAAAAOE8C7cXTPwtNYM0S5HB45P4nqAYAJfz0xTvqm/GNWc/o84pCq9Rl/wAAAADAeZ7+WeBymaQHS8ChjqCehqAaABwy48/Z6rnmKbO+od5AxXa8jn0DAAAAAG6Y/pnvLDSBNYvDTlANADzWyg2bVWfKIEXasrStXEs1uPlFdw8JAHCWrM6f8fHxat++PfsQAAAvDapZDiWq6VBpNY9DphoAv7frQLZWTvynatqSlBQQq+r3fSMFBPn9fgEAb2V1/kxISNDChQvdPRQAAHCGAo8z1dPhoVG1AHcPAADcKSk9R7e9O1+7sq9XZKRLPQf+S47waA4KAAAAALg5U62YnaAaAHiWlPRs3fLWfG3el6lq5aPU5t73Va58qLuHBQAAAAB+K+A49dM8NajG9E8Afml/Ro4WvDpQA1JfV9WIYE28u6PiCKgBAAAAgFvZbDYFHZWtZvPQ6BXTPwH4ne1JB7T2rYHqUzBdzgC7el31gKpXKufuYQEAAAAAJAU4bMpzen6mGkE1AH5l9eadyvjoZvUuXKEC2ZV88f9UvVlXdw8LAAAAAHBEXbW/omrHmRHqETw0gQ4ASt/k2XPl+KCPOhauULZClHHNJ6ra9Q52NQD4mHHjxik+Pl7t27d391AAAEApNCvw1Ew1gmoAfF5OvlMffPCmOk29Vo1t23XAXkGuO35S+ZZ93T00AEAZGDZsmBISErRw4UL2LwAAXijIcWQQzUNjakz/BODbVu06qL9/uVwVkg7q1sBs7Y5orpjBn8lRvrq7hwYAAAAAOI6AozLVHB4aVaOmGgCflFvg1Lu/LtBLc1LldBWqYrnWWt19glpceLnkCHT38AAAAAAAJxB4VKaap07/JKgGwKcUFhZq5tK1Spv0Lw3Mn6kvCp9V0xZt9PSVTVUpPNjdwwMAAAAAnGFNNQ+NqRFUA+A7Vm3dq+Xfvah+qR8r0pYl2aTx7RLVpH8bdw8NAAAAAHCaggLsRwTUbB4aVSNTDYDXW709SUu/f1UXJ3+sW2z7TTAtMayRIq99SU3qd3X38AAAAAAAZ6DkdE9PnfppIagGwGunec7dnKL3Zm3WiM136Vb7NhNMSw2oLGfXR1S162DJ7nD3MAEAAAAAZ8huO/66pyGoBsCrZOc5NW3eQr2+OFsJe7PMfU0COiguKFP5nf6myt3vlgKonQYA/mzcuHFmcTqd7h4KAAA4C44SkTRPnfppIagGwCuy0lZs3auEPz5TjW3fqI9W6Lv8EdoS2EHXta2uazqMVvnKEVJgiLuHCgDwAMOGDTNLWlqaoqKi3D0cAABwhkoG0shUA4CzsCMlUwvn/CbHis/UPW+GWtoyzRRPy9D6+/XSTRcrKiyQfQsAAAAAPsRRIqhWct3TkKkGwKPs2J+ln1fu0R/LN+nF5KG61r6v6AFTL62KMppcr2rd71Kb6LruHioAAAAAoAzY/2r+SaMCADgRl6tQq3fs04aFU7Vryxq9lNLp8GMHgsqpsi1diVV7qnK3warQ6CJVoPkAAAAAAPg0e4nsNA9OVCNTDcD5l56Tr4UrVitl6U+KTpyh9q4Vam7LUW5hoN62tVSzujXUt3msqsV9ppDY2qodGMphAgAAAAA/DKrZPbioGtM/AZS5fKdLy3ft158bU+RY+ZkuPvClLrJt++sJNumgo4L2x3bT9MvbqWJsHY4KAAAAAPgpR4lAWskAm6chqAagTKZ0rtuVrA2Lpylz5S/qtzhdm/IqmMfucCSpSeA2uWTT7nJN5ax3iWLbX6moaq0VVXLiPAAAAADAL9lLxNE8OFGNoBqAc1dYWKgtSQe1ftks5a6frpiUBWpVuEZNbPnm8WXOytof1lud60WrVbUB2hfYQZVb9VX1cpXY/QAAAACAk9RU89yoGplqAM4qiLZpX4bmbt6v+ZtTlL1prl4ueFp1bTl/PckmHbBX0KagxrqnTw89175XibnwfzUjAAAAAADghDXVPDemRlANwOlN59ywK0lbl01XwZbZikldrN/zmup159Xm8ShFKzwkRxm2cCVWbK/A+t0V2+pSlatUXzt/+UUt2lzq0cUlAQC+Zdy4cWZxOp3uHgoAADgLJSsDOchUA+BtQbQ1u1O1c9EkubbOVuyBJYov3KhGtr/+OXEFuLS01mB1rFtRF9StpNzQeQqPaaT6JX775ecXTf8EAOB8GjZsmFnS0tIUFRXFzgcAwMvYmf4JwFs4rcYCm7dr3frV+nlfZS3Ysl8ZOXlaGvwPRdmyip5kk/Y7orWvYjsF1++qVq0u0acxjUu8CvXRAAAAAAClPP3Tg/vZUVMN8NOaaFt3J2nz4qlybpquGgcXqnHhNkWqkv6W+4p5TrmgQC2KuFhx4VJo/a6Ka9lLFaPrqKIHp94CAAAAALyfo0T5oJIBNk9DUA3wE/vSczVnU7IK5r+reom/KN61TnVKTOe0MtHsgWH6d7dqatOwtprGRSrAcZk7hwwAAAAA8EO2EnE0gmoA3FIXLWFtgnYv+lGv7O+oVYlF0zhHBqxUq4AEE0Tb54jRvsoXKKzxxareurfiomI1iGMFAAAAAHAjxxE11Tz3UJCpBviQ9KwcrVowTZmrflHN5Jlqpm1qJund3H9JamKyz1T1Rq0P76aabfuocpV6quzJv6EAAAAAAP5dU83muf+zElQDvFxGboHmzZ+nwIXj1DxtljrZ0g8/5iq0aWtovO7vVFuvX3iJKoUHu3WsAAAAAACciv2ImmryWATVAC+UnZOnGas267s1mfpjXZIaO9fr++BJZkpnusppe4VOCozvozoXXKW6EZVV190DBgAAAADgNJUMpJGpBqBUrF+3Wjt/f0dN9n6vAwXN9WvBPeb+tEottDDqNsW27avqrXqpqSOQPQ4AAAAA8EoOun8CKA3ZOblaNPljlVv1sVrlLVVDW6G5v0vgWt3bua6uaBlnaqXZbD3Z4QAAAAAA36qpZpfH8uChSTNnzlS/fv0UFxcnm82m77777ojHCwsLNXLkSPN4aGioevToodWrV7ttvEBpSsnI1ZSPX1Dqc03VdekItclfIrutUOtCW2vthWMV9/gyPda3iZpVizKfDwAAUGTcuHGKj49X+/bt2SUAAHghu5c0KvDooFpmZqZatmyp11577biPP//88xozZox5fOHChapatap69eql9PS/CrUD3iYpLUf//n6VOj/3u9avXak47dMBRWpprTuVetd8NXp0uhr3GiR7UKi7hwoAgEcaNmyYEhISzPkhAADw7ppqNg8Oqnl0o4I+ffqY5XisLLWxY8fqySef1LXXXmvu++CDDxQTE6OJEydqyJAh53m0wLk5mHZQS796Xm9uidHc/HrmvgVx12lFzVaKv2yIWoeUYxcDAAAAAHyene6fZWvLli1KTExU7969D98XHBys7t27a86cOScMquXm5pqlWFpamvman59vltJW/Jpl8dregO0/9fF3Ol1a8MMbapzwP/VQqkJtjZVV7UWN6NVAnepWNFH5Qi99D3H8/ffzz7H332PvL8ffl7cNAADA3exeMv3TozPVTsYKqFmszLSSrNvbtm074feNHj1ao0aNOub+KVOmKCwsTGVl6tSp8mds//GPf3rKLjXd/qG6aY25vUfR2lu5mwZVT9aBdfv1yzr5BI6//37+Ofb+e+x9/fhnZWW5ewgAAAB+Mf3TQVCt7Bw9t9aaFnqy+baPP/64RowYcUSmWo0aNUzGW2RkZJlcybb+qbBqvQUGBsrfsP3HP/7Wfpn/6TPqs/0NBdmcylaQEurfo6bXPKY+QSHyFRx///38c+z999j7y/EvznQHAABA6XOUiKp5cEzNezPVrKYExRlrsbGxh+9PSko6JnutJGuKqLUczTrpL8sT/7J+fU/H9v91/Dfvy9B3H4zRiIxxkk1aVa6TYm9+VW2rN5Cv4vj77+efY++/x97Xj7+vbhcAAIAnsHnJ9E+P7v55MnXq1DGBtZJTS/Ly8jRjxgx17tzZrWMDTmTK6kT1e3W2XkturZlqoxVt/6Nmf/9FlXw4oAYAAAAAwJkoOeXT7sGRK4/OVMvIyNDGjRuPaE6wbNkyVaxYUTVr1tTw4cP17LPPqkGDBmax1q26aAMGDHDruIGjFbpcmvzZa3pgWXXlKVAd60SrwY0/K7Z82dXxAwAAAADA22uq2T04U82jg2qLFi1Sz549D98uroU2cOBATZgwQY888oiys7M1dOhQpaamqmPHjqbhQEREhBtHDRzJ6XJp/vi7demB7/V/AT2U0O4Z/bNfUwU6PDjcDgAAAACAm9iPqKlGUO2s9OjRwzQeOBFrx44cOdIsgCfKzctT+VXvqKtztlyFNjVo2lY3XtXMsystAgAAAADgRvYjaqp57qHw6Ew1wJtZWZQJr92gi5yzVVBo15pOL6rNZYPdPSwAAAAAADyao8TELqZ/An4mv6BAK169SR2zZyuvMEDru76s5pfc6u5hAQAAAADgZZlqNnkqijoBpczlKtSc1+9Vx6zpyit06Ke44WrU/Ub2MwAAAAAAp6FkHTWmfwJ+5NlJa7Q+sa7aBIZqywX/J0deFXcPCQAAAAAAr+Hwku6fZKoBpeiT+dv0zuwtmulqqemXTlWTS+5g/wIAcJ6NGzdO8fHxat++PfseAAAv7/5p9+DIlQcPDfAuq5bO1ds/TDfrf+/dUP06N3f3kAAA8EvDhg1TQkKCFi5c6O6hAACAs1AyO63kVFBPQ/dPoBTsTdytCt/frm8CsvR23ec1rGd99isAAAAAAGeBRgWAH3X63P3eLaqmJOU4wnV//94eHUkHAAAAAMCTOUrMq/TkRgVM/wTO0ewPR6p13hJlK0i2GyeqXPnK7FMAAAAAAEql+6fnRtUIqgHnYPn8P3ThttfN+sY2/1Rso7bsTwAAAAAAzoGDoBrg2/an7leFX+5TkM2plZHd1bzfg+4eEgAAAAAAXs/O9E/AdxUWFurPD/6pmtqjJFsl1Rv8rpWf6u5hAQAAAADg9exekqlG90/gLHw0b5ueTbxEaYFJ6nzV3aoSRR01AAAAAABKPajmwYXLCKoBZ2htYpqe+XmN8hSs3EtfUJ12ddiHAAAAAACUQVCtZNMCT+PB8T7A8+Tk5umHD15SfkGBejaqrEEX1nb3kAAAAAAA8CkOL6mpRqYacAZmv/eYHsl+V+1DO6j59T95dMQcAAAAAABvZPOSmmpkqgGnae4fP6tn4ntmPbbTDYoOD2bfAQAAAABQyhwE1QDfsWvPbtWa8aActkKtiu6jxr3vdveQAAAAAADwSfYjpn+SqQZ4rYICp7ZPuEtxStZuR5wa3fmmu4cEAAAAAIB/dP+0yWMx/RM4hd8/fVGdcv9UfqFD9uveVWBYFPsMAAAAAIDzEVTz4KgaQTXgJH5fvkntN75i1tc3H6GqTTqzvwAAAAAAKEOOEoE0D579SVANOJGNSel68JuNGpD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", 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", 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VmLKEMg6UZu/Hj+Ldjnfxb7/rseK3jyDETw/LuYtw8JnFGGPeA8MXN6EzbSd8/AK8PVVCCBkUVq/ObrfzjKe+nC0LixBX8NSyWSd6WxNCCCHKRJl3MuLcoVOrgBsij+JX2g3wPfCet6dFPKyzvRUjj32AAFUnLhifzAN3jN5gROLvPkENQpHgqMC+D+6n14YQQggZArZkNjs7G1lZWW59HqlhBSGEEKJsFLyTIZVKhbi5Qte7EZ170VBd5u0pEQ86+P0bCEYbKlRRmLD4xhP+Lyg0AhWz/sGvT6hYieqyAnptCCGEELGjhhWEEEKIolHwTibY8iBH954dq4uSnD4KeZp0aFQOHP3pI29Pj3hQwJFP+FiSehU02lNXxo8775fI1o3BRvtYvL0xn14bQgghROSOl7yj5eCEEEKIElHwTiZ610BRd1c1bky5kI/++V97a1rEw2oripFhyeHXh80Xsi9PplKrYfrlp7jZeg9eP9iF/JpWD8+SEEIIkQdW7y4zMxNTpkxx6+NQwwpCCCFE2Sh4JxO9z8OymndM8jm/5mOm5SBqK455Z2LEo45t/QxqlQO52hGIjEs57e0mpsZgQWYU7A7gjU1FHp0jIYQQIhcer3lHiXeEEEKIIlHwTibsvfbmnDt4kQnDcVSbwYM5RRs/9OLsiKcYS37iY0PiwrPe9ndzhyFBVY2M/Y+joabcA7MjhBBCyFBQ7I4QQghRJgreyUTvM7GqXq9qQ8pFKLFHYF+lySvzIp5jcwD/6FiKx61XIWziZWe9/aSkELzh9wquV69G7uoXPDJHQgghhAx+2Sxl3hFCCCHKRME7GTWsOLnmHRO/6HbMtTyHJ2qmobbV7KXZEU8oawN2mRPwX91SpGZO7FdX4taxN/DracUfw2KmAC8hRFqqqqrwxz/+EWlpaTAYDEhISMDFF1+MH374wdtTIwrhsZp33SNrT0YIIYQQ5aHgnUyw2mVOx0N3QHx4MMbFB/P/X5td5Y2pEQ/JbxFe+WnDwqBxFj48i7GLrkcdghGBRhza8LGbZ0gIIa5TXFzMAyabNm3CE088gYMHD+L777/H/PnzeR0yQmRV8677xCxl3hFCCCHKpPX2BIhr9D4T2zvzjrlgdAyyy+pxdNcGYFrfHUiJ9A1v+gmXq3UYF/OLfv+M3mBEXuwlCK94D5r9HwEX0PuDENLN0n76p0KlAXTGft5WDeh8znxbvd+An/bbbruNBzTWr1+PmJgYqNXC+chRo0bhhhuErGJCCCGEEELkgIJ3csy8Oynp6sJ0H/xqwy3wr+lEY80ChETGeXx+xP2Wmb9AjL4eh/3OGdDPxc+/CfjwPYzu2InaimJExCa7bY6EEAn5Z+zp/y99IfDrT49//VQaYO3o+7ZJs4Hrvz3+9XNjgI76E2/zt+YBTa2hoYFn2f3973+Hn9+pgb/g4OAB3R8hUkGLZgkhhBBlomWzMqx5d3LwLjEuFjXaWGhUDuRv+sTzkyNuV1dVghhVPewOFZJGzxjQzyakj0OObpTw/lj3H7fNkRBCXCU/P5//3cvIyKAnlShCz74drZslhBBCFIky72Si977cyctmmbqEBUgvzocubzWAuzw7OeJ2FYe3IgbAMU0CUgJDBvzzrZm/RNO+J3CwvBnTHY6e2jqEEAV7sOLMy2Z7uy//DLc96TzhnQdddsKKPquIGBpWsIvNZvNQwwpCCCGEKBFl3sm8YYVT3Iwr+ZjZuQfNjQ2emxjxCHP5AT7W+Q8uC2XUwhtxjv1V/LNlMQ6Vt7h4doQQSWJ16E536V3v7qy39Tn7bQcoPT2dB+6OHDkyxI0kZGioYQUhhBBCPIGCdwpoWMEkDp+AUnUc9KouHNn0mYdnR9zN0JDLR2v4yEH9PKsZNXtkPL/+9YEzZNsQQogIhIaGYtGiRXj55ZfR3n5qA4ympiavzIsQd6HMO0IIIUTZKHingIYVzm9WxJzPr2pyexUOJ7IQ3lHAR5+4MYO+j4vHsuL0DhTu/Ql2Ny//IYSQoWKBO7ZU8fzzz8fnn3+OvLw85OTk4Pnnn8eMGQOr/UmI2FE1C0IIIUTZKHgny4YVfdcrC510OR8z27bB1HlqpgKRJvZaxtmFbLmotPGDvp95w8Ox1vAA3rDej6O7f3DhDAkhxPVSUlKwa9cuzJ49G/fddx9Gjx6NBQsW4IcffsArr7xCTzmR/f4eIYQQQpSDGlbIhHNfTn2GPgNp4+fgxW+vxjcdo/DH4lacO3LgdYaI+OTXW3Gj+XmM0ZXi5eikQd+PUa9Fc/AIoLkEzVkrgakLXTpP4jkOux0HfvoMnYXboQlNwsjzr4H/IBqZECJ2MTExeOqppxAYGAi1ms5HEvlSdS+cpdAdIYQQoky0pysT9n503lOpNaga8zsccSRiXU6NB2dH3Kmgrh3VCEWhcSxUQzx41Y9bxsfU2vWwdXW5aIbE05mY+/51EcZt/C2ml72JKQceRvszk3B0z0/0QhBCiFR1795R4h0hhBCiTBS8kwnnmdgzZd4xCzOj+bguuwb23oXyiGQV1gpLoCN9hv56jpx1GZrhh3A0IWfHdy6YHfG0N/+3Gmnte2BxaLEr4DxUqCIRhXrEfPlLFB7aQS8IIYRI0Fl27wghhBAicxS8kwnnmdgzZd4x04eF4ULDfjxofhZHd//omckRt0rIew/3aD/BWG3pkO9LbzAiN2Qev96++xMXzI54UlZxI5466IvLrI8h+7y3Mfme/yHo7ixk68cgQNUJ3efXob2thV4UQghxkZdeegmZmZmYMmWKW59TalhBCCGEKBsF7+S2bPYst9Nr1bg2cA8u12xG857PPTI34l7j6r/DH7RfIE1T7ZL78xl/BR/TGn6mpbMS88z6PD5OnTwd4+dewq/7BQQj9ubPcEiVjifMV+DZDUMP8hJCCBGsWLEC2dnZyMrK8kzNO1o3SwghhCgSBe8U1LDCSTViMR9jqza4eVbEE40JYrrKhet+US65z4zpS9ACX4ShGUf30HtEKpobqmEt2QW9RoU7zks/4f+Cw6NR+8vV+NY+HW9vO4b8mjavzZOIEwUEvI9eA9Kv9wk9TYQQQogiUfBOQQ0rnIbPWgqLQ4MERwVKju7zwOyIu9RXlcJf1QmbQwV9QKRL7pMtnf0y5k4sN/8FX9QKNRKJ+MVUfo8vDQ/j5cgvEB1kPOX/52dE4vyRUbDZHXjxhyNemSMRH51Ox8eOjg5vT0XxnK+B8zUhpDfn7h0l3hFCCCHKpPX2BIira96d/bZBwaE44DMeY027Ub5jFRKHj6eXQaJqSnIQDqBaHQG11nUHfGEzr8GOoj2oyq7D/Usc/QoKE++xWsyYat7G181HT7zwtLe787w0xBx9H7ce+RolR/9Hv/sEGo0GwcHBqKkROpD7+vpK6vfdbrfDYrHAZDJBPcRu297MuGOBO/YasNeCvSaEnMz5W+mg3DtCCCFEkSh4JxPOnTl1Pw+6OpMXAEd2I6hkPYBH3Dw74i7tVQV8bNTFuPR+zxkewesjHqvvwNHqNmREB7j0/olr5e9ah7GqDjQgECNnnD54Nzo+GNago4jtqMeub/6OxLs/o5eCIDpayLB1BvCkFvjq7OyEj4+PpIKOfWGBO+drQcjJKPOOEEIIUTYK3smE3S6M/T10SZ65DDjyBDIsh1FXXYHwqFh3To+4SVdDMR/bfeNcer9+Bi1+ndCIpNJVqFh3ABlXP+DS+yeu1X7oWz7mB8/CVO2ZP9YDFj0ErLoQE5rXo6okD9GJJ9bHI8rDgl4xMTGIjIyE1WqFlLD5bty4EXPnzpX0clM2d8q4I2ck8eA0IYQQQoaGgncyK2Dc38y7qMThKNSkwGS1o/TgISyi4J0kaVrK+NgVmOjy+14SXo0plWuRV1wIgIJ3YhZft4WPmgyhGc2ZpI2bjcPfjsMoy34UrX0Z0Tc964EZEilgwSOpBZDYfLu6umA0GiUdvCOk38tmqWMFIYQQokjSLBBDztCwov9PzndT38MSy+P4pCyEnlGJesa4ArPNz6EhY7nL73vY7F/wRhjptnyeoUXEqbaimDefYa9VyqSF/foZ0/jr+Zhe9j9YzCY3z5AQQqRh6dKlCAkJwRVXXOHtqRBCCCGEnICCdzLB6v4MNHg3f0wyHzfn16HTYnPX1IgblTSaUeaIRGR0gsvvOywqHrn6Ufx68ZZPXX7/xDWO7VnLx3xVEgJCWPuSsxt7/q9Qh2CEowkHf/iQXgpCCAFw++2347333hN5zTtKvSOEEEKUiIJ3MuHcl+vvsllmZEwAYoOMUHd1YOehXPdNjriFpcuOyhYhayo+xMctj9GSdD4ffY/94Jb7J0P3lXkirrI8hG/8f9Hvn9HpDciLv5xfN+57l14GQghhJzXnz0dAgDgbNKm6F85S6I4QQghRJgreyYS9e29ONcAi5Q+Gb8Q+w++g3/KUu6ZG3Lhc8mnty7hH/znC/fVueYyYyZfycUTnPrS3NrnlMcjQ7CztwDb7KLSHjh3Qz6UsvA0ru+bh0dZLUN7USS8DIUTUWGOSiy++GLGxsXz/5YsvvjjlNi+//DJSUlJ4DcRJkyZh06ZNkAvqV0EIIYQoGzWskFnNu4Fk3jGJqZkwlFuRUr8RDrsdKjXFc6WipTIPl2s2o1wVxQ9k3CFx+Hh+/3GoxuFt32DCwt+45XHI4LDl7kerW/n1JP+B5WOwLrOrEu/HjsIGfLG3HCvmp9HLQAgRrfb2dowbNw7XX389li1bdsr/r1y5EnfeeScP4M2aNQuvvfYaFi9ejOzsbCQmCk2dWEDPbDaf8rNr167lQcGBYPfT+75aWlp6OiC7o2sz20djumw2yXWFdgXnNitx2xnafnr9e78PlIbe/8p9/yvltbf2c/soeCczA43hDJ9+ITp+NiBaVYe8g9uRPm6mu6ZGXKy9roSPzdoIRLrp2WXB3LLwOXDU/IQjpdWY4KbHIYNTeHgHHtB8gBz9WATpMwf880snxGF7YQNW7S3HbfNS3RYEJoSQoWKBOHY5nWeeeQY33ngjbrrpJv71c889hzVr1uCVV17B448/zr+3e/dul70Q7D4feeSRPgOBvr6+cLXycnZyVY38/HysNim3idS6deugZLT99PorGb3/lfv+l/tr39HR0a/bUfBOdt1mB3bwbfT1xz6/SRjfsRW1u1ZR8E5CuhrL+dhhjHLr41jn/w2z39uPyCojfulwUIBHRFpzfsRvtaux19CAEtXAg3eLx8Rg5Zdf4+KGn1FwUI+0sTPcMk9CCHEni8XCA3P333//Cd9fuHAhtm7d6pbHfOCBB3D33XefkHmXkJDAHzMwMNDlj7d51UGgphKpqWlYcl46lIZlJbCDtwULFkCn00FpaPvp9af3P/3+K/HzTymffS3d2ftnQ8E72TWsGPjPWtMuAA5sRXjFBpfPi7hRayUfuvyi3fo0TxkeC1/9YdS0mnG4ogWj44Lc+nik/zTV+/nYGT5mUE9boFGHh4LXYlLbT9i+JYSCd4QQSaqrq4PNZkNU1Ikns9jXVVVV/b6fRYsWYc+ePXyJbnx8PFatWoUpU6b0eVuDwcAvL730Er+wx2fYwYU7DjDUak1PRrycD2DOxl3Pr1TQ9tPrT+9/+v1XIrl/9un6uW1U4ExumXeD+NmUmZfD7lBhuC0PNeVFLp8bcQ99uxC8Q2CMW59ig1aD2Wnh0MCGnfsPuvWxyMCEtuXz0RA3sGYVvWnGLedjSvU6OOzCwSchhEjRyasPHAPMFmfLbGtra/nylbKystMG7npbsWIFr6uXlZUFd+rZDGo3SwghhCgSBe9klnk3mJpV4dEJOKobwa8Xb/3c1VMjbuJrruWjPiTe7c/xL8OLsMfwO8zec5fbH4v0T5fVgviuUn49PHX8oJ+2EbMvRbvDiCjUI2/vRnr6CSGSEx4eDo1Gc0qWXU1NzSnZeFJFFUkJIYQQZaPgnUw4T8QOtNusU3Hqb/Co9Wp82iwE8Yj4BXbV8dE3PMHtjzVm/BQEqTqQZs1DXZUQMCLeVV54GAaVFR0OA6IThg/6fow+fsgJFBrVNOz61IUzJIQQz9Dr9byT7MkFrdnXM2e6txEXWzKbmZnZryy9oXDu3jko9Y4QQghRJAreya5hxeB+PvGca/CWbTG+Ktag00JL58TObnfgPPPTmGX6NwJSJrn98SJik5GvSYVa5UDhti/c/njk7OoK9vGxTJcEtUaohTRY6sxL+JhYtR4Ou52efkKI6LS1tWHfvn38whQVFfHrJSVC53XWPOKNN97AW2+9hZycHNx11138/2655Ra3zstTy2ZPXmlBCCGEEPcrbejAqr1l2FXcAG+j4J3MgneDaVjBjIwJQGyQEeYuO7YWCBldRLzq2y3otGtQoYpAZIhnGkjUxczjo7ZgrUcej5yZuTKbj03+aUN+qkbMuRydDj1iHdUoPLSNnnpCiOjs2rULEyZM4BdnsI5df/jhh/nXy5cvx3PPPYdHH30U48ePx8aNG7F69WokJSXJI/Oue+Esxe4IIYQQz8kqbsBdK/fj3z/kwdsoeCcTx8/EDi56x2rlXZRuxC80P6Ft0yuunBpxg+oWEx/D/Q3QaTzzaxw6UcjOGt6aBYtZeHziPR8Zl2OG6QXkZa4Y8n35+gchx38aqhwh2H84xyXzI4QQV5o3bx5vQHHy5Z133um5zW233Ybi4mKYzWbs3r0bc+fOdfuL4OmGFZR5RwghhHhOS6eVj4FG73e7peCdTDh35gabeccsiajFU7rXMafiLeo6KXKdBdvwjO5l3KBf77HHTBs3B/UIgr+qE0ezKPvO2wrrOlGJMETFp7vk/qrOeRIzzC/gpYqhZ/IRQghxrePNZin3jhBCCPGUFlMXHwN9KHhHXMS5MzfYhhXMiGmL0OrwQSiaUbCPuk6Kma3qIC7XbMYMx36PPSarq1YYLBT+bjv4rccel/Rd87Corp1fHxbh55KnaM7Y4dBptCiobcfR6lZ62gkhRETLZgdd1JgQQgghg9bszLzz0cLbKPNOJuyOoe/bGY0+OOI/jV+v3/Oli2ZG3MHRXM5Hs2+0R59g+5jl+HfX5XijZbpHH5ecqLbyGJ7G07hb9zkSQn1d8vQEGHWYnR4ONezYsvcQPeWEECKmZbPOK5R4RwghhHgMLZslbuw2O7Qzs7a0RXyMrNzgknkR99C2V/HR4R/j0ac4c9ZFeMH+C6xvjMSxeiHzi3hebeF+LNHsxFLtNpfWPPxVZAl2Gm7DzF13uOw+CSGEuA7F7gghhBDPaTEJmXdBtGyWuIwLat4xabOWwuZQIcVWjNrSoy6ZGnE9Y2c1HzXBsR59ell21qSkEH5949Fajz42Oa69MpeP9cZElz4tY8dNRLiqBenWo6ivLqWnnBBCRIIaVhBCCCHeXDZLNe+IyzPvhnY/4ZExOKLP5NePbf3cFVMjbhBoFQJnPmEJHn9+z0v1wyL1Tqiz/uPxxyYCR0MxH00Brg3eRcalIF+TCrXKgYKtq+jpJoQQkdS8o4YVhBBCiOe1dAoNKyjzjri85t1QGlY4NcafD7tDhYZyyrwTqxB7Ax8DwuM9/tjnRbbjNf1zuKz+PzCbOjz++ATQt5UJT0Owa4N3TG3suXzU5a+hp5oQQsRS844aVhBCCCFeWzYbaKSGFcTFNVBc0YssfO5NmGx+BX9ouBKdFpsL7pG4ksVsQiCEenNBEXEef3KHjZ6GOgTDT2VGXtY6jz8+AfxNlfxpMISnuPzpCJ90KR8z2rJgNlFdQ28oPLQde5+6CKWPjMSuJ5dgwxFhmTwhRLl6Mu+o6B0hhBDiMbRs1s1efvllpKSkwGg0YtKkSdi0adMZb//zzz/z27HbDxs2DK+++uoJ///OO+/wM54nX0wmE8TC0Z1654ozsxnJCTAGRcLcZcfWgjoXzI64UoMJSDe9h5mWlxAYEunxJ1elVqMoeAa/3nr4e48/PgHCu4SGJYHRw1z+dKSNnYUahMJXZUbu9u/o6fawgxtXIfbTizChfRMSHBWobbPg+nd24fHvcuCgo3ZCFI9id4QQQohn2O0OtJlp2azbrFy5EnfeeSceeugh7N27F3PmzMHixYtRUlLS5+2LioqwZMkSfjt2+wcffBC33347Pv/8xHpvgYGBqKysPOHCgn1i25kbasMKZwDwvJFR/PpPh6lovdjUtZlhhRZdftFQu7DT6ECo08/nY0zNZq88vpJ1tLfC3yFkxIUnDHdLcLY4dBa/3nnoW5ffPzm9qtJ8JP9wK4wqKw4YJ2H/vLdQNPp2/n+v/VyIF3/Mp6ePEKXWvOvev6MgPiGEEOIZraaunoz3AFo263rPPPMMbrzxRtx0000YOXIknnvuOSQkJOCVV17p8/Ysyy4xMZHfjt2e/dwNN9yAf/3rX6cEtKKjo0+4iLNhhSsWzgKLkxxYqX8UdxxaBodNiDYT8QTvmDB/g9fmkDb9It6VONlewgMOxHPK24AR5ndxPl5BUEi4Wx5DPXY5Xu66BK+1zKQDRQ+q+u8fEKDqxBHtCIy4azXGzVuG25ZfgkcvHQUDLIj46T4c2vyVJ6dECBFJzTtCCCGEeKfenVGnhkGrgbd5v+qeC1ksFuzevRv333//Cd9fuHAhtm7d2ufPbNu2jf9/b4sWLcKbb74Jq9UKnU5oCdzW1oakpCTYbDaMHz8ejz32GCZMmHDauZjNZn5xamlp4SO7T3ZxNWtXd4DN4XDJ/Y9NT4FNVYogtOPorh+QMlEoYi9Wzm12x3MrOgUb8IzuPTQ4JsFqne6V7fcNDEO+LgMZXUdQvO0LhEXfAW9S0utfXNcKO9TQBsefst2u2v7hUxbg1+u1MLfYkV3eiOFRARAzObz+hQe2YHzHVh4U1172IlRqTc/2XDU5DlF7nsOi2g0o/yEbrWNmw+gbIJttHwqlbL/ct4+cnfPkLK2eJ4QQQjxb704MnWZlF7yrq6vjwbWoKGHJpxP7uqpKqBF1Mvb9vm7f1dXF7y8mJgYjRozgde/GjBnDg3D//ve/MWvWLOzfvx/p6el93u/jjz+ORx555JTvr127Fr6+vnC1/fVsp06D5qYmrF692iX3GaQdh3m2rSj66V3kVImnvt+ZrFsn/wYKjrwfcblmM35us5/yWnty+9X6TB68qz+6HatX9/174GlKeP03Vwm/6zpzs1tf/2H+auQ0qfH615txbqw0qixJ+fXflpOP39iTUa+PQ1NBBQ4XVJzw/10R01Fd8wXiUI2vX78P9uGXyGbbXUHu29/RQZ29lc416yoIIYQQ0l8t3cG7QCMF79zm5KWjrD7ImZaT9nX73t+fPn06vzixwN3EiRPxwgsv4Pnnn+/zPh944AHcfffdPV+zoB9bvsuy/Fj9PFfr2lcOHD2M0NAQLFky1SX3udNeAezfipHmA4hZ8i7EnpXADt4WLFjQky0pV1lvrAHaAH1oIq/X6K3tz04fjtnvz0WTIxY7F82Dzkv195T2+hvefxxzdVvQEnMZliy5yW3b3xCYi03rVmFSZxuWLPk7xEzqr39DuwX37tThY9s/8M214zAzse+yDHv0DYja9wDOaf8Otpl/h39wuOS3faiUsv3O7H2iXD0177w9EUIIIUQhKpuFBKaoQHH0OpBV5l14eDg0Gs0pWXY1NTWnZNc5sdp1fd1eq9UiLCysz59Rq9W8MHFeXt5p52IwGPjlZOzgwh0HGGxOjEatctn9Z8xeBuu+h5BoL0VdZQHCE0dA7Nz1/IqJzlQvXPGPPGVbPbn9Y0aMQIdvGdraLThY0YZpw/r+ffEkJbz+EQ27MUmzHdu1s936+p8f14Xr9f+CpVkLi+kB+AUEQ+yk+vqvPlwGq82B0XFBGJ2acNrbTb7oZhQfeBnJ9lJs/+pfmH7j05LfdleR+/bLedvIwFDDCkIIIcQzyho7+Rgf4gMx8F6qjBvo9XpMmjTplOUz7OuZM2f2+TMzZsw45fZsaevkyZNPu7PMdpz27dvHl9SKhfNMrKsaVjDhEZHI0Y/i149tPbH7LvEevVkI3mkCIrz6MqjVKsxJFxom/Jxb49W5KEmASVhOaQhPcevjxKeNRYUqCnpVF/J2fOfWx1K68qyvEYg2LJsYf8bbabRa1E+5l18fU/IBmuurPTRDQojXu812L5ylzDtCCCHEM8oahbIlFLxzE7ZU9Y033sBbb72FnJwc3HXXXSgpKcEtt9zSs5z1mmuu6bk9+/6xY8f4z7Hbs59jzSruvVc4QGJY7bo1a9agsLCQB+1YN1s2Ou9TDOzde3MujN1xTQnn89G3eK1r75gMmp+1gY+GoEivP4sXxrbjP7p/YcnuG709FcUI6xICNoHRw9z6OCq1GqWhwkkPc84atz6WktVXl+HBxoex23ArFqecPRl+wsKrUaBOgZ/KhJxvX/TIHAkh3u8227NslqJ3hBBCiIcz71zfswBKXzbLLF++HPX19Xj00UdRWVmJ0aNH86LurFMsw77HgnlOKSkp/P9ZkI+dPY2NjeV17JYtW9Zzm6amJtx88818eW1QUBDvMrtx40ZMneqa2nIu0b03p3Zx9C5m6lKsy/sJGzqm4mGrDUad91skK12ArYmPviGx3p4KJmUkIezHPUAXUFdVgvDoRG9PSdY621sRhmZ+PTxhuNsfzzBiIbBlFRIatsBht/OAHnGtgq3/w1SVA4WaZKTFnn7JrJNao0HNuFuxKWs91pQPx9vOMzeEEFmjhhWEEEKIZ5U1iSvzTnbBO+a2227jl76wrrEnO+ecc7Bnz57T3t+zzz7LL2LWk3nn4vtNyxiDa33/jIpmE87Lr8N5I/uuHUg8gwVQgh0t/IUOCPP+su2wqATkadKQbstH0favEH7Z7709JVmrKc0DOw3RAl8EhQhLlt0pffoSWDZrEIsalBYcREL6OLc/ptJoCn/kY23sfKT182cmLLkJt+1PQWOzFRtya906P0KISFDDCkIIIcRjumx2VDSZRJV5R2kUMmF3U+Ydq6HnDNitz6G6Zt7WarFhtPlNTDW9hOAoIZvU2+qi5/BRXfCDt6cie81VRXysV3tmyTRrUnHUOIZfr9j1tUceU2nB+ORW4cRRUKZQoqA/WAb08ilCluv7249nkhNC5MtZ847WzRJCCCHuV9Vigs3ugE6jQmTAqY1IvYGCdzLhcOO6ivNGRiJeVYPQw+/AYbO6/gFIv9W3WWCFFh2GCBgNelE8cyFjF/MxtXUnbF1d3p6OrLU316HLoUar3nPNStoS5vHRWrbPY4+pFMdy9/Bl0J0OPVInnDOgn716RhJmqLOxrPTvMDdT4wpClIIWyhNCCCHul1PZyseUcD/eqFEMKHgnE6wDLuOO99X05GB8rf8L7rO9gaK9whIv4h11bWY+hvuLI3DHpE2cz5dxBqMNefs2ens6srY74FwMN7+HT1Me89hjhs+6DvPNT+PG5htgsto89rhKUH1gPR/zjaNgMA4sHT8u2Af3B67BMs1m+FVudtMMCSFiQQ0rCCGEEM/ZW9LIxwkJIRALCt7JhLPmnauXzTIsw+tIwHR+vXHvly6/f9J/tuKteFb3En6j/l40T5tWp0e+/2R+vXH/am9PR9aqW8ywQ43gkFCPPWZqcjJMgSkwdzmwo0jodExcQ1MudKdsixpc8yPrmKv4OLFjM+w2CqwS4g2s2VlmZiamTJni1sdx7t05KPeOEEIIcbu9JUKTyAmJwRALCt7JRHfindu6kTmGC0sjoyp/ctMjkP5Q1eRgqWYLJtsOiOoJs6Yuwnb7SGxrCvL2VGStukUomhoZaPTYY7K6l+cMF5bp/pRLdS9d6V+WZbjHcgt0oy8d1M+PPveXaIEfYlT1yN3xnUvnRgjpnxUrViA7OxtZWUIw3l3ccG6WEEIIIX1gte4OlAnBu/EUvCPualjBDrTdYfjMS2FxaBBvL0dt0SG3PAY5O3ub0FnSbHR/p9GBSDr3JvzS8he8UDcRDe0Wb09HtpaV/x9e1D2PVPsxjz7uBXFmvKJ7Fsv23eDRx5X7EvjtTUH43D4X6WMGl3ln9PHD4dAF/Lpl1wcuniEhRIwNK5wnawkhhBDiHtsK6tFusSHIR4f0yACIBWXeyYTDzWdmw8PDka0fy6+Xbv/cPQ9CzkrdIQTv7L7iCt5FBxkxIjqAH1RsyhPmSFxvgjkLF2m2I9zo2aO3CcOTsVC9C6NtR1BdVuDRx5arfd2p+GmR/nzHYLACp/6aj5ktm2DqEArrEkLki2J3hBBCBqvD0oUb38nCyqwSehLP4H97yvh48bgYaETSrIKh4J3sGla4783VknQ+H/2KhSLrxPN0pjo+qv0jRff0s6WVIWhB8T5aWu0OrJNvqEMI+IREJ8GTgkIjkK8bzq8fy/rWo48tVx0Hv8INmu9wfoRQDHew0iecgwpHOHxVZuRspBMrhMhW9+4dZd4RQggZrLe3FOOHIzX40+cHofRlsdUtpj6b8ZU2dODbg5X8+uUT4yEmWm9PgLi6YYX7ntG4qZcD+U8h0XQEHW3N8PWn+maeZrAIwRtNgLgy75gLQyvwJ8OtaCgOgt32S6g1Gm9PSVYa6yoQrrLD5lAhJCLW44/fED0LKMuFupAFZ2/3+OPLTcKxVbhEtwXbNVEALhv0/ajUauw3TkNn5x7sOdaACS6dJSFELI7v3lHuHSGEkMGpb6PyRv/bU4Z/rj7CS9joNCpMHxaGy8bHYdHoaFi77Ljnk/0wd9kxLSUUExLE06yCocw7mWXeOWuiuMOw9JG4V/9nTDG/jM3HOt32OOT0fLta+GgQYfAuY/wMdEKPcDSh6PAOb09HdpqqhfT2BlUw7/DraUGjFvIxtTWLOpu6QHTHUT4GJE0c8n0Vxl+O8yz/wr/KMtFpoa6zhMiRu2oaE0IIUQ6b3Q4l+3JfOe7+ZD8P3DFWmwOb8upwz6f7Me6RtZj093XYWdwAH50G/1g6RnR/eyl4J7PMO3e+v9ib13/0ErTBF+tzqt33QOS0/OxC8M4nSOj+KSYGoy/yfIW8n5q9tLTS1drqhNoLTZoweEPaxPlodxj50ujCQ9u9Mge5aK6vRgyE2pDxmdOGfH9xATrEh/ii02rDBuoITIgsOXfvaNksIYSQwbI6gwYKVNdmxoP/E5YLXzsjCbl/vwA/3HMO7jp/OIaF+/GltOzpSY/0xye/m8HrUosNBe9kwhM175jzR7IlXsCPR2phV/Avv7de4/nW5zHF9DKMcaMhRubk+XwMLP/Z21ORHXNDOR/bDd4J3OoNRuT5jufX6/Z/75U5yEXZkV18rFBFIihk6Fm07GN/yegoGGDB4Z0/uGCGhChPaWkp5s2bh8zMTIwdOxaffvopxMS5e0d7XoQQQgbLZlPuX5GXNuTzDrJj44Pw14tHwaDVIDXCH3ecn86DeNseOBdb7j8Xa++aizHx4iwPRsE7mXB3t1mnqSmh+J1hLd6y3IuCnavd+2DkBB0WG9ptatQiGKGB4jsTwMRPuZiPw83ZaGsZWiF+ciJzexO6HGqYfbzXrKQzaT5229Oxt9HotTnIQVvZIT7WGIe57D4vTbZht+EW/L7kLnS0CbUxCSH9p9Vq8dxzzyE7Oxvr16/HXXfdhfb2dtE9hZR5RwghZLBsCv0j0m7uwsqsUn793oUZUJ/UKICtMIwJ8kFcsI/olsr2RsE7mbD3ZN6593H0WjXmBVVhrLoIzfu+dO+DkRM0dlh6XgO2Dl+M4oaNQpkqGjqVDXnbaemsK30X+Aukm9/DruH3wVsizl2BZZZH8GztxD67M5F+qs3lgyko1WVPWXpaBprUIfBRWZDzs7gyhgiRgpiYGIwfL2QXR0ZGIjQ0FA0NDRDdslnKvSOEEDJIXbbjNe+UtIru24OVPBGGLY+dky6+2vH9RcE7megJonsgUqzKWMzH2Jqf6RSwB3VUHsWzupdwn/5/oj4jUB42k4+W3HXenoqsVDWb4IAaYSHeS+NmqeXRgUZYuuzYWSSeg1qp8W0t5KMmMsNl98m6zpbGLBKu53ztsvslRCw2btyIiy++GLGxsfxv4BdffHHKbV5++WWkpKTAaDRi0qRJ2LRp06Aea9euXbDb7UhISIBYOP/uKzRpghBCiAt09QrYWXoF8uRu1R6h/NCySfGiPo4+GwreyYRzZ87dmXdMxsxLYHZoEWuvQmX+Pvc/IOHMdYVYqtmC87FT1M+IasKvca/1d/hn+8U9tRjJ0FW3CF2RogK9t2SV/bFjZ6sC0Y7cA9RReLDusN+DS82PQp8pnAhxldDJl/Mxo3UHzKYOl943Id7GlrCOGzcOL774Yp//v3LlStx555146KGHsHfvXsyZMweLFy9GSYnQqZthAb3Ro0efcqmoqOi5TX19Pa655hq8/vrrHtkuQgghxFNYUwYns1UZwbvmDivvIMtcMi4WUqb19gSIq5fNuj96FxISir3G8Zhg3oXyHasQky50GCXuZWmp52OHNlDUT/WoyfPw5WoLrE0OFNW1Y1iEOOvzSc39TX9Fi06LOA07cPVe3bulgUfwhOF3KM5JBnCR1+YhVa0mKwpb2XmzNCQnpbj0vtPHzUbNV6GIVDXgwLbVGDv/CpfePyHexAJx7HI6zzzzDG688UbcdNNN/GtWv27NmjV45ZVX8Pjjj/Pv7d69+4yPYTabsXTpUjzwwAOYOXPmWW/LLk4tLUI3eKvVyi+u5rALpQpYRqA77l/snNusxG1naPvp9e/9PlAaev+77v1vsnb1XG83meGrg+xf+/XZlTxoOTzSH9EBOlH+HvV3ThS8kwlngpOnkkDbk84Hju5CYAlbGvmohx5V2bra6vho1gVDzPwMWkxJDsXWgnr8fLSWgncuYLWYMdexG9AADUF+8KaMCbOh2eZAqq0IdVWlCI8Wz7IyKSioFQrgRwQYEOTj2j0mtUaDorC5iKz/Ap2HvgYoeEcUwmKx8MDc/ffff8L3Fy5ciK1bt/brPlim+HXXXYdzzz0XV1999VlvzwKCjzzyyCnfX7t2LXx9feFqeeVsD0+D8ooKrF5dBqVat07ZJTlo++n1F7uaTiBAB/i4IcpA7/+hv//LKtU9iy+/X/cDwiTSg24or/2HR4VtTtS1YPVqcTbc7Ojo34oZCt7JLPPOU2u4E6ZfDhx9AmnmHLQ1VMI/NMYjj6tkjg4h3ddqEHfwjlmUrMHI4tUI3fktMOtlb09H8hprK3iuHes2GxwW7dW5hEXGIV+TijRbAYp3fovwS27x6nykpuXwOvxN+wlqA2YAON/l928cfTHw8xdIqd8Iu83GA3qEyF1dXR1sNhuioqJO+D77uqqqql/3sWXLFr70duzYsT319N5//32MGTOmz9uz7Ly77777hMw7ViOPBQwDA12fIV/ycwFQUsAbayxZMg5Kw7IS2MHbggULoNOJPFXEDWj76fWXwvufrbi5499bYNSpcfBh1+3j0Pvfde//N0u2sz9Y/PrMOecgNcK7SQHufu0dDgceO/gzO82H6y+YiqnJoRAjZ/b+2VDwTm6Zdx5KvUsaloG9mtEotQTAP7cM586g4J27qTqF4J3dGAKxmxtrR4ruA3Q26WHqfApGH3H/YRC7ljoheNekCkS4CIIxtZEzkFZZAEfBBgAUvBsIXclGXKddi+1q9/xOjJixBE9v+BVWWyfgmYpWjEsQf7CfEFc5+QQm22nv70nN2bNn8yWp/WUwGPjlpZde4hcWPGTYwYU7Dq613Z/9KpVa1Afv7uau51cqaPvp9Rfz+3/HsWY+mqx2t8yT3v9Df/939KpzZ4d0/p4M9rXPq25FXZsFBq0ak1PCodN6/ziqL/3dNmpYIRMOOGveee4xV0/8D263/gHflEjjl17qNOYmPqp8xXnGoLfkkVNQg1D4qCzIy1rr7elIXkdDJR+bNeII3PqPXMjHpOadcAzgYJcAxqZ84WkIG+6Wp8Ng9EVBxk0ocMRhXXY1PeVEEcLDw6HRaE7JsqupqTklG8/VVqxYgezsbGRlZbn1cZwxSOoDRQghZLA6zMdr3pm7hJNOcra1QKgZz0o6GUQauBsICt7JhLNxjCcaVjidP1LYIf4xtwZdCmo17S06i3A2S+0XBrFTqdUoDp7Or7cfXuPt6UieuVkIwnRoxRG8S59yPkwOHSLRgGO5e7w9HUkJ7yzmo1/cKLc9xoJM4bN5bXb/lgsSInV6vZ53kj25Jg77+myNJ4aKZd1lZmZiypQpbn0cD56bJYSQQaHPKfHrsB4P2Jm75H/8ntXdZXZaiviTX/qDgncy4emGFcykpBAEGbWI7CzE4UP7PfjIyvSI30OYYnoZ7cOWQAo0w4VaF1G1W7w9FcmztQrBO7NBHIFbtgz6qM9Yfr1q73feno5kWMwmxNiFgFpUqvD8ucO5GVG4WLsdtzf8A+WFh932OIR4UltbG/bt28cvTFFREb9eUlLCv2b159544w289dZbyMnJwV133cX/75ZbbpFJ5p3qhJUWhBAiNh7MISGD1GE+HryzKCB4t7ekqSduIQcUvJMJVtfFkw0rGK1GjWdD/4e1hj/BuuVFjz2uUtV2OlCLYAQGS+PDJ23aRbA5VEixl6CqtHupIBkUi6kDVocGXT7honkGK4b/Bg9Yb8Qn7RO9PRXJqC7Ng1ZlR4fDgIiYJLc9TpCvDr/z3YiLNDtQtmOV2x6HEE/atWsXJkyYwC/OYB27/vDDD/Ovly9fjueeew6PPvooxo8fj40bN/KucklJ7vtd82TmnRMtmyWEEDIYLFhn6bVaTu6ZdzUtJpQ3dfKg8liZ1ICm4J3sls169nEDhs/hY3ztRtqjdLOmdisfQ3z1kIKgsCjk6Ubw6yU7v/b2dCTti8DfIN38Hg6MuANikTj9CvzXdh6+K9EoomaGKzSWHeVjtSaaLy13p7bE+Xz0PfajWx+HEE+ZN28eP1F58uWdd97puc1tt92G4uJimM1m7N69G3PnznX7vDyVeedEeXeEELFS9VoDZncenBLR6LScuL8u9/33vaVC1l1GVAD8DfLo00rBO5lwLqPwZOYdkzHrYl77KtpRg7IjntlxVSKrxYxH7f/GX7XvIkQvnbMkjbFzYHboUFV+zNtTkbS6dgvfJQoJ8IdYjIgOQLi/Hp1WW09KOjmzjlqh3l2TMc7tT1XMlEv5mNG5Hx1tQr1MQoh09eze0fEwIUSkeh+GWqmhmeh0WI83q1DCstm93ccnExLlkXXHUPBOJryVeRcYEIRsH2HZXGXWF559cAVpbqjBUs0WXKtZi0A/X0iF35wVGGd+HQ81LKamJkNQ12rmY3iAAWKhVqtwYZId12jWoG7rB96ejiT86LsY402v4efUP7r9sRLTx6FCFQW9qgt521e7/fEIUSpqWEEIIaey2uhMg9i096p3p4Rls3tLGvk4IUEaJaf6g4J3Mqt55w2dKQv4GFz6g9fmIHftjTV8bFH5QaOVTtrvqNQkGH390Wrqwr7u1GUycH9sehQv655DjENody4WF/ofxaO6d5Fe/KG3pyIJJQ2daEIAgqKT3f5YbFluSdgsft2cQ01FCHEXalhBCCGn6upVW42IQ7v5xMw7c6/Os3J8/x0oE1aeUOYdER1n7E7thTY/yTOW8THVkovm2jKPP74SdDQJwbtWVSCkRKNWYXaa0GRh8xF6bwyG3WbDLPtuLNHsRIi/uOodJk0ROh+nWY+iuaHW29MRvdLGDj4mhnome9Yn8wLh8eq3wEHLVwiRtJ5Vs5TMQggRKXuvD6jejRGIOLBkit7k/BrlVrfy0j4BBi1SI8RTdmioKPNOZh+W3gjexSUOw1FNGtQqBwq2/M/jj68EnS11fOzQSCt4x1waWYPv9Pdjya4bvT0VSWptqoNOJZwZC46IhZhExafimDoeGva7n/W9t6cjaix49of6f+Bh7XtI9mM1DN1v+LQLeWfbansgCkopeE6IHFDsjhAiVl29lsr2vk7Eoc0sND90MlvlG7zbXypk3Y1LCOalfuSCgncy4fx49ELsjjuQfhuutfwJ77VN9c4EZK6rTQjemXRBkJoJmSMxUl2CNGseGmrKvT0dyWmqq+BjC/xgMPhAbCrDpvPRenS9t6ciai2NtbhAtQ03aL9HbJhnCuf6+PnjnsSVuNTyd/xQfOIOGyFEYjXvVN4vk0IIIWdi7ZXJ1fs6EWfmnZxr3h2qEIJ3Y+Kld+x8JhS8k1nDCm8F74bNvBw/28fhh7xm2Xeu8QZbewMfLXrpdcsJj01CgTqFZ2YW7vjG29ORnLb6Sj42q8T5x8c4/Dw+xjbs9PZURK2mJJePdQiGj1+Axx53+sgUPm7IFZbeE0IkWvOue6TQHSFErLqcB6QUvBOlNrNyls0eLheCd6NjxXn8NFgUvJMJhxeXzTLj44MR7q9Hq7kLWcVCoIm4UKfwnNoM0gveMTVRQuF8Rz5lZw2UqamKj63aUIjRsCmL0OVQI8FRgcpjQoCKnKqlsoCPdTrPLn2enxHJx5ziCrS0tdJLQ4hUeevsLCGEDKJJBXWbFZ82kzIaVlhtduRUCfu8o2KlV3LqTCh4JxPOVRTe2rVja8mXp5jwgPZDmH540kuzkK9vw27AZNMrODDst5CigNFC4fyU5p28AQPpv66Waj6a9OIM3gUGhyFfPwJWhwb5B7d7ezqiZakr5GObT5xHHzcxzBcvBbyNLN1vkb/pM48+NiHEdahhBSFE7HoH7GjZrPgz7+S6bDa/po2vBGTNKjzVJM5TKHgnE95sWOF0blQHfqf9FqOrPqfOhi7WYGbL7YJgCIyAFA2fvIAXzg9HEwoPUYBnIMydbbA4NLD4hEGsto55DOPNr2NlyxhvT0W01E3FfLQGJXn8scNCw6FX2WDLXePxxyZE7jxd844QQsSqq1dnewreiQ9bIcf4G7R8lGupq0PdS2YzYwNl1ayCoeCdTPSc5/Di+3PkTKGzYZSjHiXZO7w3ERlq7hSKzQf56CBFeoMRuX4T+fXafd96ezqS8l3QLzHc/B6yht8LsRo9ZiLa4YOtBfWw96p3Qo7zbS/lozZMqEHnSf5jlvBxWNM2ynwlRKI175yoYQUh0lfXZsbTa3NR2tABOendYZaWzYp32WyYv17WmXeHK1r4ODpOXvXuGAreyYS3a94xvr7+OOI7iV+vylrltXnI0UX1b+Ov2ncRbS2DVLWnXozPbXOwrjne21OR3A4ei8oHB3quycFATUgMhp9eg4Z2C7K7uzuRE/lZ6oUxKtXjT83wKQvR7jAijGW+HtxKLw0hEqTqPjtLp0cIkb67Vu7DCz/m44pX5fU3mZbNilurSUgGCfNzBu/kWcrocPexiNzq3TEUvJMJZ7KLtzNDzakL+RhW/qN3JyIz55g24HrtGgRDugXnE+ddj3ust+K9quSePx7k7Op58A6I6D5LJkY6jRp3Ru7Bl/o/o3XDc96ejuiwbMQLLE9ivOk1BKZN90rm61H/yfx63V7q+EyIFNGyWULkY0eh0IiuukXYx5MLWjYrjZp34f4GPnbKsGGF3e6gzDsift5uWOE0bOblfEzrykND1TEvz0Y+/B1tfPQJFGfTgv4Wzk8O8+Vt5NnyStI/K+r/iVd0zyLWLnSdFasxoXaMUxfCv+xnb09FdOrazbDYHGhRBSAq1Dsp/NZhC/gYUr7BK49PCBkaalhBiIx4+4DNI5l3lCcsNq3dy2bjQ4QmDo3t8kumKK5vR4fFBoNWjWHhfpAbyryTWcMKlZdPzUbFJiFXO5xfL9zyuVfnIhesO6u/Q6iJ4RccDimblx6GUapiVOz62ttTkYwptj1YrMlCsFHce3rRE4SOwummgzB1tnt7OqJS2WTiY2SAkWcpekPS9Ev4mG49iqbaSq/MgRAyeM7dOwctnJUVVtSf6hgSuejdpIIaVog38y45XAjesXI3cnOkSlillhEdAK2X9rndSX5bpFDOP/xiWFZRH3suahzByKsUUsLJ0LQ2N0CtEl7fAIkH7y4LPIJvDQ9iUfH/UUfifrBazAiEELgNDI2GmCVlTEQtQmBUWZG/a723pyMqpqMb8Lruadys/85rc4iKG4av9Evwt65rsPWYUMiXECKdbrPOVB3nSgsifZ0WG6b/8wf88vXt3p4K8TARHK65RRcF7yQRvEsMPR68k9vJgyPO4F2UeGuFDwUF72TC+XvnzYYVTkHn3Y1p5hfxaPVsmGS4lt7T2prq+Njp0MNgFD5spWr41EWwOLSIddSgNP+At6cjes0N1Xy0OVSiD9yq1GoUBwkHr205FLzrzVF9CAs1uzEOefCmw+Mfxnu2RfihUMgEJIRIsNusRx6FeMLO4gbUt1uwo4hOdhN5sDqLsNOyWdFhQTpnt9mkMGE5qcVmR7tFXsfquVUtPZl3ckTBO5kQS8MKJjMxElGBvrwI5jaqbTZkHS1C8K5V5Q+p8/UPQq5xDL9esYsK559Na3fwrkXlD41WC9EbNp8PYdXy6p42ZM1Cl2irX4xXp3FORgQffz5aywv6EkKko+fcLP3qyobcMl5I/4kg18ItKPNOvMxddl53nIkIMMCoE8JADW3yWjqb2515NyJafp1mGQreyYSzBoq3a94553DeyEioYEfW/n3eno7kmVqEM7IdaukH75j2hHP46FPyk7enInodjTV8bFVL4w9Q8pQlfEztKkBTnbgbbHiSvr2Cj6rgeK/OY3JSKNL0jVjYuRr5OfTZTIiUeH/vjhDiKiqZ/kZ39WpS0fs68b7GDiFIp1Gr4KvTIMxP6Dhb3y6fjscdli4caxDKDVHmHRE1ZxKFCGJ33KVxLdhpWIHrc27mDRfI4JUGTsRk0yt4MuxRWTyNURMv4uPwzv0wdQhddEnfzC1C8K5d450OpQMVEZuMfZox+MY+HXvySr09HdHwMwkZlLrQJK/OQ69V4yn/j/BP3ZtoyFrp1bkQQgaGGlYQQqS1bPZ48wrifdUtQpAuMsAAtVqFUD/9CUE9OcivaeOlxML89Dy7UI4o805mqfdiqHnHjBs7Ab4wIwKNyN+/2dvTkbQmswN1CILV37tZO66SPGISahAKH5UFeVlrvT0dUTN1tMLi0MCkC4ZUfDn+Ndxu/QPWVRq9PRXRCO0SgrABkd4N3jGW5HP5GFz+s7enQggZRKYOrbSUj955SbSEVllEcrjm1mWzrJ4aEY/qFqHecVSgsH/uDN7Vy2jZ7JFenWblioJ3MuHcmRPL3wLWWCE3YCq/Xr/7C29PR9JaTFY+BvroIAe8sUHIDH69LZuCd2eyO3gxhpvfw6fJj0Eq5qQLjTU259d6eyqiYDGbEOZo4tdDY4d5ezpInHYJH9MtOWhupNeIEKmhhWjyZKM6pEQGaNmsFIJ3hhOCd6zjrNzq3WVQ8I6Inb0n887bMznOPnwxH6Mqf/T2VCQtpuw7/E37DiZZdkEuTBN+i6ssD+Gxjiu8PRVRa+Cp7CoEBkin3uHUlDBo1YChMQ9lZUKjBiWrqyqDCXqYHTqERsR6ezqISRqOYnUCNCoHCrZT0xhCJLdslqJ3suQsJE+IlFntx7PtaNmsNDLvhGMNuTWrCIBcUeadTPT8zRdRHnb6rGXocqgxzF6M8sIj3p6OZEU37MR12rUYZsmFXIybPBs7HKOQXWtGeVOnt6cjWo3dZ8NC/aSTdelv0OLDgBex3vBHlG/7GEpXagtBpvktXO73Ns86FYPKiNl8tOVS5ishQ/XSSy8hMzMTU6ZMceuTebzZLAV55HwSniiDeI7W3Jd5R8tmxaWq2ayYZbPDoyh4RyRCTJl3QWFRyDWM5tdLtn3q7elIltbczEeVj3Tqnp1NkK8O4xOE7dl4lJbunc7C8pfwmu4ZpJkOQ0rsUWP5qDtGddUqmllwWoXA4AiIhf+oC/iY3LQNjl5nyQkhA7dixQpkZ2cjKyvLvU+fiE7OEhfpFa+jZbNEDnpn28m926zd7sDuYw3otEijMWNN64mZd7HBwljeKI8kivo2M+rahAAlBe+IhJbNimvnri1pAR8Dj1GGx2DprS181PiGQE4uTejEX7XvImLLI96eimild+7DIs0uhGqEtudSETJG+L1PbdsNW1cXlKyiSdhZiuneSRKD9CkL0eEwINTRhIKjB709HUJIP/Ts3cn7eFixKHhH5Lb8W+7LZj/ccQzLXtmGOz7eCynWvEsI8eVjWZO0jjHOtmQ2MdQXfgYt5Eoca3iI7BpWOCXMWo5Xuy7CX9uXoUlGa+o9yWgTPox0/qGQkxlxWlyvXYNpTd/CahHOlJAT+dmErEtjkHiytvojbfxctDp8EIR2FB7cCiVLPfofvK57GrPtuyEWRh9f/DvmCUwwv4b11dKpp0iIkvXUvPP2RIhbUPBOWVQiS7Zwld4BO7kH7/79Qx4f12ZXQwqqmk/MvIvvDt6xk8y9uwRLVW61/JtVMBS8k1nmndj+GMQmZ+CL8N9hly0dPx6p8fZ0JMmnO3hnlFnwLnXsbDQiAAGqTuTv/cnb0xGlILuQdekfHAUp0er0yPebwK/XHfgeShbdtA8LNbsRp2mEmMSNOxct8MdPufS5TIiUOKg2miwDdjZ6XRVFXEdrrtN7qaxV5stm6yRUK665w4oWk7ASJjbYh4+RAQboNCr+OVTVnZUnZbkKaFbBUPBObpl3IvxrsCBTCDysk8iZCbHxd7Tx0SdQXsE7jVaLgoCp/HrTge+8PR3RMXW2w08l/DENCIuG1JgTz+FjQMUWKJm/RajpaAiNh5icM1zI5txV3IhWk9Xb0yGE9LthBZHjEkPKvFMYER6vuYKSMu+kpLCurSdgxxrLMWq1CnHdgbwyGdS9O9IdvKPMOyIJzu5jYmpY4bRgRDjmqfdhdt6TMHXKY129p9htNgQ42vl1v6BwyI0j9Vw+hldv9vZURKelQciIYh2bA4OkF7iNnSg0RRhuOoTOduEPqhIF2+r56B+eADFJCvPD7UGbsFL7MPI2rvT2dAghZyG2lRXEtR1mKXhH5EApNe9YswonP70GYldYKxxLDovwO+H7CaHC0tnShg7JZ6Tn17TJvlkFQ5l3MuH8DBFbwwpmTHww/k//Bn6tWoPc7d96ezqS0maxYZr5ZZxnfgr+YbGQm5SpF/MxvSsP9dVl3p6OqLR2B++aVIFQqaX3UZ2QNhZvaK7Eb633YFepMoN3VqsFoQ6hbmFwVCLEZmZgHSap89B1RNlLmwlhWltbMWXKFIwfPx5jxozBf/7zH3Fm3lHqnSwDHdT4m8gv806+H1YVzccz1UL99RC7ojpn8O7EOsfxIULmXanEM+9qWs1oM3dBo1YhKUwISMqV9I4IyZlr3onw+VGpNSgOm8Ovmw597e3pSEpzZxfqEIQyTQKMBvH/cRio8NgkFGiG8etFO77x9nREpaO1ARaHBm3qQEgRCzjmjvg9fraPw6ZCIYClNA3VpVCrHLA6NAiNEF/w3SdTyI5MatgKBx05EoXz9fXFzz//jH379mHHjh14/PHHUV8vZM6KATWskHf2DtW8UxYxHq+5vuadfDPviuuOZ6pZuuySWTY7LPzEzLuU7q8LaoX/l6qC7vknhPjAoBV/JuRQUPBOLnpq3onzz4FxtJBhlVK/iS8FJf3T3CnUogr00cn2KauJnI1yRxjyKhu8PRVRKfYbh+Hm9/BQ5AuQqtnpwlLvzXl1UKLmmlI+1qtCoNaIb2ciY9oFMDl0iEI9SnL3eHs6hHiVRqPhATzGZDLBZrOJsjmEGOdEXFHzTvwBAELOpqvX+1jOwbuGjuPNKkxWu2SWzaaelHmXHiksMc3r7tQqVQWn2T45ouCdzDLvxFjzjsmYcSHaHUZEogF5+6m+WX9ZK7PxV+27uFqzFnKlmv8AZpmfx9O1U044C610jXzHQIUAP+nWbpiZGo5Z6oO4pPY11NeUQ2maG+rQ6dCjWRsGMTL6+iPXZzy/XrmbMl+JuG3cuBEXX3wxYmNj+YnKL7744pTbvPzyy0hJSYHRaMSkSZOwadOmAT1GU1MTxo0bh/j4ePzxj39EeLj8as0SkWbeif/4n7iQWJMthnpiofdSWTkvm+0wC51bGZNV3EkpbH7OzLT0qJOCd91fs2W1Ug62FnTXuzu5pp8cUfBOJhwi/2NgMPoiN2Aav96we5W3pyMZjrpcXK9dgwW2gR2ASMnEYdHw02t5y/XsyhZvT0c0GtqFs3ohftJdLh0RYMCjxo9xi/ZrFGWthtLk+E3GSPPbeCHhGYhVZ+J8PvqX/uTtqRByRu3t7Tyw9uKLL/b5/ytXrsSdd96Jhx56CHv37sWcOXOwePFilJSU9NyGBfRGjx59yqWiooL/f3BwMPbv34+ioiJ89NFHqK6uFs2r4ty/o8Q7+aBus0ROTm66IoXlpIPF6qs5mbvsok4+yK1q5YHUUD99T3dZJ/Y1a7jB/r+4uy6eFBXWKSfzTugVTCTveMMKiJZ9+GJgz8+IrvzR21ORjK72Rj6atdKse9Yfeq0aM1LD8WNOJbIO5WB03HRvT0kUMoo+wGu6Leg0/YK1fYFU1UTOQGpVERx5GwD8FkpS3WLi2ZOhwcEQq9gpFwFH/w/DTQfR3toEvwDxzpUoGwvEscvpPPPMM7jxxhtx00038a+fe+45rFmzBq+88gqvX8fs3r27X48VFRWFsWPH8my/X/yCfQafymw284tTS4tw8slqtfKLqzlLjvDsFjfcv9g5t1lO227ptS1my5nfN3Lc/oGQ2/b3PlzrzzZJYftPzkBrM7nus1Bs29/aeXzZLNPWaYaPG7vODmX795UIZYlGxQSgq+t40NEpNdIPB8pakFPRhORQI8SmP9teUCMs+00MMYrmPTJQ/Z03Be9kQgo1UIbPWoau3Q9C3dWJkopKJMbGeHtKomfvEIJ3XTrpLp3sj1+G5eFfhntQsSsFWLTF29MRhciWg5ik2YXtjnMgZb4jzgeqPkJC0w7eFEGKnXMHq7pFOLCPChTfzpBTQuoYHFUNQ74tHIFHizF7krCMlhApsVgsPDB3//33n/D9hQsXYuvWrf26D5Zl5+Pjg8DAQB6IY4G7W2+99bS3ZwHBRx555JTvr127tqd2nivtr2eH+xo0NjVh9WrlZTI7rVu3DnJxsFJ4TZlNmzejJEBZ2z8Yctl+q1XTE8IbyO+zmLff1HViaKGmqc3ln1Vi2f6Dx9i+7PH92a+/WwN/D5QnH8z2f1cgzNWns7bP18PXIvz/15v3wVEi3mzJ0227xQZUNAm/T0X7t6E2G5LU0XG8CcqZUPBOJpyxO7VIl80ygWGRuD36bXx1TIs/F3TiJvE1XxQdR2cTH22GIMjZyMzxCN7VDn9LNlqa6hEYLM4aYZ5ksAiBW42/tGsupU9ZAMsGLaJVdSgtOISE9LFQioXHnsYiXRnUXXcDSIMYsWDq+2Pfw/s7SnB1qRazJ3l7RoQMXF1dHW8wwTLmemNfV1VV9es+ysrKeOYeOxnKLr///e959t3pPPDAA7j7bva7LWABv4SEBB4wZAFAV1MdrACOHkJQUBCWLFFehjrLSmAHbwsWLIBOJ48mXtVbjwHFufz69BkzMSExWFHbPxBy2/6/7d+A9i4h02bJkiWy2H5eqznreAkOCzRYsmSRS+5bbNu//atsoKKs5+s5885FTJD7TtQOZftfeoGdwGrDZXMnYkFm5Cn/35xViu1f5aDdGI4lSyZDbM627TmVrXDs3IYgHy1+cckC0ZYQOxtn9v7ZUPBOdg0rxP2GHT9mHL46lo112dW4ac4wb09H9NTmZj7ajfJeyhY3bCRKVbFIQAXyd6zGxEVXQ+mMXcKHuD4wAlLm6xeIQ4ZRGG3Zj4o93ykqeJfesQ8pmmM4aBB3Cv85GZE8ePfT0RoetJDqjg8hJ793B/J+ZvXw9u3b1+8n0WAw8MtLL73ELyx4yLCDC3ccXGo13bvsKpUoDl69xV3Pr1f0fm+qNf3aLllt/yDIZft7fy4NZHtEvf2qE5fNsi6sdpUaBq3rlpOKZftNXSeueOtyeOZzeaDbz+pnH+1u5jAtNbzPn52ULCRMHCxvgUajhVqkNbhOt+0lTaaeend6vXTrhPf3dVXO+iWF1LwT+zHXgkzhrPi+4ho0Nku7LbUnaC1CAEflI+/gHVMRPpOP1lz5dtYdCH+bELj1CTr1LJnUtMXO5qO+5GcoSYi9no8BEQkQsxmpYdBrVNA2FuJY6fHi/oRIBesKq9FoTsmyq6mpOSUbz9VWrFiB7OxsZGVlufVxnPt3EqiSQvqJGlYQOWGNGxi9Rt3zedXSeWqNNbk1rGA6RdpxdmeRsB86PMofYf6GPm8zPCoARp0araYuFNVLr2lFQY0w52EKaFbBUPBOJhzd/WbFHrxLCPXF40GrkKX/HfI3vO/t6YiezioE7zS+IZA740ghtT6pYRuvjaZ0QQ7htfcPce+BpyeEjBVe2+D2InR1iXMHx9VMne0IhnC2MyQqGWLmZ9DizaA3sMFwD6o2v+ft6RAyYOxsO8ucO7kmDvt65kzhxJC7sKy7zMxMTJkyxa2PI/LdOzIIvTtUOlfQECJVzZ3CKoNgXx38DUKmcItJ3CsPBqvD0nVKlqEYbS8UmlVMH3b6ckQ6jRpj4oTyTLuKhdtLSUFtm2I6zTIUvJMJ5998KSx3SokIRKCqE5q877w9FdH7P797cZ75KbQlL4TcpU9dBLNDh2jUoiTvAJSss70VPiqhk1VgmPSDd2ljZ2EZnsJ801M4WNG/mg5SV18pZLB1OvQIDA6F2Olix/HRt+R4vRpCxKStrY0va3UubS0qKuLXS0qE3zVWf+6NN97AW2+9hZycHNx11138/2655Ra3zstTmXfO6J3zZC2RV+Zd7+tE/sR/tDZwTR1CoC7EV48gH2EJYEt3QE9u2swnLxEW34lpVjbip9wafn1m6plric9IFeprb8qrg9SUNAiNHpLDXN8oSowoeCe7hhUQvbDJS/k4om0Hz04hp1du9kGBIw5+QdJuWtAfvv5ByDOO5tcrd38NJWtqrIXZoYXFoYWfv/SblWi0WkSksk4IKmyW4I7BYLTUCgGFenWoJDrsxky6iI8Znfth6qCSBkR8du3ahQkTJvCLM1jHrj/88MP86+XLl+O5557Do48+ivHjx/NusayzXlJSEuSg5+QsxXjkmXlHwTvFkstr39QpnHQO8tUh0Kg7IRtPbtpPWjYrxuBdQW07ius7+DLm2elnrp89N104ztycXwebxN6PZY0dPav7lED8RxREVg0rnFk41QiDr8qM3G3fens6oub8o+c8gyV3Nam/wL+7luLLluFQsnpVGDLM72KB/l1JBH76Y7ZzxyCvFkrQXid0IWvRSiPwnpgxAVUIh0FlRe4Oyoom4jNv3ryeTrC9L++8807PbW677TYUFxfDbDZj9+7dmDt3rtvn5ells9I6rCJnQjXvlKv34Zpcsi4buzPvgn10CPRxLpuVZ827ju7gnY9OI9plsz/kVPNxempYzzLm0xmfEIwAg5ZnTx4qF2puS2X5cl2bEDROCKHgnWS9/PLLSElJgdFo5DVQNm3adMbb//zzz/x27PbDhg3Dq6++esptPv/8c75zxjqLsXHVqlUQZcMKiB8LRhSFCzvUpkPKzrA6E1b37XbLG7hL+ymCtcIHk9zFn3MNnu36Bf5XFijKs1iewrpDsd9mH79AyMWcZD88p3sRz1b+Bu2tTZC7jrZmvmS20yiNbsHsc/lYqFAbzJS9xtvTIUQyPLZsthuVRpOP3hkucgngkP5Sya7eYXOHRUHLZoXgXZi/XrQNK9Z3B+/OH3n2xndajRoz04SltRuPSucke3ljJx9Z4NEZMJY7eaR09LJy5UrceeedeOihh7B3717MmTMHixcv7qmJcjJWM2XJkiX8duz2Dz74IG6//XYerHPatm0bX45x9dVXY//+/Xy88sorsWPHDohG9we/FGreMb6jL+ZjasNG2G3i+8ATg/a2Zlyn+R53aFf1pJ/LXXqkP2KCjLxj1Y4i6RVNdZXGXjtAcpEYGYqp2nzEquqRv+vEovJytDlgMUaa38aa9L9BKnQZQmORuLot3p4KIeQkx1fNyuNAn5wYvJNLAIcMnNSWKZ41867Xslk5NqxgGd8dFuHY1dnBVWwJBywJYPexRn79vJH9q509d3iE5OrelXYvmY0P9ZVMDGSoZBeifOaZZ3DjjTfipptu4l+z+idr1qzBK6+8gscff/yU27Msu8TERH47ZuTIkbyuyr/+9S8sW7as5z4WLFiABx54gH/NRpatx77/3//+F2Jgl1DNOyZj+mK0bfBBuKoJufs2ImPSfG9PSXTamurA+uZYHBoYfZXRQYd98J4/zA9V+zejZnsRMPz3UKKAwtV4Xfdf1FtZhup0yAHL7CoLnorYxm/QeWQ9MP8XkLPqFhM/sx4eHACpSJ++BNatGsSjEuUFhxCXKtSgJOR0WF05V/DE8lZ3LptlF5ubT0RKZPeODAAtm1UuOS6bdTasYDXvnNskx5p3LMHAuX3hfnpRBu82HKnhsYGRMYGIC/bp18/M7a6Lt6ekkQddpZA4UtadeZcQ0r9tlANZBe8sFguvcXL//fef8P2FCxdi69atff4My6pj/9/bokWL8Oabb8JqtUKn0/HbsK5lJ9/GGfDrC6u3wi5OLS1Ch0V2n+ziajaHsNae7Ty64/5dTa3RYWPoMuTUmOBTqsKwsUObs3ObpbDt/dVSX41oNqoCEMQOCs5wYCCn7b/EPxtT9M/gWHE8rNbf9etn5LT9jLE+GzM1u7HdntCvbZLK9quGzQN2f4PI2m0unasYt7+qWdihCPPVuXVertx2o28APgq8ERvrA3BOmQpXJYrn+ZTSa+8OYt0+VoduKGe7WQYD+3l3B77cvWyWXdh+XlCQ+xoMOZ9nStCSj97ZdnLJviLKbVjR3Hl81UiXTdimls4uWTerCBVp8O6LfeV8XJjZv6w7Z8OH1Ag/3uiCBf8uHR8HsSvt7jQbr5B6d7IL3tXV1fEdwKioE9+o7Ouqqqo+f4Z9v6/bd3V18fuLiYk57W1Od58My/J75JFHTvn+2rVr4evr+jdYWxsrmKnCnt270ZovjT8Cu/wW4X2bBtH72xCnWu2S+1y3Tj7L8UxVR8DaNrTCF1tWr1bM9lvNRkxwqJFkL8MnK9+DIaD/Bf/lsP2Mb3en0roO8G6Jctl+a6cPJjlUGGY/hk8//Qh6v2CX3r+Ytv/myhdwk86Okv3LsbqcheHdy1XbvstnDn6wa1C5rRBBTfmQCjG99u7Q0SHsoIrNjz/+qJilKt7W07BCGrt4pB8o8065ev8ey7Fhhbk7mCXHmnfOJbOsWYVfdyMIMdW8K2/q5F1jmSsmxQ/oZxePjsGLG/Kx+mClRIJ3nXxMCKXMO0k7eUfSeWZ3ILc/+fsDvU+2tPbuu+/u+ZqdkU1ISOBZfoGBri9C3xZRgp37D2PpgtlIDJfGMq1ZnVZ89MRPqOoERk2fh6QhtHhmWQns4I0tb2bZknKwf30jUAmYtYG8LqOStj8v7wWMtGYjUd+ASUuuOevt5bb9B/LeAixAWHwaJp/ltZfa9hc+8QzSbAVI9G3HxCW/csl9im372d8H656b4acxoXjW025dfurqbR9W1YqvX9qGonYtzlswH4buTmpiJbbX3l2c2ftizLxTOk8tm3VG7+RxmE9OzriyUVRWsVmXcql32NRdr7n3stna1uOr0OTWrMLPoIFBpxZdt9mVO0t4cHj6sFCeTTcQS8YIwbufcmt5hqEzOClWZU0diuo0y4j7FRmg8PBwaDSaUzLiampqTsmcc4qOju7z9lqtFmFhYWe8zenuk2FdadnlZOzgwh0HGFdOSYR/7SEeuJPKAUy4TodzkowwHtuA/K31SFt69iDN2bjr+fUGR6fQqtusC+z3Nsll+5ti5wLHsmE49hN0uvv6/XNy2X6DVejGqguMHND2SGH7ayNnIK2yAOqin6HT3ebS+xbL9rc2NyBAxWreARFxKR6Zk6u2fXR8COb7l2CSaRvy92swfsaJZSXESiyvvbuIddsmTpyIf/7zn7jgggv41++99x6vX5ecnAyl8NSy2R4yOdAnJ2ZcyWXpJOmf3sukbTKreceWzRq06p4sMLlx1vELMOrgqxNCKR0WcSwPZst3P9whrN75zfSkAf/8yJgAJIf5ori+Az8cqcEl42Ihhcy7eAVl3smq26xer8ekSZNOWT7Dvp45c2afPzNjxoxTbs+Wtk6ePLlnZ/l0tzndfZL+uzlwG17WP4/EnNfpaTuJrUPoEmTVuT5TU+zCxgnZZultu2G1yO+s3dn4dgfvDIFC8Vg58c9chIP2ZGxvi+zJcpabhiphx6nV4QO/ANcuDXY3llF+a8Am/F77JUz7jnddJ6Qv+/fvR0PD8c7g119//WlrDJMh/m52p97J81NTmXoHbeSydJIos94h259r6jzebTYmyKeneZcctq+3mu5swogAA0L8dCcELr3tf3vKUd9uQUyQEYtGRQ9qH5Bl3zGrD1RCzFpM1p5AqpIy72QVvGPYUtU33ngDb731FnJycnijiZKSEtxyyy09y1mvueZ4hhf7/rFjx/jPsduzn2PNKu69996e29xxxx08WPfkk0/iyJEjfFy/fj3uvPNOr2yjnCTNvIKPGeZDaKwV94eEp+0MuRjnmZ/CxnjhvaskaeNmoxGB8Fd1Im/PBiiNv11YIucTHAm5SZ9+IZbZn8DT7RegoLYNctTaXbOwQSNkb0uNdvgCPsbUbvb2VIjIsbrAhYWFPV/LNSAvBlRaUH56BzXksnSSDGLJtAyCWw3tlp7tYE0cIgMMUKuEoHRdm7xOwte0CCsrogKNCPbV92y/t1m67Hhpg1Cr+KY5w6DTDC7M4wze/Zhbg0YRbNfplHVn3bH3m9iX97qS7LZ0+fLlqK+vx6OPPorKykqMHj2aF3xPShJSR9n3WDDPKSUlhf8/C/KxmiWxsbF4/vnnsWzZsp7bsAy7jz/+GH/+85/xl7/8BampqVi5ciWmTZvmlW2Uk5ikDBRoUpBqK0L+ls8x5bLfe3tKolFrNaDAEQdHcCKURq3RoCBwKia3rEdt9iZgurAkSwkcdjt8HJ28vpF/SP+7REmFUafB1ORQXkx3c14d0iKlUaNzIDrrhS5frbr+N1sRk9TpF6Nr+928aUzlsVz+OU1IX1gd33/84x/Ys2cPgoOFLNPXX3+dn+A805l9dpJULjxV844aVsiPHJdOkv7pXeNQDlmXzuWxLBvNoNX0BLcqm02oaOrk1+WWeccClKHdwTsxZN59vqeMvw7sNfj1tMEfO46KDURmTCCyK1vwya5S/O6cVIhRaaOz06xylszKMnjH3HbbbfzSl3feeeeU751zzjl8x/NMrrjiCn4hrlcTex5SS9+ANu87ABS8c3KmAgf5iLPWkbvVT7oTc7+/EEHtw3EOlKPdascY8xvwgRl7ouRZN2pWWjh255eh9PBWYFYK5KaruYKPnUZpZk4GhYQjW5+JTOshlO74EjFJf/T2lIhIPf3003xcs2YNrw3MAnMbN27kF6UE7zxV886ZeeeghbOyQcE75bL36m8gh6xLFqBj4oKPB1LY0k0WvGOXCZBj5p2BLxFmGrqbdXgz6+7FH4Wsu1vOSeUnygeL/Y2+dmYS/vT5QXyw4xjP4tOwNEqRKWvsVNySWVkumyXSEz5pKR8z2rJg6pDnMrrBmFjzP9yl/RTxXcegRBMmTkGJIwoHy5tll3J/JkKKugoOnS98DPIM3J4X2Yp9hptxV/k9sqxp2NnZiQ6HAV2+A683IhbNcXP5qC9W3rJ10n8hISG83Eh5eTnPPGPLZj/44APY7fbTXtzelVXuNe+kf5xP+si4osw75WbeyeG1L28ynRK8i+2+7gzsyUV1i7DfyrIJ2ZJNZ6ddb5aN+J+Lsu6cLhkXx5NHWEOIn3JrIEaV3e+r2GD5ZHX2BwXviNeljZ2JaoTBV2VG7rZvvD0d0ZjeuhZ3aFchyioswVOayAAjT91mNuXVQimcdTNYty65SssYgw6VD69pWLD3Z8jNZ/5XIdP8FnIzb4dURUy4iI/D23fDYpLXjjdxHVZm5OjRoz1f//Wvf8XYsWPpKXYj6R/mkz6bFlBUVlHklnVZ0Ucg5XjwTgjsyUV1q+l4w4rufXWrzYE2s3c6zpq7bHjBRVl3Tj56DZZPSeDX390mziSS6u7ly3Jakt0fFLwjXqdSq1EcLiyMrM3d7u3piIavrZWPev9QKNXymGq8pnsGkT8/AKXoKtmJ/+iexm2qz2Vd07DQfxK/3nRoLeSmqpnt2KkQFewPqRo2egbqEIxO6HH48F5vT4eIFKsXvGvXrp6vWb3hAwcOeHVOckUNK2SeeWeTfgCHDLxZhfyCd8cz7xJDheWMRXXyWlVV2yvzjgW5jDq1V+vevbu1mGfdRboo687pN9OS+N+djUdrUSjCBnPVvRqHKAkF74g4zLwds83/xgONF53yR02p/BzCB6VPoDQ7VrrCpDhfLNLsQmbTT7ArZKmVva4ACzS7Md6RAznrSpnHx6CqLZAb55KKSAnvUKg1aryS9iqmmF/B99UhUARTC9DR4O1ZSIq/vz/a29t7vqZus+5HCVryYetV+Iwy75Tj5NdaTg0rei+bHR4lNCQ7Wi2+wM9gdVi60NqdYceCZYwz+67RC3Xv6tvMeOEHIevuvkUZLsm6c0oM88W5GULt5ve3HxN1118lkWXDCiI9E8aOQ/OXNWhtNWN/WRMmJCrkYPEMHUcDWPBOBfgGKTd4lz7pXLSt8UGIqgV5B7ciffwcyF1XWx0fLXqhc6NcJUxaAhz8G9ItR9Da3ICAoFDZ/O6+1HEfanWBiNZ/yHbrIFXjxoyD/dBe/Hy0Fg8sGQm5aqksROV/f4+MFiGQfMxnFEKuehWBibT882zGjBmDF198EVFRUT3dZo8cOXLGhhXM3LlCTUU58Fi32Z564dI/0CenZlzRiWvlOLlBhRwaVpQ3npp5NzzKvyew127ugp9B+mEH1nyD8dNr4N+9PSx4x77vLHvjSc+uP8qDiazM0LKJ8S6//6tnJOGHIzX4bHcZ7l2YIZrX0OFw9Ko9KARRlYIy74go6LVqnJMRwa+vO1wJpevsaIVeJRwIBASHQ6n0BiPy/Cby63X7voUidNTzocsg7+BdbHIGylQx0KrsKMhaA7loqq/GeHU+Fmj2IDxE2q/hnLRwsAZjR6paUNnQAjmqL8mB5fXzegJ3TFLnYajeugDNJYe9OjcpeOKJJ3iziqVLl2L+/Pm8S90//vEPfr2vy7x58/goJ6zTbHZ2NrKystz6ONSwQt7BOzlkXw1EZXMnvtxXji5br7arCuw0K4dls6xZQ3134Co53K/n+8G+el4XjsmrkUf2XWlDBx/jQ3z53zsmxE/nlWWzm/Pq8MH2En79zxdmQu2GjrBz0yOQHOaLVlMXvtgnnhrsbeYudFptPTXSlUQc4VNCAFyaZMHSnKeQkNUCLN6j6OektakOrFKE1aGBr5/QtEGpLMnnAtlbEFx+5kwOuVCbhGV7dl/5B23LQ6chvv4LmHPXA+dfBTlorC7huXaNCESIUdrt60P89Phb6DosbPsCpRtuR8yyuyEn7KDxo88+wS32ZhSqE1BzwWtQGQPg/8WNGOU4ivwPbkTgA9t6dtDJqWbPno2CggLs3LkTlZWVuO6663DzzTdjxowZ9HS5ibQP88lpmxbIIPtqIC54bhOaO62ob7PghtkpUJKTX2upB+/yuwNzMUHGnmy03tl3ta1mHK1uxfgEaZ/QZEq7MwwTQo9nGDqXzXoy844FTO/9dD+//qtpiZiR6p5VWiwgePWMZDz2TTbe23oMv5qaKIp9oururLsAo5bXHVQSCt4R0ZiamQbftQegs9tQln8Q8WljoFQdzcLSyVaVH0LVyk6QTZh6MZD9GNItOWhpqkdgsLyXEetMjXxU+8l7Oxn7mCvxt3WBKOqYiWmQh9baUj42akIlvGD2uPQwPaLbG1FZ+AMAeQXvXv25AE/XTMZWw2N44toLMD05lX8/P/BTfPj2fXiu5VI8drgaF4yO9vZURS0oKAgLFizo6Ta7ZMkSXHLJJd6eluw4j5cUFuORNSUvm2WBO2ZjXq3ygncya1hR0N3MIC3y1CZdGVGB2JJfj+wKeWTvlzUez7xzCvUTgnf17UJAyd3YZ8UfPzuAqhYTUsL98OcL3VvW5IpJ8fjXmlzkVrdiR1EDpg/z/vFJjULr3THKjgoQUQkKCUeuUQjYlW2Xb6fN/qjVJ+Bc879wv88jUDq2vLJEHScsr9zxDeTOYBWCd1p/+WfeZU5diHftF+Dn+sDuDq3SZ24UlhW06oQyAFIXOv5CPqa37YbV4pkdU091xntxg1Dk+crLLkVSd+COSUtJRuWsv6MWIXh6bS41YRiAoqIiCty5ifdzHYg7M7CUtmzWSa9R3qGo3LrNOjPvUiNODd6NTxSy7faUCPu2UlfW4My8Ox68cwaQnNlg7vbShnysza7mvzvPLR8PX717c7GCfHS4bEIcv/6BSBpXVLeaFFnvjlHeJyYRtbbkRXwMOrYWStZoUaPQEYsa/wxvT0UUyiLmYqc9A/sr5RHgORN9l9C50RAoj+DPmQT56jA2Lohf35wvZJtKna25go9mH6FDl9Slj5vNlwD7qzqRv5tl38nDrpWPI7WrAFOTQ3HZeGGntLebzxnGC1KzOj1bj5R5ZY6E9OZcquSghbOy0WVTbuadk8GF3TGlQq7LZvvKvJvYHbxjmXem7hplUlbanXmXEOLTR/DO/cco2wsb8Mz6o/z6Y5eNwjgPLUX+9bREPq45XIW6Nu+fyK1rFZYoh/tT8I4Qr0qauYyPwy3ZaKwVDoKVyLmcINhXKIKqdOb5j+BKy1/xn6p02WfBXKX6P4w0vQV18iwowYJkDZZrNgA7/wM5ULdV8dHuJ4+llmqNBgWBU/n15oPfQQ6qig7jworn8a3hIfxllrHP+i2BRh1uHqPG27onEfXFcq/MUwrUajW0Wi0sFkvP1xqN5owXdnsycM53qcz/BCqKUmve9d6Po8w76b/2R6uF4F16H8G7uGAfRAYYeGbpgbJmyKVhxYmZd0IAqcbNmXfsbfJ/a4/ycfnkBCyfIgTUPGF0XBDGxQfBanPwzrPe1thhOaHeoJK4bA+Kddo6fPgwampq+I5wREQERo8ejZEj3bsOm8hLTFIGCjTDkGorRP6WzzHlsj9AifzKt+Iu7XfQ26azaoBQuunDwnlHYtZuntXWSIsMgFx3aBs7rbDBiNDAU3eC5GheSB1G6/6DuupgOOx/hUriNR47LV1odxigDoqBXDjSFgB71iOyehPkoHTN84hWObDXMAUTxkw47e2WTExB6qEDUJsdaK0qQED08aW1RHDNNdfwfT4WlOv9NXEDZ807enJlo3fQRurZVwNh7jrebpXt2ymNnDLvGtstfN+cGRl7aoM99vdgUlIIvjtUhZ1F9ZiaEgqpYg0pGrs7yva1bJbVoHOnXXUqHCxvgY9Og/su8PzKLNYYY3/ZQfx3ZwlunjPMLd1t+6ux+3Wg4N0A5eTk4JVXXsEnn3yC2traE86mOHfewsPDceWVV+LWW29FZmama185Iks1sechtbQQ2jyW5aHM4F1o7XbcoV2FHSb2e3QLlI51EpqWEooDecXYs3cX0hbNhxy1mLp6duKUknWZPuk8dK7VI1zVhKIju5GSOQVS9rT+Vhw0/wpvZE6EXKROvxj23fdjmK0IdZXFCI9JhlRZOtuRUfU1v26f8tsz3jZtWBr2aUdjgu0gin9+H2OW/81Ds5SOd95554xfK8FLL73ELzabh5aESTxLh5wm807CAZzBri5hFFjyTlYNKw53N6JIDvPlGet9mZkaxoN3rHHF789Nh1TtKxXq9g2L8Duhq64zeMfe12xpsNENS8G3FtTjk0Lhl+XWealeWS568bhY/P2bHByr7+DzmZ0e7tWgMRPip4xjpd4G9ZFZXFzMA3Iss+6tt97ChAkTeIex9957D6tXr8a3337Lrz/88MOYOHEi3n77bYwZM4b/DPtZQs4kYsrl2G4fiS9bR8qiPsJgqE1NfLQbpd9W3VVuCMzCHsPvMGLv3yFXLVVF+I/uafzT8K5b/viLkcHoizyfsfx61d7vIXXOmidRQcfPykpdaGQcNhnn4j9dS7BN4rUJD69/F4FoRwUiMO6cy894W3YSsiFpMb9uLFrvoRkSqVmxYgVffZKVleXWx5FiPmOnxYbvDlai1XQ8WENOF7xTzjPT0it4Z7YqaMNPE3+3Szggf6hCWAo7qrt+cV9mpglBnt3HGvlnglTtKxGOzSYkhJzw/UCjFkad2m1LZ1ftLcON7+2Bxa7COenhWDE/Dd7AGmM4G1d8tNN9jSuK6tr5Nu8qbjhtqaRGWjY7MCNGjODLYVngbtmyZfD3P/Pyrra2Nnz22Wf497//zX+us1NIryWkL6ljZuCab/+BimYT5uTX4byRUYp7orQW4Y+hyoeCd07DRs+A5rADwzv3w9TRBqOv/JaVtteXYoFmNypU8mh20F8d8XOA/F3wLdsIKeuy2XsK+cqtA9buSU/h+R/zcVGpGhdDunwPvM/HwsQrEKs7+xnbmEkXA4X/h2RTNro6mqD1pc9k4u2GFdLxt68OY+WuUpw7IhJvXSftrGr3B+/sisy8M/VaQqsUJ2fa9W5cIjWHyruDd30smXUaFu6HmCAjKptN2FncgHOGS7Mh297SphM66Pb+bI4ONKK4voMvnU0Mc93J22P17bj30wP8PTMxzI6XfjUeGi8uV2VLZ9/ffgxrD1ejptWEyAAh69AVmjus+POXh/D1/uM1788fGYmnrxh9ym2bFLxsdlCZdx9++CH27t2La6+99qyBO4bd5rrrruM/w36WkDNhH4ILMoWAHftwUCKdRUhDV/ueeHZHyRIzJqAK4TCqrDi6Q/oZWn0xNQvlB9o1pz+DKUcR4y7gY3rHfljM0u0o3FhThi91D+I/+mcQ5ievHYpzMoSd7U15dTxIKUVlR/chw5oNq0ODtEX9K0eQMXIMjiEGOthwbJc8Gna4Un8aVFDDCuU2rGCBO+bHIzXenooEGlZAMVp6ZWIqcYXNKTXvpPRL3QvLimLZdMy4+OAzHtfN696HWJ8tzeM61g16f3fwbkIfHV4j3VT37s3NRfxzYmZqKK5Ot8Pg5RqRI2MCMSExmDcg+XSX6xpXsBPfl7+yhQfu2HmqsfFB0KpVWJ9Tg799c+SU2zd0Z94ppcRQb4N6B7Bsu97+9Kc/IT8/v18/e/nlZ16mQgizcFQ0wtAMY/ZK2Lq6FPekGLqE4J3WT7qFXV2NNTIoCZ3Br3fkrIEcWVqE4F2HVlnZPazOXQMC4asyI3/PBkhVY2URxqiLMU5dCI3MCvmMTwhBhA8wzrwLhw/sghTt3r8flY5QHPKZgui4/tXtY2e4i4Om8estR3528wylhzWoOPnCSqqwg7r09HRccskl/MKus++x/7v66qu9PW1JcvYBkeZhPjlb8I4FBxSZeafA4J1dJjXvyho7eTYdC7KwgM6ZLMyM5uO67OrTLoUUs6L6dl6XmgXPMqJPbZrHMguZqmbXrS7Mq27Fx1nCCZBb5qbAiwl3J/jVVKHLLWtc4YrPLXOXDTe+uwsFte38efxyxSx89fvZeP/GaXybV+2tQG7z8Y13OBxo6g7ehcrsRHl/uOTo4qmnnsLOnTtdcVeEcFOTgvCD8T48Yn8RR/f8pLhnxdfWykdjQJi3pyIq2uHn8zG2bgvkyN4u1BOz6JUVvFNrNCgMmAK7Q4WKo3sgVW11wk5Wk9Z7RXzdhQWxng/6CO/pn0Tn9jchRS+XpWCm+Xkcm/uvAf2cfdg8/GCbgCxTgtvmJlWsQQWra+y8/OY3v0FBQQEvlXLkyBGsWrWKX9h11tyM/R8F7wZH1Z17J6UDX7EcbIoVy16RegBnMFo6uxQdvJNLt9mdRQ18HBMfxOuhncmM1DD46TU8M21Pd+04KdnbPecxcUHQ9XFyNjbYh48VTSaXPbdXv7kTli47z1qcLqIuvReNjUWAUcuDt9uL6od8f09+l8uzGlkW3Yc3TcPY7ixO9p65qjtQuL36+B+TdosN1u5UZVo26yYfffQREhJop5f0n06nQ0GgkO3QtPcLxT11fo42PvoEiufDWgxSp12ELocaifZyVBTnQm4c7cKOkM2ovNe9asqfMMH8Gl5qnwepsjaW87FdL816LmdjGLGIjwk1G+CQWH2mo9WtyK1uhVajwfzxIwb0sxGTL8eN1vvwQsNkRWXHDMZf/vIX/Pa3v+1zlcUVV1yBG2+8EX/+85+9Mje5kNI7UKuWVwayq/X+POkdyFNW5p20/pa4wsnBOqk2rNjRHbiZmnz2fVbWhG3RKCH77rPdrltu6elOs+P7WDLbO3jHAlpDVdHUiZvezeKBzoRQH/zfsrE9NU/FwEevwYVjYvj1L/YK+72DtbekEW9vLeLXn7lyHIZFnFiO7crJQvzoYIMKrSYh6O/sNMuyINlclGbQf1Wff/55XHzxxXjsscf41y0twjK/vthsNlRUHC8+SEh/qEZcyMfYqg2SOtM8VGxbr7T8DUvNj8AYLd2W6u4QFBKOPP1Ifr0062vIjcYk7Ag5fJRX63Di2LFohj8/+9Z7x15K7C3C3zmLrzyb7GTMugRmhw5xjmocO7IbUrJpRxY0sGFuegSCBlgjZUR0AHx0Gr7jWFArnFghfdu/fz8yMjJO+/SwpmXsNmLW0dGBpKQk3HvvvRATER279Zs3C6tLQe+AnVQDOEPtNqvEzLuTz31JsWEFO1b5+WjtCd1kz+YX3YEYVtes3Sytkkj7S5v7bFbhFN+Tedc55ID+vZ/u50t0Wd237++Y21NPT0yWdned/e5g1aB/h9l76K9fHeZ1XJdNjMe5I07dd2bPQXKYL6wOFbYVCsdIjQpeMjuk4F1wcDB27dqFv/71rzwavGLFCoSHh2PBggW8Bt7KlSuRl5eHrq4ubN26lf8fIQMxfPZSWBwaJDnKUHJU3Dv7rtRhsSHfHo29jnQEBZy+e5NSFaVfiz9af4vP2sZAbtQWYbm02k95n5fsrOWwCD+wY5ntBcLyYanRtlXx0eEvnJGUG7+AYOT4TuTXq3b+D1LBsgQX7b0NOwwr8JvEgS/x0GrUGBcfiGjU4+iRQ26Zo1ywBmWbN28+7f9v3LixX43OvOkf//gHpk0TMv9FSULH+RS86//ySakunRxyw4ou5QXvTl42K8XAbU5lK6pbzPzE1rR+Lulkt2OdZ9vMXbxemlRYbXbkVgn752Pjzpx5VzHEmnf/2VSIrQX1/Hl9bvl4+BnOvBzZW6YkhyIu2Aet5i6szxlcExLWyOhAWTN89Rrcv7jvFREsxjR9mPD+2n1MWLrc2N1pNliBnWaHFLxjRYkrKytRWFjII6cXXnghZs+ejdzcXF4D76qrrsKIESNgMBjw+uuvY/Hixa6dOZE9/8BQ5PpM4NfLd3wOpWjqPiOp06j4hzc5UfzM5fjENh/fFQt/UOXkCf8HMML0NhqHXwEluj4iD5/oH0HAT3+BFBlNwg6MJjgWcmVOFToDh5ath1SU5u1HvKMSAejE5MmDC8rcpPkW241/QMK+Z1w+Pzlhy2X/+9//4qGHHkJT0/G6Ruz6gw8+yE/sntz0TEzYSWdWn2/JkiUQGyk2rKDg3QC6zSooeNdutil62ezJwTopLpnekCt0kJ6ZGsaXxPaHWq3CLeek8uuvbyzkjQqkoLC2HRabHf4GLeJDhCDdyWKDhey4pg7roLMKtxbU4cnvhc6qf75o5ClLSMWEvZaXTYgd0tLZlzYIzU6vnpGEiADDaW83OUlYjbSrRFi63Nod/A80ijOw6W5DLkaRnJyMSy+9FLfddhu++OILlJSUoLa2Ft9//z3++c9/4qabbuLjK6+84poZE0XpdB4olqyDUrTXlOAu7ae41vCTqGociMXo2CCeKs3O3Dlb1MsFSwU3waDYjMvR0T6Yqs5FQr00G5K02zRocxhhDJVvjdfUWUJgeXjXUdRVFEMKKrO+5GOucRwCAge3JN0ndhQfg1vlV2vTlZ544glMnjwZjz/+OF9xERcXh/j4eH6d/d+UKVP4OBgsa4+Va4mNjeV/G9k+58lefvllpKSkwGg0YtKkSdi0adOAHoMtlWVzFyMpNqyg4N3psddRscE7i7IbVsih2+y3Byr5eH7mwMqEXDYhjncUrWk14/PdQ6uX5ik5lS09JTRY0KovAUZdTzBpMEtnWWOKP686xFef/GJSfE9HVzFzLp39KbcW9W3mAf1sdkULb1zCOhXfODvljLednCRkO2ZXtKLD0oVOi/CZwTL2lMgllWRZJ7ELLhCCLExYWBgWLlzIl8++9tprfPTx6TtSTciZpMz6BR9TrUdRWyMsSZM7a20+7tCuwtX41ttTESX2h/OSZDuu13yH+i3vQE6cqeBKreOQOnUxb0iS4KhAVUkepOa31j9itPkt+GTMh1yFxyQiVyvUNMvbJo26kwGlP/CxPencQd9HRPpkPsZ2lcJh6XDZ3OQmKCgIW7Zswauvvsr3CwMDAxEQEMCvs1UYbEkt+95gtLe3Y9y4cXjxxRf7/H+W1XfnnXfyrL+9e/dizpw5fNUHO6nsxAJ6o0ePPuXC6jJ/+eWXGD58OL+ImZQO89V0AvK0To7XnLyUUs46Tsi8U17w7pSGFRIL3hXWtiG7soUHXi7obkLRX3qtGr+dM4xff/XnAnRJYAUN21YmM/bMf7t6mlYMMHjHMvWueWsHCuvaEe5vwF8uzpRE8kZaZADvvssyR789KARz++ujncf4yJqYRAacuaZfbJAR/loHf5zC2nZ0dn9mKLFZBaPMfEMiGRGxyXg88M/4tCYe9xSZ8etIyJ65Teg42qkO8PZUROuioCJM1r2PgmJ2tuYeyAFr7POk5XE0av0RqpsOJQoMDsMRXQZGdOWgdNdqRCfeAalgO1+s9gcTFSTvk1WHRv8Rt++oRWzzRMyAuLU1NyDddJilLSFu6qWDvp/k5FQ0OAIQqmpFdcE+RI2c6dJ5yolWq8XNN9/ML67EAnFnKsHyzDPP8G62bMUH89xzz2HNmjV85Yczm2737tM3Wtm+fTs+/vhjfPrpp2hra4PVauWBxocffrjP25vNZn5xcjZuYz/HLq5mswmfLyzG4477dwdNrxQBs9ly2qyV/nBus1S2vT+ZNr2xIMaZtk1O2+9c9sZYbQ6YzJazZmnKafst1hOXVZqtXWfdLjFt/5d7hW6xM1ND4a9XDXhOyyZE44Uf81DS0MHv65JxZ68T7M3t39/daXZ4pN8ZHz8qwIAjVa2obOwY0Dzf3FSI7YUN8NNr8MTSTPhoTt1OMb3+vV08NhoHy5v50tmrJguZeP3ZX17VvdR2+eTYs24T658QYgDauoCy+ja0mbq7zWoG/t4Ts/5uy6CCd+vXr8f5558PT/8sUabACUvRsCYX67Kr8etpSZA7W7sQvDPrKHh3OqkzLoV99/1ItRWhprwIIZHxkLrWxlos0AgHltYA8da5cLem6JlAWQ7URT8BkE7wrrrFxEe288WWT8jZmBmLkLt9I4oKG/hOmFgLKjN5277CBJUNJao4JKaNHvT96HUalOhSENp1AHVF+yl4JzIWi4UH5u6///4Tvs9WgbCmaf3BAnzOIN8777yDQ4cOnTZw57z9I488csr3165dC19fX7haRTv7VwuzxYzVq1dDCiwmlhkhBGW+/PY7GFyQKLFunTzKqAjJZ8c/O+sbGvv1usph+2ubjr8vmK8G8N6Qw/bnNrFtP77Bh3Lzsdp8VDLbv3Kf8PrF22sG/Vk0M0yFbzs0ePKbA0DpXmj7uRbQ09vPfk93FQvb23nsAFbXHDjtbU1NbCPU2LL7APyq+9doka0AfX2vcP9LEy1oz8/CaqEUnGhf/94M/PyVFntLGvHZV6vh24/dwa3VKrSbNYgwOtCQswOrhTJ/ZxRiUKO0XYX123ajjce4NKipLMfq1aWQC9bpvj8GtcfNznzOnDmTL0+46KKLoNPpzhpJ/Oabb/hZ0G3btvGdLEL6a9GoKDy1Jhdb8+t5nTNWMFTObB3CGR6rTpl1z/ojJCIGR3XpvO5W8fYvEXLJCkhdS0MVWFWHFvgiUH/6wq1yFzh6IVD2JlJad8Fus0GtkUZafHv+FnyrfwAFhpEAjpeRkKP0SH8khfniWH0HNh6txeIx4u2u23VkDR/LI2ZjqBVkWv2SgeYDsFT37yCLeE5dXR3PXo6KOrH+Evu6qso9JTceeOAB3H333Sdk3iUkJPCA4WCXBp9JdnkjcCALep0eS5ZIY2n+/+VsRKNFOLExd/55CPMf/N82dizBDlwXLFhw1uMOKWA1v7Dz556vA4OCsGTJdEVs/98PspNzx48F5557PsLOUi5ETtsfkFcH5Ozp+To8JgFLlgh1VcW+/XnVbajatpU31btn+fkI9BncXOaYrNj+3BbUtFtQFTQSN89JEeX2/5BTA9vOfUgI8cG1l88+43LWI+vysKO2CKGxyViyhO0Lnt2j3x5Bm7UEccFGPPib2dD1TlcW4evfl/dLtyC/th1+wyZi8eizL6P+8M0sVigI158zHBeepd6dc9s/LxLKn4TGpyKYrTIvK8aItBQsuUAo4yIHzuz9sxlUFGTfvn245557eMewkJAQnHfeeZg2bRpSU1MRGhrKi7A2NjYiPz8fO3fuxI8//si/Zjs07GcJGYjUCH/8NigL53V+jyM/1mLy4mvk/QR2CsE7mz7I2zMRtfrYeUDJUWgL2Qe69IN3HY1C564WVSCUHLZNmzAPHd8ZEKpqQcHhHUgdK43liebaQkxWHwNUQmFdOWM7r79JakJEy+vw/zEKGCPO2pNsX+SFtnmY0qXH7PFC/dSh6ApJBZoBbVOBS+ZHXO/kAyv2HhhM7aDrrrvurLcxGAz88tJLL/ELCx4y7MDKHQdXOq1wn+y4RWwHb6fDlkT2XHeoXTJvdz2/ntZhFYKaTqz0V3+2Sw7b39FdcN7Jhv6/N+Sw/Sr1iSclWckNqWz/99nCvuo5wyMQFjj4DONQnQ5/WjwCf/zsAJ5dn49pw8IxOTlUdNu/pVA4JpuXEQm9/swBZmfJlPoOa7/myJo2vL9dqMn6j6Vj4Gs0iP7178s5GZHIry3CloJGXDLhzA3balvNyOpuNnjphIR+b0uwXvhbUt1qQVB3wNjPIL7nYij6uy2DCt6NGjWKd5Nl9UFYLRHWsOKzzz7rc6eJnX28/PLLceutt/IuY4QMFHtfnR9SjWnmHGTlfAXIPHinMjXx0W4cXFdEpQgbfyFQ8jrS27JgtQysy5EYdTYLO0TtGmUHbfUGI3b7z+JnoNpKG5E6FpJgbargY6dRAYU5AcxO8sPI7K1obfSB2dQBg9H1ywSH6nBFCza2xSNLdxVunjr4ZhU9kmbi1fw8tKonYowrJkhchnWz1Wg0p2TZ1dTUnJKN52orVqzgF/aZxRp2uI3465efwtKrGP3JARula+o8sb6RXSENK1hzhvaT3gvO7pFKbVjR0nliDTyxYsf133R3mb1obOyQ7491Vf35aC3vXHvnyn34/s65oltdtb2wno+z0sLPetvIQKHxQk1L/45JXtogrI+9aGwMDw5K1ey0cLy5uQg7i4WyT2ey5nAVr9s6LiEYcd0NPvqD1bxjKptN0HdnJxqpYcXATZ8+nXfuYgWCWTfZw4cPo7a2lgdbIiIieAevCRMmQK12SVNbomBBEy4FvvsIw1u28kCNTsbLCrWW7rRZH/ln8AxF2rg5aPgqkGdoHdizAVJnba3jo0lHr3v2jGfw2DfZmFMVjmWQBnWrELyz+ol3CakrZUw+DzXfhSISDTiw9WuMPXc5xGbDkZqenW6DdujLr0PTpuKJNVZEtBlwnwvmR1yHZUSw/VG2rGjp0qU932dfX3rp4BuV9MfJmXfuJqUYj9naO3gnjQCFpzR3d5c/XUBHrpydIplAoxYtpi7FdZw9ubNw80mBXLFiJ8RYR1SDVo3zM4d+UoTFC55cNhb7S5tQ1tiJv3+TjSeWieeMbX2bGXk1bfz61JSzZwVGBAjHprVtZw/eHa5oxupDQiD09+emQcomJgrJJkV17ahrM/OOuafzU66wX7ZwgO8fZ+ZdZXMnIrufZ1+dNMrquJraFV3Fzj33XPz00098mcF9992He++9F9deey3fkaLAHXGF9EnnoxGBCEI7cncKNYzk6v2AG7HU/Agakk7fVY+A10IrDJwGs0OLsoKDkn9KbO3C2T2LnjIu56QLZzizihsks1Ov76jmoyowRjm/fxFCNpv5wP8gRqF7XsAC9S6cn+6abKiUCL+eZR+9uyUSz2AdYFnpFWf5laKiIn69pERYdsTqz73xxht46623kJOTg7vuuov/3y233OLWebGsu+zsbGRlsTo+7iPBxDuYu2yKza7qb+adT/cBqFKCd+3dQVy2WCvYV3/K+0QJWAYb4+yw2yKRvyf/3Sl81i7IjHJZhhy7n3/9Yhx/P3ycVYofjwj7UmLA9kGZjKgAhJ6lJiPjDCqxzDvna3w6T3x3hJ+IuXhcLEZES7tYTpCvDsOjhEZ7e7qXxJ6uw/a2gvqeZdcDEdwdD6xqNvVkcfsoNPNO7YqoeUxMDM+8I8RdNFot8oNn8+tt+7+S9RNdZA3BXkc69KFnrhtAgPoZD2GC+XU83zhL8k+Ho1NYLm2j5dK8IQLbCYroqsSh7GxIga+5lo+G0DgoRcDEK/iY0bQJFvOJ9Zu8rbG2Ale1vYf/6J/B/CTX7OAFGnUY4deGKaojKCspcsl9kv7btWsXX83BLs5gHbvu7Ai7fPly3hjt0Ucfxfjx47Fx40beCTEpSR5d6p2laRy86p34ddns6B2PomWz6DPbKipQOCqVyomqoeoQ2uzCT6/tCVx2Wo5naCqBczV5SHfwUgqZd+yE1Rd7y/n1X00bavunE00fFoYbZgmNC/70+UGeXSUGO4qE4N2UlP6dVHdm3rHs0pOXhvdW2tCBTXl1PGD5x0XyaLgwKUl4jnafIXjH/o89L+H+emTGDCxgGdhdDo7VUa1uEfY3jQrNvOtX2JxFjzdt2sSvz50795T/Z40rWA283//+966fISHd9KMuArasRnLtBjjsdqhkuhy7pfuPuLMgJzm9KWNHofOrChypbkOTxGMm/wu9CTcUzcfdaak4fb85ZWAHqf/y/y/mWj/Dth3XAuOeh9gFdQnLnn3DlBN0HzFlAerWBCMcTcLS2flDbwrhKgXbvsBklQP5mmFIixvmsvv9h/o1TDLsxsFcPTB8OJTuhhtuGNTv95tvvjngn5s3b95Zsxluu+02fvEkTy2bVUls2ay568SATIdCglP95QzYxIX4oLi+A23mLkVl3vnqNQj2FfZzGzqOd55V0rLZEF8dX2bIXntWC1DdnYknRu9tO8YDL2mR/pgxLMzl93/fogxe/y6/pg0Xv7AZH/12OoZHBUAMmXdTU/q3vb56Lc8kZK9nTYsJ/hFCNtrJvj0oLJednhKGhFDx1QsejAkJIfjvzlIcKGvuV/3Agb7XtWrwDsdWmwMN7cLnhTP4rzT9Ct5ZLBa+08R2uPraOWFLEi677DI8+eST+NOf/uSOeRKCjFmXoHPznYhW1SH/0HakSaQL5UBd3r4STRo1QtTj2MIvb09H1Fga+/iEYOwtaUJuo7QPDBrbLTDBgIAgWjbL+CRNBBo/Q3jNVoid3WZHk90HfiofBEfLI8un3xnR4fMRXrcK5v2rABEF71R5a/lYE30OXFlNpsM3DmjeDWv9MRfeq3S98847HgveiZWnGlY4e8JJJHZ3SvCuk2renaC5O2AV292hkh3wD7Y7spS0OzPvDNqeAv+sFIGSsEBd78w7FstrNXXx5YdixN6b/9lUyK//fn6aW96jLIvq7eum4Lfv7cKRqlZc8+ZOfPn7WYjqfo94Y5tZN1hmSnL/98ujg4w8AMlq+A3rI3jHlsd/truMX79onHzKrGTGBvbU8jvd59ieEiErb0o/ugr3hQX8mzu7UN9m6QmWKpG6v61rWQ0RdulLZmYmSktL8eCDD2LOnDm85khBQYGr50oUzugbgIMBs7DeNgHbC4QlanLDMgpvtX+Mv+g+QJBW/Gn0YvCbyBJ8q38AF9a8DClznnl27swpXfLUC/mYas1Hc714aqD0pa7DggssT2Kc5U2ERSdDSfwnLEOOPRE/NkfC2quzpDd1WS1Ib93Jr4eMu9C19x0Qz0dNS6lL71eq7Hb7gC+eauwgNz3BO4lE71h9o76CNuTUzDuGxXNMvRp8KCHzLqK7sL3SgnfO+oYGnbone0jMde/e3VqMpg4rhoX78Rpt7sKy0D6+eTovnVLVYsKtH+z2Wj1EVruNvUzxIT6I6Q6w9wd7jpjCWqHRxclW7S3nwT22usoVHXvFgmVJssw41oCGBS77es+zRIveS2wHyvm7Yune1/TRy3MF3tn0a6tZ0wnWjII1oegLa1Ixe/ZshIWFYcuWLbj55psxfPhwhISEYP78+bjnnnvw0UcfuXruRIFK5r2Am6z34cMSeWaktbU2QasSPpQCggdWzFOpxgyLwyj1MYzrOii6ulsDcUPDs3hK+yqiHUInJqWLiE1GsToBapUDBTtXQ8yqm4UDD9ZhS+uCrqZSMmLaEvxG9wxe6VzQU4jY2/L3/IhAtKMJ/kifMM+l960OEWr9+HQI3YUJYUtm2UnsKVOmeOTJkErNu5MPunt3GSXHG1bEBB3PLFLC0tneNe+cNcJqWqW77zaUZbOsYUWgj1bUde/Ye/KN7qy7P5yX1tNkw11YE5P/XDOZdyLeU9KEh784fNZyCe6wq3vJ7ECzxJzZdqwrb18+2iFk7d86L1VW5ZH0WnXPMmeWfXey3KpW/l5iy4oHuxz65Ew7o0KXzbokZMmWy65ZswY1NTU8A+/LL7/EX//6V77UlnUDe/bZZ3H11Ve74qGIwp03Mgrs70ZOZQsv+Ck3bU1CRqHZoYPRVzh7Q84sbdws1CMI/ioTCvdukOzTdU7XVvxCuxEhOvnvvPdXVfgMPnbl/QAxY2eIGW8t7/AmFqxcNDqaX//ukFDHxdua9n/Lx/yA6dDqXLtz7BshFNUOtohjW8UsPz+fn9Btbj59DRw58Fy3WWktpzx52awSAlMD4QzWsGCFs3NnuwKeI2fmnZ9B0xO8U+qyWY1K1RPAEWvw7r1txWjssCKFZd15KFMsOdwPz181gWcbr9xViie+P+LxAN7OQQfvnJl37X2e0DhULizFXdy93yQno3qWzgrb2NvBciHrbmx80KADwOwzozel1rxzeb5hXFwcLr74Yt79a9WqVSguLkZ9fT3WrVvn6ociChTip+cfpPGqWuze8TPkpqNZyFxpVfnJvu6Jq6g1GhQGTuXX2w59DymyWsw8U4gJCI3y9nREw2fkIj4mNmzjS8rFyjfnE6zWP4AbbZ9CiZaMjoEvTLAdWsWXrHqboyZXGIcvcPl9B8cKzS9C7fWATf4H2oPxzTffIDU1FRkZGbzJ2e7du/n32QnetLQ0fPbZZ96eojT1dKyAJJfNtpno96XP4J2PruegVAkBzo7ubfQ1aBUbvHOWmNBp1LyLee9mdWLCgsn/2Xi81p1W47llivMyIvHYpaP59dd+LsTvPtyLepPnPrv2lTYNuN4dkxpx+mWzLHDHlnyG+emRKJNGFb05M+oK+tj2nMpWPg60y+yZgnW+VPPOfdjy2XPPPdeNj0CUZEXIDmw23IH0vY9DbjpbheBdu9q7HZakxpEmHKRH126GFDU3CEtl7Q4VAkNoubRTxrQLYHLoEI06HMvdA7FSNxYiU30MMRphZ09ppqeE4Afjffg/+zPI2faNV+dS2dyJq9ruwHzL00idfaXL7z8qLgkWhwZa2NFRLxSdJsf99NNPWLp0KUJDQ/kKjN7ZEpGRkTyo9/HHH8vqKfPUslmJxe5OWTarhKyygWjuEII1rEkBa96glOAd61jK+PWqecc6riqJcwm5j17Tk3lX3Z3BLybvbz/Gs+6Sw3xx6XjP12f7zfQkPHH5GGjVKmzIrcP/HdDgp6Pur3nOln2y+pOsG3LqaTrGns6wcOH2Fc0mdJzUpIfV0WMmJIbIMkHD+Vz1lXV4pErIxsuIHvzxLauT2Rtl3hEiEWmTF/Ixw3xQ9IXsB8raJqRpd2ooeDcQKdMuhM2hQor9GKpK8yE1bY3C+7hF5QetjhpWOBl9/fFZ6G9xs+UubKjqf8FgT9O0VfHRHiCfzmEDXTpbEjaHXzftWenVuWw4IuzYh8SPRGiI62ujBvoY8AJ+ib9Yr0OtRT71alzl0Ucfxbhx47Bjxw6+nPRkM2bMwJ494g3Ei3rZbE/DCmmE78wnNV9oVUBgqr+6bHY0djepCvVT1rJZZ4CSZc04M+/q2y38OVGKTou9p2bXpO7Mrk92lYnqd5u9F193Zt2dm+7RrLvefjk1Ed/dMQcTEoJgsqlw64f78EOOe4/9srqXzE5OCoV6gEs82QoxFphmalrMfXZbnZgUDDlyLhkuqmvvacrCsPc16yDMjBxC5p3fyTXv9NSwghBJiB2WiSJ1Em/scHTz55Bj8M6speDdQASHRiFHlcavl+z4ElLT3iDsiLSqBv9HTa4sk27GWvsUrC8Q/vCLkdEkvH6aoDgoVeDUX/Mxs/EndLZ777XakCMEUudnRLrtMb4JuBLv2xai2iregLK37Nq1C7/+9a95o7O+xMfHo6pKeI3I4Ijn8P7MzCcFY2jZ7HG1bWbeyZJlFIX7GXqCd0rIvGtsPx60ZBcWG2ExKxbAUwpTd1Yqyxy6akoijDo1sitbeoI7YvDB9mNoaLcgKcwXl3kh66639KgAfHjjFEwMs6PL7sDt/93La5+7S1Zx46CWzDqxOpaMM0DvDGD1BO8SB3e/Yhcf4gu9Rs3rnVY0He84W91i5t2K2e96WuTAMhl7Y5mqTuy+2GMpkTK3mkhedaywDFtz9DvISXbQXCw1P4K10Td7eyqSk+0/Hatss7CxXnpntEzNwsFsq1aeXZSHYl6GsIw4q6hRtFkJgRYh28snLB5KlTH5fFSoIuGnMiH7J+8sizSb2vHPol/gVd2zOC9FyOhwB+dSL6V1SOwPm80Gg+H0z31dXR30esouVkTDipMy75QQmOqvqmbhsyMywMAze5zLZtu7O7HKWV2bENAI89fzwvWRAUKjp94H+3LX2b102Eev5plarBkfsyVfHB3b2XJPZ9bdCg/XujsdVh/wN2l2zBgWypde3/TuLrfUSmTNRJydZicPsFmFE1tu27ujtHMZLQtisfc8a9ogR2zbksN9T6l757yeHOY3pA6xvZfN+uq1slx63B/e/20kZBDCJ13OxxFtO2Dq7LsdtxTVdPlhryMd7SEjvT0VyamJWYC7rCvwdnncKYWyxc7aXevQpKfg3clYh7Pzgivwe9XHyNkuzoYkvHkBC+JFJECp1Bo1jsVeyK9rDnmnIcHR7d8jQtWEiZoCjEx2XyB1mE87pqiOwFp52G2PIVUjR47Epk2bTvv/X3/9NV9WS4aybFZaNe/0WuFQg4J3xznrm0UFCYGr45l34mta4Gr17ULAJczP0PM3/nR1suTK1F3zzqgVghFTu4NEu7prookh645lQrKmCksniGdFAYshPr98HH/PlDd14qZ3s06pKzdUhXVtvM6fQavGmLjBBdlCujPvnHUte9e7GxkTIOtGC86af2zprFNZYwcf44fYpMO5HJkZShBQ6ih4RyQpddxs1CAUviozjmz9GnLrPuYsYEv6L86P7Qzq+Rm5XceEs2ZSsTXkUowwvY3VKfd7eyqiw86s/dZ/K27XfgHbAfF1c21tbujpFBwWlwoli51zDR9HdWShoabc44/fcUhollEUMguq0yzbdIVFptX41PAoUgo+cNtjSNWNN97Iu8m+8cYbPAvP+Tvc2trKa8Nt374dN98sr8xyaljRN+dJNPZ3mWmlbrM9Krsz76IDheDd8W6z8s+8q+/OvIsI0J9QJ6uvDpVKaFjBTO5enskCPL1rhXllbhbb8Vp389N4xpuYsKy2N6+djBBfHfaXNeP8p3/GhzuOuaxe4MHyZj6ywJ3zxMNAsSY0Jy+bdXavleuSWafYYJ8TsouZ8kYhqzY+ZGilRnovm/VRaL07RrlbTiSNHZgVhc/j182Hv4VcDKtZhxs1q5FkK/H2VCSH1T+YmxaKEaoSFGWJM0PrdOraLTDBAL8g6jTbF+PIRXxMaNgKh11cWZW1tTU4ao9DJcLgHyjvnbKzSRoxEXmaNOhUNhzc4tnPZfa+SKrbyK8bRl/k1sdS+QtLnHSdQpdoctytt96K5cuX8wBdWloaD9xdeeWVCAkJwSuvvILrrruO18STE881rBBS78RU1P5MTN3BO1bXjBFr2QNvqOrOvIvuybzTKeY5qm87MfPO2aFSScG7nsy77uyhEdGBPPuSZafuL/Nu1/r3thXzpc0JoT5YOlE8WXe9DYvwxxvXTubBNbYc9aFVh/DWlmKX3PeRyqE3VmCBRYbVeXPKrhBq9I0eZDafVMR0f6Y5P+OYsu7gXVx3YM8Vy2aju098KBEF74hk6afegFstd+C+5l94/UyVq0xp/AZ/0X2ABFOut6ciSb8M2I/vDfdj1tEnIcWd2XB/qgXVl+HTFsPi0CLWUYPS/AMQk2O2UCy0PIUbQt719lREIXviXzHb/G88VznKo49bdHgHolGHDocBI2a4N3inDRK6CvuYxVGfSGw++OADnn23YMECjBgxAhEREbjooovw6aef4s033/T29IiHNHdnnTgP2Fi2kZI6ip5J9UmZd/7dmXdyD96xrC62OsJZ845J7S5gX6CgZbOd3fUgWcMKZ62wBZlRPUtWvdlM5MUN+fz6H85NF13WXW+TkkKx+vY5uHCs8Pf4sW+y8frGgiHfb053V9QRMYNvHBjsI7y3m7o/A9kJl5wqIXiXOYSgoBQ4SwE4s4uZsibXZN71Xm4cN8RAoJSJ97eSkLMYPWkWNutnoaRdw1On5cDYJfzR0PlT7bPBSJ2yCDaHCsn2ElSV5EEqFle+gqe0ryLZ5pozh3Lj6x+EXONYfr0i6yuIibPIdlywcs8C9jZj7kJUIAJ7S5qQX+O5rrP1e4T3Ra7fZBh9B9/NrD+MoULnvYAuCt71ZjKZ8N5772HHjh24/PLL8fnnn+Pw4cM8K+2LL77AsmXL3Pq6KIVEEu96uoeybpVOSmjIMKBls90Hus6GFXKvC+isd8cyppx1/lK7l80eq2+HVSHBXVN3ALN33a5rZybz8Zv9lSfUSvOkF37M58vbR0QHYNlE8TfgYp1LX7xqAn53zjD+9T9XH8HTa4eW/HCku4vtUDLvTm5YwX7fWRYeC9IOpduqpDLv+lw26+uyzLu4IQYCpYyCd0Sy2Bmh+RmR/Pr6HHksX/K1CX80DAEUvBuMoNBI5OmFZh/HdnwJqZjYuRW/0P5/e/cB3lZ5tgH4kWR5723HjuPs7ey9gRCy2IQVEiBQSoBSSinQ9me0hQKFQiHMUvYsJMw0C8iALGcPZy878Y73tiz91/cdyZH30j7PfV2OZNmRzpDko/e8YyMidOqZttZR5d2VMnn/jPVwxeCdpc+H2onJgTP6KxkEX2xxXAA9OusHeVnb5zK7P1ZQhFJKFGYsdJ9IigOIKbNLlizB7t27nb0oHsndButZepvFBPvK5u9CqQoGMrSHpaRMbBtVBe/Mz4nIAO/6MvD4ED8ZyKutM+FYTpnKet5d+Bg+LDEUPSMDUFNnRKp52qkjnc4vxwdblRPIf5wzQAaa3IF4Hj1y2QA8fFn/+gDkMnP2YGeqYHLNE2z7xXQh8848sMJSNnvIHBDsHRXo8YMWLNnE4j1OZByKbGvL+13XM++sgnehXQsEujMG78itzenjg/t0yzFp9+/gCYJNSqaKf6gSlKSOK4qfIi+9Tykf5t1BqEnpcRIQrqT/U1OxI+fJy36V+1BZ7riMrraMP/w0Vno/gok1m529KC7jlqEBeEv/DyzZfQWqq5QpY/ZUWGXEl1VjsNPYB70nKpPI7SksSnmdeqEOxkrPyPq21Yeo7t27o6RE+aCiFhxY0bwCc+ad6HkXqJLgVHuI7DJLJoolKzHIsn08fKhH/aTZQKXfnaDVajC8e6i8vjPdNaatOrrnncWYZOXEfaoThq49u/qwDKBO7RuFyX3cr//yXVN74dHZSgDvudVH8On2jvcOP2wumRWvS0tAvTNCzUMHLWWzx3OVoHS/2M4HBN1FdLBP/cAiMbU3p7RatrbS6zSIsnrddwYz7xQM3pFbm9AnFvd4rcDUui2oKsqCOzPU1tRPrQwKY/CusyJHmIM85TsdEjjoKrGMwVCWMzRKKcejppL6DkMWolABHxzYt8NlNlF4+QkM1J5BhL97nKV2hAmDe2Oo7gwiUYz9P3xk98c7UKTDa3Xz8XTcvxAebf8G2yHBQbK3nlBWnGf3x3MnixYtkj3vamouTNnzdBxY0XrZrAjUBPqqIzjVHiJwZzCaZDZiTJCSpRLkq1dFcFMMQrDud2dhmcAppq2qKvOuUfBuVA9z8O6UY4N3O88UYuX+bDn47RFzAMwd3TmlF+6b0Vte/7+vD2LryY61trBkyImy4a4IC2hYNmvJ5rOUlHoyHy9d/YRxUTpbYHnNB/jIQL3tMu/8oFYM3pFbCwoJx2G/4fK6T+4uuLPiwgsfAoPD3O+sl6voOXg88hEKf001jqWuhasryleCzrUmHYJCIp29OC49YfqDAa9iZPXr+C7XdV4fobU58jIwSulXQ4CXXo+TiVfKTeG770O7b5IDBcoBoaXhtyMOTv9lWoA/1y5GiVG9pRvNmTBhAnQ6HYYNG4ZXXnkFq1atwsaNG5t8UcdZPvaY3G6qKDPvLPZkFOHaN7bUZ/dYPswGmYObJeYP+54+qKNxBs7IJCV4t8MJGWfOzLzzswpGCGPMwbv954rl8AhHEKWNT608JK9fOzJRTr51Z7+9pC9mDoyR5ce3vL0dOzpQgmzJvOtKvzshxDywwrIP8+uH0nUt88xdWHp5ZpdUotCcfWjpA9gV4qSHRVdLcN0Zg3fk9ip7Xiove5e7d/CurFDp21eCAHjpOXW0s7Q6HU6FjpPXSw+uhqsrPa8E7wo1IdC68GQvV5AyeCiM0GLDUdfIdqozGBBlVM7shsUrDZNJ0ePiX8Fo0mBw9R6cPX7AbpultDAfQ8o2IRAVuNhBwTvhG78r8EHdTBSYlGbrpBATZvfu3YvDhw/jvvvuw5w5czB9+vT6r2nTpslL6nzPO3dosygCAiybbeq2d1ORZ87CSYq48N4RbC6zE8MCPFlmsXnAU6MP3qJs1kurQUZBpRxc4enE1F3B16th8C4x3A+DuwXL8tVPUzMcsizLd52TmXciC/CBmX3hCe0b/nXDcFzUP1oG8H7z6Z52DwA5bJ4I29UApiVQVVJlkCWjltd8VJA6gneWsviC8tr64F2YuQ9gVwyIDZLZi6K83NN7B7am8wXdRC6i58RrgbS/YaDpOHKyMxCX6J4fovN0MfhN9ZPoEaLBi85eGDdXOeIO3LxqOArKR2ElXFtFQba8LNWFgsXSrZvYO1L2zTh9vhync4rQI0bpk+Ms53MyEK2pk1mTkbFJTl0WVxOX1Bd7/UYhpSoVZ394HQm9X7HL4xzd+Cme07+BO3x+QK+oa+EoIf7eyDRPkKML3nnnHW4OQkmloT5LQvS8s5SFFnt4ZllbLAFNoXv4haxdS+ZdabXyYd9dhgV01Flzr7/GJW/i+TG6Rzi2nDwvB9DdPikZnkoEtqsMxmYz70TgadH4Hvj9F/vwwZbTuGNyMrzseFJX9GT7mznr7r6L+tQPUHF3IrDz0g3DMedfm3DmfAUeXbEfy24a0er/EYMVjpoHpgyI61rZrKXHp6VEWm2Zd8FWmcSWtzLxd8AW+3XD76fLQL+aMXhHbi+qWzKOevVBX8MxnN6yHHGJD8IdFVRrscfUG6Yg5wYkPEHKqKnY/L9qGPOqcbawosvjye2pqiRfXpbrlbIRav2A6M8R63FR8RfI+HEJetzwqFM3V0HmCRlwzdNGIN6Lf04bM45YDGxORf+sFaiqeBq+/rZv1ux/7Bt5mZ94MRyZM9BLX4hAzTHU5EcBfV2njNsVet6RfWjccDCBeM8WH7gsGSe5JcrtalRa1TBwad3vzBK8s/S9CzFn4nkay3T25vpVXTQgWgbvfjiU49HBO5FVJwK0QnPZQ/NS4vH0/w7Lk0Nr03Jw2RD7DTL7+/8Oy4By35hALJnsWdtcvPe8dP1wXPPaZny/Pwu/ySlF31YmyJ7KL5dDFkRftcQufmYQ/SxFfEns5opqQ33mXWSQOqqqLCdrRCax0ZwqbouyWcHbPLlczbgFyCPkd7tIXvqdXAN3ZcngCLfRG5yahfjr63uorD/iGiWWLdkTdgn6Vb2Lz7o/7uxFcQt9onzRTXMe/md+dPaioDz3tLws0juuXNOdDJ1xPTI1MQhFGfZ+/6bN7z8vOx2DqnbL690m3ghHWlD1Kf7r8yQiTyx36OOSilnqZs3ZO67MumRWiLYE78wfYtXomHnipKU88sax3Rv00RQf+JsL8nkK8Zw9Zw7exTcTvJs5MFZeiiEDuSVKbzxPHlbR3MAKS0DvxjHKc+Pdzcoxhj2IXnCW0ty/XTkEeg9s2zIsMVRmdAqiNLg1h8z97sRE2K4OVhAZlAHe5uyzKlE6qrymuzpt1e0y7+S6N/xbQF3nea9UUqWYUVehxOSH4xW+KHPTAx/vzK24Xfc9RuCwsxfFI8zvXo0/eX2A2M2PwZWdL6tBNbzhF8xhFe0RO2KOvOxbuQeV5crBlrOcrzDgqLEbivw5rKI5Oi8vHBl4nxzs8Jf0QTYPOJxY/zF0GhPS0AvxyQPhSHU+5gzpyiKHPi6pl/XHSReP3dWXidUH74KVD615pZ4blGmLZYLo5D6R2PTQjCYBLEu2iig59kQigFFVq5SLxoU2Lc/sHuEvT7qKbKWv92TC04dViPiQaAPSnJvHJcnS6W2nCpCWqfRhs6XaOiP+uELpRbtgVGJ9gMsTiX6Kwu701oN3h82TZrs6rMLCUhKdXlAhL8X+tEXfN3dwoYfnhcBlqErW3RFY50MeIbHvMEzRvI7saj38j+Vjth3TzO0lJnsDrtB/hK3ywO0WZy+O2xvfzRu9vf6HimIfVFdVwMfXNUtn8y1j1FVyRq6regwYjWxEIlaTj73bvkfKjOudtiw/e0/CnTWJWDqgFyY4bSlc26h5v8K9B35EWZ5BDhqZ1s92nR1Dj38lL48EjUMfOJbRV8ns1Va3/oHA082YMaPL9yGyFH744Qe4q2XLlsmvuroLGTV2Trxz+YmzYvCA9UTA6CBfVWfebT6RL0shhZZK90S2igh6emrm3TlzvztRQi0yDZtz1YhuMkPqk9R0WTrb1Qwol540q9fJ976WpnVeNjgW3+3LwnubT+OZa4badBn+8/MpHMkpRZi/Hg9f1h+ebHj3sPpJz+2aNBtrm/YeAaLvXWm17Llnmbrtic/n5lyYnm1AeY1yMkI818g2mHlHHkGj1WJgmHIwIHpEuCNdlXJW1uTH3me20GvIeOQjFP6aahzdvhauavK5N/EP/evobTji7EVxm9f6mcgp8nrVwZUu0b+nuRIgupBNct2oRHn9tZ+O2WyznExLRX/DIRhMWtTFjXX45tYEKJkKXtXqzrwzGo0yo7IrX+I+3NnSpUuRlpaG1NRUuz6OBu5TNnumoLzBUIZoFfe8E/vqKfNQgF5RAbhlfPPDjYL8Lkyo9ETniira/Hs5PyVefvA/mVeOtYfc81i+vWWzjYdVNHbLeCWj/7t9mfXTaW1B9IF+cZ3yt/jR2QMQ5uHljKJ01lK2ft6cEdxa5l1/W2XemUuiLcE7tQyrEIItPe+qa+tbKHj688yRmHlHHmNwuBE/Zmlw5lAqamsGQO/tXm+UevOHQG1AhLMXxWOCPCdDJyCyaCXKRZBnyuVwRYPKtqKP7jj2am9y9qK4Dd9Bs4ENy5F8fhNMTvzgb8kuaa75Nl1wx5RkFGz7BPdkfoGDm5/HoAmzu7x5DmxZjSSTBvsCJkAf4PghP17+SvDOp7YYarZ+/XpnLwK5oHTze2NShBK8s0yxzCur9uhpqs3ZeCwfB86VyA/z/71rQou9nyx9ojw18+5UvhLE6GF+TrR0skcEN5f9dALPrjqMqX2jmh3q4ErE/np+zVEZdHzgkr4tZtNZWAJxba3X6B5hsjeiOM5Yk5aN2YOibRJIfuzrgzKAOCY5HNeMTICnE5meg7sFy9fgyv1ZWGgOijaeuisGhFh63tlCgI+uwYTliED1BK/qp2dXGer7uaulZNgRmHlHHiM5CPja9wksx4M4st39BldYPgTqA9n7zFZ0fWfKy7i8n+GqguqUoK1fqPuVejtLv3GzUWHyQTQKcGL/FqcsgwgaflB0C77zfhQ9fZUsE2peXIgfro/NQG9tJkwbnunyZqqoMeCP6aMxufol1Ez7k1M2u3eQ8j7tZ7B9PyIidy+bTT9vybwLkJeRgd5y+UXgzpKJoRZvbjwhL28Y073Vpu3WH3g90ck8ZWBHcqTynGjJHZN7yiylE3nleOXH43B1d76/Uw6WePnH4/jl+Hl5W0ZBhZxc2hxL37/mhlVYE0HAK4d1k9e/3WubHoBr0nLww+FceGk1+NsVg9sMNHqKy1OU7dhSL0VLyawo87dkjXWVv3lghWXytphiqxaWnncllVYDKxi8sxkG78hjiBO51aG95PWyvV/D3fjXKcE7bw4usJne4+fJsrok41lknnK9QSAiABRmUvZ7UIQyaY3a5usXgC0hs/G24TL8kuGcD4Lnc84iWlOIAZoziI1l4LUtPeb/ETUmHQZX70Halv91adt/tzcLpdUGeIV3x/CUkXAGnyAlQ9rf6NyhKaQe7jKwwlBnrM82EUMIBC+dVvZ8EnJVNLTiYGaxDOiITMPbJrU+2MgSNBAfeD3RqXwloNszKrDV3xON7f9y+SB5/bUNJ+Q2dOX+dVtOKgE74V8/HsPXe85h8rM/YdZLG7HvbFGLPe/ak1E4a7BybPHz8fwul86WVxvw+DcH5fU7p/REnxZ6L3qieSnx8uTBjjOFMrDaYslsrG1KZq2DdZaTFW0Faz2J5UTE+fIaVJiftyHseWczDN6RR9H1VyZRJuWtd2o5XWcEGZU/Hn4htmvornYhYZE46q1MoczYrjS3dyUlxQXw0ZhTyqOUM4PUPvmTn8RfDAux/IxzUvHz05VgcK42Et4+TSfnUUOx3ftgd+Q8eV2/7k8wdrK5v/h/q9dvkNdvHNvdaQ2gfcO74bna6/C6ZoFTHp/UnnnnutG7rOIqGIwmeOu0iDWXy1oPrcgpUUfwTvTXemT5fnldDFFLCGt9aFZ95l21h2beWYJ3bWTeCZcNiZMDG0Sm5kNf7JMBYVdk3cNRBGi3nyrA7z7fK78XffuueX0L1h/JbfB/qg3K3z4fr7Y/gg+IC5JtOUS23uYTF4KEHWU0b0fx2hSluPfOcPSIJ+cSA0DG91ROuH3TTBZj/bCKONsFNOsz78xD6drqceiRPe+ssoh99Qw52Qq3JHmUvuPnoNLkjTjk4cSBrXAXItAYbFJKCgLDopy9OB6lJHEGsk1hOJrteuVtRdln5GUJAuDr3/rZaGpounlq6d6MIuQ5YYJhaZZSznPem0HX9up13d9QavJDn7rj2Pnt653a7rvXvI+3y5fied9/y+Cds/iHRGFZ3RV433Cx05aB1MZ6YAVcPkgjggTWve0swyvSzQ3cPZnIcprx/AbsO6tkjd0xObnN/yP6vXlq5p3oKWbJQGqrbNbiicsHIcRPj4OZJfjTVwdcMoCXWVxZv04z+ivHJCJwbelZJ0pn7/l4d4PnfLW5nNa7HcE7UdZ6ycAYef3vq4+ivJNPDTH84/v9WdDrNPjHNSmqCiRZXD4sXl4u33W2ycCfQ+bgnT0y78rMwXhX791oS5YTEdb0WoacbIVbkjyKr38QjgSMktfzUlfAXYiJPNfWPIbFNb9HSCSDAbYUefFvMK76Ffwlb7LsleVKSvPPystCrdL8ntovOtgXI7v5Y4L2APZv/9Hhm85w/qS8rAjw/IbPthIZk4ADve6Q15P2/AOlxcqE7Y5k3YWlviivd0vsabPeNF1pRi1KQkRWAxEp0jKVE2UDGk1tTIpUgnenVRC8E5k8xeYg3FXDu2FoQttDdTy5550loCsyMQN82jcrUWRqPnP1EJlx+mlqBua8sgX5LpC0KQI/xRW1MiC56Vhe/XrdOObCyaR7Z/TGR0vGYWRSmAzevL/ldJPgXXsy74S7p/eS2XfidbP2XOc+tv98LF9eimUca85AUxuR/Rro4yV7KW44quw3C0spbc+o9gWW28PffIxgoaay2QBvL9nKykKcxHFWlYQnYvCOPE5NH2WSYXTmOriLwgoD9ph6Y5tuFHx9WYJnS73iIpAY7i/PgFqaCbuKimKlnKJUr86Dqa76XeBqfOz9FKL3vebwx9YXKwfjdSGt9zGihoZf+zDOaWLksJH//fffHc6662k8jTKTHwZc+bBTN634ANpPk45RmsOoqFCypsn9eXl5YdiwYfJryZIlcCXu0ls+zdw/amB8w+Bdjwjlg/EZ8zALT3Y8V8nkmdwnEi8sGNau/xNgLrNztZOMtnAqr7xDWXfWPd9eun44wvz1MgD48kGdw8quRcmupT+dJQCWXVyF97ecQcqTazDsybVyKq4QF+KL6f2j8ebCkfjirvH43cx+MrPu11OVPtxf7TmHWnPmYE0HMu8sQcwnzT0At+VqOtX7TpTzCuN7qfdYU2S2LhidKK9/sEWpehHEfrFkhUYH+djs8QLMr2c1Bu9EoE4ESi1ExifZDoN35HH6TLoGdSYNetWdRObpI3AHhfWjtJ2XSeKpRNnBRf1joIURu/fthivZHTQD/arexSfdn3T2oril6BFKD7V+5TtRZ3Ds4IrASiVr0juqt0Mf1xOGjRTN/BeW1P4ODx0fhB8P57Tr/1WUl6Dbtr/K6we634SQcOe2FxBZE594/xVf+DyJ6lzlAxy5v9DQUOzZs0d+/fvfHQsu25u7DKxIMw8YGNg48848vOKMCjLvjuYoAf0+0e3voWUpZbQ0ePckJ/PLOp3ZND8lHqvvn4LkCH8U1Whw49upcvCCJevNHj7fkYGxT63DoMdW4/5Pd2NdWg5ufnsbLl/2M97++VSzPdWEmYNiMarHhUqKqf2i5KTl/LIapJoDaBeCd+0P5kzrFy0noVbUabD2UMMeem0RgakjOUowebTVsqmRpXR2V3phfemsJXAnssPCbDgRtfF0WbWVKlsmzgp6HcNNtsStSR4nLCoOHwffjltq/oC1Ge4R7a/KPoLbdd9jptceZy+KR5oTW4RUn1/j1sN3udQgk5ySalTDG4FhHFLSGb2GjEcuwuGvqUZtjmMD9ccNMThujEdIQj+HPq4nGDR+FpLGXyOv//6/+9rVA2vvO/cjFvnIRhRSrn8crnBSoBJ+8np1BSfOkiOec64/sEJkBVmmiraUeZdRWOGS/cts6ViuOXgXE9jhD/uVVtlenkIMb2jPpNnW2mS8dcsIBOpNSC+oxLubT2Ph29ux7KfjTfqXtVdL/+/V9cflcAcRcBPZd1/tycSS93fUH7Olm0ssrxx+ocWNyLxrjghajE1Wst32mvsf1ve860BAQwSW5g6JldfXNyr5bIuYVCv0iQ5ERKDtMsvcUd+YILktRcJEtjmD09IzOTzA26alnZaBFRZq6nnXOHjZkec6tY1bkzxS9Zil2GhMwapDHeup5CxeWbvwZ/1HuMbwjbMXxSMNGTIMfqhBFApxYv9muIrcUuXgIcqGqfpqotFqcSpisrweXrTLoc3I76m8ExfX/ANRfUY77HE9ye8v7YdB8cHwKs/G6VevQkFu0wlwFqnf/xvj87+U1/Om/R1+AbabCNcVlVpL8E75UEb2tXHjRsybNw/x8fEyePrVV00niL/66qtITk6W7SdGjhyJTZs2degxSkpK5P+bNGkSNmxQphq7Co0bDKzYe7YIogWkKD+zTJe1EH3BRKlgbZ0JmUUu0LzMDkRW1XOrD2OjOcDStwPBO0/OvDvVgUmzLUkK98cjKXX4/cw+mNhbCYg9t/oIrn19C257NxWTnvkRi9/ZjtTTLR/3iyyrL3eexcx/bsCkZ35q8LsimPfsqsN4dpVyIvDuab3wYgslz6KP4aOzB9R/79coUGNtcLcQefnMqsNyEq0l886ng9M3p/SJlJebjp2XQcX2WnUgS15aBl+omQig9TYHkC29OfPKlOBdlI0Dm00y71QWvLPOtvNi2axNta9rKJGbmTkwFn/9/hC2ny6QTWVDbZgKbQ+GMqUXW7W+7abG1LlSvd0BozC84hfk7foWvVMmucRmvPTcK5imz0Ok6QFxWOvsxXFLvoPnARu+Rkr1TjnQAHr7l56LzBEh1F8vp+FR5w6i31k0CpkvPoRhdWk4+fplqLrlc8T36Nfkg8e7W8qwTBeEY/HzMW6akrHnCqo1fiIFCrUVrjfJ2hOVl5cjJSUFt956K66++uomP//ss89w//33ywDexIkT8cYbb+Cyyy5DWloaundXmsmLwFx1ddPp1GvWrJFBwdOnT8vLAwcOYM6cOdi/fz+Cg5ufQCjux/q+ROBPqK2tlV+2ZjBcuM+a2lp4a10vgrf1hJLlMyoptNlt0DPCH4dzynAoqwhxwR1777Tcnz22rS2I4M89n+7FmrTc+qE2yeF+7V5eH/Nn3YpqQ7P/x9XXvyVioI8leJcY5tPp5Rf/L1APXDkuAXdOTsZH29Lxt/8dwY4zhfW/c7awEuuP5OFXk5MxvpdSIiqCZWlZpXJIwZ6zxQ0C39e9sQVXDY/H3CFx+GLnOXx/IFve/tClfXDHJGVC8Ivr/JsMWZk7NAahvloM6RaMIzllGJMU0uJ6DYi9ELD8ctdZLBilDLny0nRsXw6O84efzoSiylrsOp2PYYmtf14QAb6F7+xA6mll+1zcP9Ltnjv2eP73jw2UZcT7zxZhSu9w5BQp+zYiQG/T7dNoXgX0WlOX7t/dXv/Wfe70Wo2q1r2z2rt+DN6RR+oe4Y+5kTkYUrQOhzacx/jLFsKVmSqU4J3BV939KOypttdMYP8viDjn+MmkLRlWsQWJukykef3a2YvitvqPn4PS9X6I1hQhbc8GDBx3qd0f80x+aX0mAHVedIgfKhe8ivOfXqEMonhnKrZ0X4iwYbNRV1OF9zKi8fku8WGqD17o/TaevPkil9rcNTp/wAgYKjmwwhFEIE58teSFF17A7bffXj9o4sUXX8Tq1avx2muv4emnn5a37dy5s9XHEIE7YfDgwRg4cCCOHj2KUaOUCfaNift84oknmg0E+vvb/r1BScjyqn8MXxdM5FiVJiJQWviXZ2LlynNNfh5Yp/z8mw07UHWic8HHtWvXwhXtPq/BmqM66DQmXJ5kxJBwAzb9uKbd/z9bxhC8UFReiZUrV7rd+rfkfJUoFfWS22X/lvU42MXKRMv6h4kg2xBlu3trgTh/YFe+BtvytHhj0yn51Zx4fxP6h5iQXw3sK9Diy12Z8kvQwoSrk43oVnIIK1cekrfF6bQ4bVWoFqQ3oejIdqw8CtzcDaiJBXb/8iNa6qhcXtvw4/axU+nykc6mn8bKlcrU+vbqHazF/kINPly9BZnxpjafT6mnlceN8zMhfc8vyNgLt9fl53+ReALqsGHPUSRXHMbP55Tvq4vzWn3dddSRYuV+LQ7s3Q1TetdPuLjL67+sWKy78mKvqWr9Pc3T1r2zKioq1Be8KywsxH333YdvvlFKD+fPn4+XX35ZNiBu7UyZOPh688035f8fO3Ysli1bhkGDlMk+wrRp05qUTyxYsACffvqpHdeGuuqm0P0YX/Y9dh0oBVw8eKetVFL3Tb7icITsoef4K4H9j6Gv4SjyszMQGatMnXKmCON5+bctKNr5y+KufHz9sS94AkaX/oC8fWsABwTvQvb8G3t9Xse2uisAuEYWp7tK6j8c2betxZEPFqJf7WGMz3gTEF/i4K/mPgDjsHhCD/xpzgDoXKxvigze1QJ1Vcy8c7aamhoZmHv44YZTiGfOnInNm9vXKkEcA4qgm4+PD86ePSsz9nr2bDkj+pFHHsEDD4is6QuZd4mJifIxW8rW64qyyipg+0Z5/ZJLZiLI17UO4cXUxkd3/iRyfrBo9iT0j21a3n7u51PYsfoYTCHxmD07pWP3X1srP7xdcskl0Dsgw7qj2WUv/usX8fELd03thfsv6vggo3NFlXh67yYYoMPs2Ze61fq35gcxYGH3Hjm8Y+6cCZ2+n5bWf3Gj3/tuXxb+9HUaauqMCPf3lhnyolRybM8wTOsb1aA/3e6MIrz84wk5yVaUON89tWeTjLbCbenY8t1heT0xzA93TO6Beeappe31Re527EovktfDomKA83kY0Lc3ZnfgeSLW/4dz67C/EKjwj8Ps2S1PMd50PB+vrT4GoFS+T3z/wBSXe7/oKFs9/w17s/D1mf3wC4nE7NmjsHvlYSA9HSn9emL2pX1ttrxxGUV4NW17/fdTJozF2OTOJ2i42+v/v3k7cbxESUwJDQ7C7Nm2f+17Gkv2flvc+5XcyI033igPuFatWiW/v/POO7Fw4UJ8++23Lf6fZ599Vp6tfffdd9G3b1/89a9/lU+OI0eOICjowoHHHXfcgSefvDAR0s9P6XVDrity5JXA2f+gf9k2VFWWy9JJV+VVrfxRhz8z7+wlMj4Jx3S90afuOE5uWYHIK0VgwHnKSgoRqDE3yo1h8K5L23LUPZi96iKUFfbHBpNJ9sOyJ03hCYRoKhDs4uX47iK2e19E/eEX7Fz1DrwPfIrYqhOo03hhdnwZbp83ASOTXPOkRq1O+ZtiqmbmnbPl5+ejrq4OMTEN+zqJ77OzlVK4thw6dAi/+tWvoNVq5XvISy+9hPDwlv8miyCf+BInfMWXeHxBfLiwxwcMvblXluCl93K5DzE70s+jvKZONn4f1C2s2ebv4nZBlBp2dvnttX27Ys3BbJw6XyEDJL+e3gd6fcc/XgX7m+rLPLU6L9lY313WvzVHcpVskoHdQmyy3G2t/5Uju+OiQXHw0mqaDA1obEzPKHzQs/XJ5QvG9MDG4wUYnhiKey/q06ll/mjJOAz4P+WzaWWtsb5PXke3R89g5TkiAoFeXl4tHuvc9t6FHsBXDOuG8CDP+cza1ed/eKASvC2trpP3U1BhkN/HhPjZ9HUV4t+w52eQn49Dnv+uwsdqmrK3Xquqde+s9q6bxwTvxEGXCNpt3bpVZs8Jb731FsaPHy8Dcf369Ws2606UVfzxj3/EVVddJW9777335MHexx9/LA/iLMTZ2NhYZdIPuQfR1yz363BEawqwd8t3SJmxAK7Kt0bJvPMK5tRRe8qPn44+GcehPy5KWZwbvCvMSYdom1tu8kVAEHsddsXwkeNxbFUFagurcDi7FAPibJ/1Yi2o5IS89Iq50LCaukbn5YWRc+8AxJfZXBffqGkhU7C1KAT9A1Mw1NkLQ1LjD7OmDgTzJ0yYIHvcddTSpUvllzhrHhKiNKe3B+u1cMWBFT8dUXq9ieymlqY2DjBn44keaFW1dR4xgTGjoAKPrlCeNzeNTUKgj1eXG9xX1BgQ5OsZH1IPZSnZJAPt/HfZWrANt50YJPKfxaO7fB/Bvl4oqTLIALcghrd0VEKA0kvsfHmNnHqbZJ7g3JrGU5/VzpKBWFJV22DarK0Hx0UEejc7kEYtrJ/fXlrXqppwdx4TvNuyZYs8aLIE7oRx48bJ20TJRHPBu1OnTskzsqLEwUKcRZ06dar8P9bBu48++ggffvihDOyJniuPPfZYg8w8ZzcyVkszx46u/8mIqYg+vwJV+79B7WQlQOuKAg1K8M47KKpT+5D7v33P/5ARV2LZqRxsKh2LtyurO3XwZCtF2Wcg8u3Oa8Ph3cXXrdr3v2jc3j/UJHvBrNx3Dr0j7XuWOa72jLwMShjo9G2u9n3vzPU/HjYZn5/sid/69Lb746t1/7ZXZGQkdDpdkyy73NzcJtl47qpBDNIFg3c/HlaCd9P7t3wSUnxAjgz0QX5ZNQ5mFmNkkntXGxjqjPjNp7uRX1Yjg1P3zOh4uayFj5fI+FQCs5U1dR0K3ompqSIIMXtIHFxNmhOCd67Iy9z2QQwkaZyZ1F5iQG2vyAA59OVEXlm7gnfNla+rWbB5yFhJpfI3VQwAEWw9fCzM3xviHIZlMLDaps1af77ydrGWJ+7OY4J34oAtOrrpAYO4raWSCcvtzZVZnDmjfDgTbrrpJiQnJ8vMOzGBTPQ52bt3b6uNEx3dyFgtzRw7uv5V+j4YB6BXwSZ89913shzGFb1XczeCjYWYklGBU11o6sn93/rzX/wRfUOzACU1Giz7fDX6hTrvE1DNma0YIjLwEILdNmqSq+b9PznkPBaXfYmeW/KxsupRuz1ObWUJrkEpjCYNDp7MxuEM2zU47go173tnrX9OptJ8f/+ho1hZofREcnYjY7Xy9vaWk2TF8+DKK6+sv118f/nll9v1sRuXzdpLw9ida0XvjmSX4nhumcwKmtK35TJEkQU5vHso1qblYHd6kdsH7/53IFuWMAb5eOHNW0Z2OuvOsm0CvL1QVm1AhTk7q7399q59fYu8vvH30+XANldRWlUrM8QEe2fEuzpRxitY9m1nTx4nm4N3J/PKMaN/878jno+l1Qb0iwnCkG72ywZ2R5aszNIqg8zMLqtWgne2znQVZe/hAcqJCsETsow7wjpgpxejlUk9wbvHH3+82SCYtdTUVHnZXGlEe0om2iqzEP3uLMQEsj59+sjpY7t27cKIESNcopGxWpo5dnT9a6pnoPS5lxGpKUa/OH/0GTkDrkY0ef7NlnUAeuDvc6YiIrDjqdvc/+1//m+uPYj/7jyH8tBkzJ7dwpGPA2z7bD9QANQGxGL27Nldui/u/1rUfrMC87O2QI86hPXrjm69BsMeDm1dBRwGsrXRmHf5hSCBs3DfO+9v36lVe5CTvQNDohK7/Bq2VSNjT1ZWVobjx483qJ7Ys2eP7EvXvXt3ecwl+hyL4zPRMkUMIktPT8ddd91l1+VyVNmsK/t2rzKtc2rf6DYzWKyDd+5OlP8KIuMtIazrQTNRWtfR4F1G4YXAflZxJRLD/eze97UjQV0hNtgXYQHq7hGrt2Te1Ri6lI3UI9K/wXOvObVGpa/e24tH1Wf8UcOyWYPRhMraOpRVKftDlDXbWoifV33wTm1ls3qr4LTluU8qCd7dc889uP7661v9nR49emDfvn3Iyclp8rO8vLwWSyYsPexEBl5cXFy7yyxEwE58SDh27FiLwTtLI2NHN1v09GaOHV1/cX1H8AQklezA4RMnMdABkyg7qlBMkBP5GxogKiSgxSbF7cH93/bz/+J+4Ti/+1v0OfAlvOb9GxonZWNuDL8at1UNxpJhcRhlo9esmve/3i8Qh3xTMLR6F7K3L0eP/sPt8jiVWYfkZZ5vD8S70LZW87531voPLfsZ9/s8jkPnxkKvt2+HPjXvW4sdO3Zg+vTp9d9bTpAuWrRIDh1bsGABzp8/L4eLZWVlyZOtK1euRFJSkmdk3lkFZFyp512d0YQVu8/J6/NS2i7bHJ6oDK3YlV4Id1dQXtNsf6vOsvS9q6xVAgrWJ3nv/ngPSs9r8dRzG3DH5J5YMlmZhCz6vFoseHMrJvSKwMd3iJoTFyqZZd+1+mP7rvS8E5LNpbKtBu/qlDcIlis2/xoT+0K8bxVX1spguRBoh+CddSaurxPb9Dg9847BO3UF70QfE/HVFnGWtbi4GNu3b8eYMWPkbdu2bZO3iSbEzbGUwoqz9sOHKx/0ampqsGHDBjzzzDMtPtbBgwdltoN1wI9cV96Up3Dtl8eQnB0EV+x6V5RzBnfovkOxTzx02jnOXhyPN7FHEKbrX4R3rQHpx+9D977DnLIcuSVVqIEeoeFtv79R+5QnXwoc3oWw9NUA/mKXzXayMgAVdSkwRY7iblE5na/SS0hvYEmrI0ybNk1WRrTm7rvvll+O5JSBFXAdPx3OxbmiSplxN3Ng24PdhiaEyBLCrOIqnDlf3q6+Xa4evBMTdm3B0hervLphIPiHQ7lYe0j0FBQfiKvx1+8P4W8rD2HR+B5NMh03nziP82XVnarisNewigFx7LvmpdPUTxO29Di0R+adCEqJL4FZd82fBBFZdoUVtcgvrakPdHal5L0l1gFBte0L6+e3aKdAtuMxz6QBAwZg1qxZssRVTJwVX+L63LlzGwyr6N+/P1asWFH/Ar7//vvx1FNPydtEP7vFixfLnnQ33nij/J0TJ07Is7jijO/p06flWdxrr71WBvsmTpzotPWl9ps0pBe8dDrZH0L0ZHE1VZlp+KP+Y/waXzh7UVQhMDgMh/1S5PXM7V85bTlyzROuooMajpOnzusx8VrZi66f4QhyzioTYW3t25qRuLX2Dzif8mu73D+5D52vmBcN6OsYvCP7s66EbCuI6Ujvb1V6RC8Yndiu0rAAHy+MSFKy7zYdy4c7K6ywbfDOknnXuGzWMhnTmngKvLv5NF764ViTn+07V9zg+6f/dwjDnlyDBz7fI4dsOEpapmVYhTrLyZvreWfR1cw7EfwuNAePG2dpWjBo0vrQisziyvrbArztm3mnNtbPb2be2ZbHBO8sE2GHDBki+8qJr6FDh+KDDz5o8DtHjhyR2XgWDz30kAzgiTO1ok/KuXPn5FAJyyRZ0QT5hx9+wKWXXiqDgPfdd5+873Xr1snJZuT6RBPS8b0ioYERP+8+CFdTU6wMTinXhzp7UVSjIulieRmU/oPTlmFRzt/xD/3rSNTmOW0ZPE1kbHcc8R4gr5/a8KFdHsNyAqBPDDMJ1E7vq0w11hmbfoAi9RAlswMHDsTo0aMdVzYL13Ayrwwbj+bJwOLNY9tfnjy5t5Jx/rObB+/OlymvfVv1c/M3BxAal82KSbIdsS/jwuecqto6vLf5NIoqarF81znsPOOYcmURJLSU9DLzTgTvtDYJ3oX66+sn936x82zT7W4Zb8qgSZt97zKLKuuDbNoutCxq7USFWlkH7Bi8sy2PCt6JpsUffvihLF0QX+J6aGjDgIg4Wymy66wPhsRQDNEfpaqqSpbMij4pFmLIhLhN9FGprq6WzZJfeukl+VjkPm6OScd2n6UYl/obuBpDiSiFAKq8WT7pKIljlUED/aoPoLjQ8R8eTEYjptT+gmt0GxEbpO4mzrZW0vsKeRlx6lub33dxaTlqS5Vga+9oJeuK1EvvowTvvEwM3qmZKJlNS0urH56mJiLzS5jRL7pDU04nmyfS/nIi36GZYPbKvIuwc+bdWauhFMKXvx6Pl65vueXHvrMXhoH8cjwfVbUXtvFBczacvZ0+X45qg1GukzuXRtu6bNbCx6vzCSC3jFcC5R9uU7JerdWay3IFBk1anzhrHbyzBzH1V62YeWc/HhW8I2rJsGEjEaUpRt/aw8jPTnepDWUqV4J3Br8IZy+KanTrOQBntInw0hhxfMvXDn/8wvws+GlqZIlnZLdkhz++J+sz/WYcMSbim8oUnMq17YeUrEObsdv3Lqzx+6OqyyFI4eXN4B05hytUzYqA0qfbM+T12yZ17O/YkG4hsldbaZUBe882LPF0FyIZ4Ly5bDHM38YDK5oE7y6U9wk9IgLQJ7rl7O/jeRdaxPxwWDnGtDiQ6ZjtnZalZN31iw3q0iA2Ty2b7WzPO2HOUKXn+pnzFXLoQnNls+LhuN3bCt5V2W1YhXDxwJgGvSzVOrDCmz3vbIrBO1KF6G7JOObVB1qNCSd/dq3ecl6V5+WlKYCZd46UFT1F2e6HV8LRCs4p/djyNWHwMWfvkG2ER3fDUz3+g5frrsI3+5pOIO+K4jN75WWVDzOvSQTvlH6VelPTflRE9qAxF8yaXKBw9sV1x1BTZ8T4nhFywmlHiKDCxN4Rbls6KwJ3pdWG+uEDtpo262cum22ceXeuUfBO9NjrGdVyNpsokbXYm6Fk4V0zMkFeHjxX4uBhFUqJp9rZqmzW0g4o0vycyyhomJVZay6bZdZdO8pmzT3v7HUydnKfKHx4+1j8+OBUqI3e6vmttmEd9satSaqRn6D0OfM5sQquxLtaCd55BSlnaMgxQocr5ZXeJWdQa2h4oGxvpbmn5GWBV7RDH1ct5qfEy8uv956zbWP37H3yojx8kO3uk9yWJiAKrxnm4WPtPGcvCqmg510DTo7dHc8txfJdSr+t38/q16AfX0c+2AobjjbMDHN1Iitu8rM/YfZLm+ozqGyVWdNc2az4Gyam+Vr/jtjevlaPGdlosqzIxlryXqosmbX0ab16hBK8O5Zb2iSzz77DKhi8a75stmsfwRPD/ZsP3pkDytaZT9TCwArz68oSzLOHSX0iEReivpP0Pux5Zzd8ZZNqxI25Wl72r9iF8tIL/UCczb+mQF7qQxi8c6Q+I2fgGt2LmF/1BLadckwDZ4vq80rpdrlvrEMfVy1mDopBsJcB/c//gGNpe2x2v2HFh+SlT8Jwm90nuS+vwAg8Y7gBb+AqZy8KqaTnncYFYncimPSnrw5AJPhcMjAGI7ork2M7ano/5eTVrvQiZBcr5WvuIC2rRJaxWkpZRb+7zgQvW2twX1Z9IXPu0RX7Ze84kam1uE8dvrtnfP3P/m/uQPSLCcJTV17o1W2x7lAubvr3Nvl/RaBoTHI44kJ85X7bcUY57rSnIxxW0UDjEtauZN4J3c3Bu/TGwTtz2WzjYCFdEGoO3uWUKINg2AbF9qyf3yybtS0G70g1kvqPxFlNLHw0tTjyy1dwFU9ol2JxzUPQJTrwrD1B5+WF3gNHyi2xJk2Z+OswRUqfoJrAbtwTdiBKSv4d9j5e9f4XCja9ZZP7rK2pRlKtkjEZ3XeMTe6TPOPg1FI6R2R3Guf3vPssNQNbTxbIbLM/zxnY6fuJDfHFyCQl8LfqQBbcRU5Jw0BjuI1KZgXRB1AorlSmzW47eR6fbM+QgZ8/z+6P4ZEmJIZdGAwieg2u/u2UNktTe0UFyvuY0Ms85fd4w1LlwvIafLs3s8NTbVtSVm1Atnk79Y7iZPbmyli7mhnXUvBOlLI393jU8L3HGoN3tseBFfbDVzaphkarxdno6fK6Me07uAJxBnt7RRzWG4chLEppQEuOzdAS1h/IkBNgHaWuUmkYrQ1NdNhjqo3XYKUsumf2/1BnUD4IdUXG0T0y8F9q8kN88gAbLCG5O3E2ubsmB90N6YDRsaX3RM6QVVyJv61UMpB/N7NvhybMNmf2EOW4Z+V+B59A64LG/ecmmgNitg3eKZl3r65X+uMuGJ2I60crZa/NCbOadttcCWDfGGU6+qQ+Sp/BNzacxCPL9+PjbelY+vEuDP/LWtz7yW5MfOZHrEvreq/Yk+aBGaKcN8RfWSe1a5x559PFUuvEFoJ3hjr2vGtLt9CGZaz2GlihZtbBY+v+d9R13JqkKkGjrsfrhrn4Z8n0+tRyZ5INj83L0bhnCdmfOAv9vM9bWFmzGMf3/uywTf5373vRt+o9lA28wWGPqTaDpl6NIgQiGgU4uKnrmbb5h3+Rl+k+faHVqW9yGDXlowM2+vwWq7x/D2OF67RiIM/teXehbNbxqXciw3TpR7vkhNiUhBDcOrHrk9IvG6y0jkg9U4DcRhltrsZoNMmMMuv+c7+/tB8emtXfLsE78XhbTyo9kW+d0KPV/ycyh169aQReu2lEk8CE0C9Wycyb2DuyPoj0yfZ0WZL7/b6shvv44104YTWttjNO5pXLy16tDNVQG32jMlZ7Zd5ZPtt0tSzXk8U3eo2Iag2yLevnX+NJy9Q1fGWTqvQfORVv+izGL1U9kHra/j0/2lKQcw536r7FtT7bGjQfJscQ2zw5qA6Bmirk71jusM0uDv5roEdcJKeW2ouPrz8OR10mrxt2vtfl+9tWHot3DJfiXMIsGywdeQJvb28YTObS2ZqG2TikHo7seWfhjLLZp1Yekv3pgn298PINI5pkEnX2Q/Tw7qFyff53wDWz7/ZkFOGhL/bi+re2YvBjq/Hu5tPy9r9cPghLp/e2yXZoHLwrqaxFVkmV7Fcngj7JkQHtymK8bEgcQpvJdLtyuNKiIzrIFx/cPgZXmb+3GNczHD/+bqqcACwe85n/He7SeliCfz2jlIw/ajpttnEwr6OSzFmvIhPUYJWMYEkIYMCk/WWzQXaaNqtm1sFpBpJti8E7UhVxkHVRf6VJ8pqDXS8N6KrK7CN4VP8JfqP9zNmLolrGvnPkZVzWDw55PDHlraC8Rl5v7gw52U7UlCXycnDpLyjIPdel+/oqPx5PGBZBN/p2Gy0decLBaTWUD8o11QzeETx2YMXnqRn1QasXrhvW5XJZa3PMpbPf7cuEK7rq1V/w+Y6z2H6q4QnfbmG2//ttnXl3Or+8vjzSqwNZWpb7sJiXEt8gWCEqDl5YMAwvXJciv794QAw+vXO8DLQ9MX8QRCxyTVpOk/XtTPCOmXcXWAfTRDCjq0NOYoJ85d8gg9GELKuBL7Usm23XiftIq16VjV8z1HXseWc/DN6R6szsH46p2r3otfc5h/Y5a05VkXKmucyrc9PaqOv6TLoGtSYdehjTkXF8v903ac6Zw/hY/1c85fMegv14ts+eeg0Zh2O63vDW1OHo2rc7fT/FFbU4YS4DGt7JyYrkeUTmhMigFQzVrl3yRx4WvHNg6p0o3RTllcJ9F/XBxQOVXrG2MmdoHEQcI/V0YYOSVFchprO2p/TOFixZcyJ4Z+kblxzRsdLTYKsSwKevGoLnr1WCdI1dNSIBy++egJeuH1Z/W+/oICwY3b0+07Kzz7MTueay2Whm3llYT38V03+7SqvVICFceQ5mWJXOWrLw2Ges/T3ZJvRWekGSfTLvODzFthi8I9WZ1DMUr+v/iYV1K3DywFanLktNsZL9V6ln+aSzhIRH4rCvcnB7bst/7f54pZlHMEGXhvG6w10+80ptK+i3QF7mn97f6Q8iB/dtx1jNIfSL9Ea4VWNwUjfx+q0P3rFslhzypHPsZj5wrhh3vL9DZvfMHRqH317cx+aPERfih7HJyjGQmHjqSuoaRe6sP5DaI3PekgEkHvdgZom83qMdJbPWtFbHFVeN6NZqydqI7mEIaFQy+NtL+shJwqJc+KcjuR1cA2XZT5mzBnuzbLaezqps1hbBO+u+d2esgneWnnd69hlrlXW2YoLVBGeyR+YdP+vYEoN3pDp+AUE4HDBKXs/dscKpy2IsVYJ3NX62m1ZGHVfRU+ljFpq+1u6bryL/jLws8bFt9gI1b+ClS3CF6R+4p3Qxfj6e36nNpN35Dj7z+Que8P2Ym5kaqIESzDXUMPNOrRw5sMLCEYl3x3JKcct/tssBFaOSwvCPa1PsdsLp8mFKD7av97hO8G79kVz0enRlg9um9I3CmwtHyi97NLkX5XyWD70ieNaZ4F2tVUWJj1fHeymLvngLxyfJ6y/9cLzDJ73OFlbIvmsiQGWP7ER3ZR3A6OqwitaGVtSwbLZd/m/uQHn57NVDbbIvqOXgna2e76Tg1iRVMvRRGtlHn1vn1OXQlSoHqsZApecLOUePidfIy741h5CfnW7XxzIWKvdf5R9v18chRVBIOIaNnCCvv/uL0rOpo2LOb5eX3r2mcLNSA7UaS9ms65X7kecNrHBU/oLInLrx39tkf9ahCSF459bRdh2qJabOiuDGoawSHM0phbOJgNXid5ruT7GMMwfFyi97sWTfHc5WtkNCB3vrWXqedcUdk3vCV6/F3owibDia16lJs2LIhi2Hebg7621hq9dSc8E7ls22z6IJPbDlkRm4bnSiTfYFtTJtlsE7m2LwjlSp96RrUGfSoFfdSWSdOeK05fCtUkoSdKENJ3+RY8Uk9MI6n0vwD8O12HC80K6PpS/JkJfG4AS7Pg5dcIs5i2DfkaNIT+9YAC8/8wySjUq2ZPIoTpqlhlbppuEtw2xU+iqDkIjcPfNODBu46a2tyCutRv/YILx/2xi7ZJlZC/X3xtS+UfL6N07OvhPBqo+3N38Sb0hCiN0fv3Hz/I5OwhzTo+t9WaOCfHDzWEv23bEOZd/VD6tgv7sGrPt++dg4eJfRTNmsN0sV2wymipJ9sn+mqXW/R+o6Bu9IlcKi4nHEe5C8fmbzl05bjqAa5YymbzjP/DjbkXF/x6t1V+C7Y9V2fZzAirPy0juqt10fhy4QU/SeituIX7zvRdY3T3Ro05z45Qt5ecSrH8KimCFLDX3mew3+ZrgZZYHKB10ix0ybtU/0Tkx8nfOvTcgsrkLPqAB8cPtYGVhzhPmW0tm95xw6kMOayPxb9J/t+OOKAw1ujwjwxq+m9sRtE5MdHrzraJbWDWO64y9XDMa6B6Z2aTnunNpTlr7uTi/CpmP5nZg0y2EVLWXe2aznXUTLZbNeVj32iBzNR6drtg8ndR1f2aRaJT1mysvA06udtgwPGO/D4prfI6DHSKctAylmmifo/XL8PEqrau22WaIN5+RlaEI/bnoHGjpqkpw6OzzvG+SeO9Xu/+d7cpW8LEy42I5LR+7K0sul2uDcyeWkEubPQPaIbYnA3b2f7EZVrRGTekfikzvGyQwsR7l4QDT8vXXIKKjEbnO/N0cTAzqs/WpKT9w4tju+vXcSHrlsgF1Lhy1CGwXvxDbpCFGitnBcEnp3MfNN9L67qRPZd/WTZqM61qvP01kPkBAlybaQaB60UFRRKycUC7Xmv0WcNkuuUjbrrJMxnorBO1KtxPFKnzO/qlwUl144a+UolTV12F0Vh/XG4YiKYUaPs4kD3UERGswybcLen7+3y2MUl5SgxqQciMf0GGCXx6DmDRo/B2n6IfDWGHBqxV/atZmKC/IwoGKXvB4z5kpuWmoiXFuBeOTDUNnwQz+Ru6g21GHpR7twz8e7ZVBQZG69d9sYxAT7OnQ5/L296k+iOaN0dld6IT5pVC47f1g8nrpyiEMHLzTOvBPbxVnuMmff7TxTKE9stgcz79qeNmurILCYFBwZ6N2gdNZgHljCCZ/kMsE7py6J52HwjlSrW89BuD3wNVxU/Rx+PF7g8MfPLqmqP6va0Z4mZHtiit6fwtbhX97LELDrDbts4vQSE8ZVL8NE3ccIDO56XxpqP41WC+PUh+T1Ye3Mvtu//r8y2HdS2wM9+jM7lpp6oOJFbPa9DxGnvuPmIQeWzdrOsp9O4Pv9WfL63KFx+OsVg502aMAydfa7fVn1jfcdQWTbX/XqZuxKb5jx1y8mCI4W6NvweNDPAdl+LYkO9pXBXOGlH462mUFTWF6D8+U19QMrCM32/bJV2ayQaDW0os5oqh8Ywgmf5EwN/oYwemdTDN6Rqg0YIj6Qa7A2Lcfhj12YkYZf677BlQH7ZOCInC9mwvXyclD5dpQW2z6ge+q8clAVF8nAnTMMmjAXafrB8NHUIuPTB1r9XfEh5bnMoZhT/RQOpjwig39EjdVplawHY619e2USNQje2agM6Z1fTuHlH4/J689eMxSv3DjCqRNCJ/WJRJi/Hvll1dhysn2ZXraw/kjTiapXDe/mlCmJgY1O5vp1sGzW1u6a2ksGglJPF2LLidb3ycl8pd9dfIivzAqjC7zsMG1WSLIK3j30xT58mqoMReOQAHIV9urRqlb8NEKqdomlz9mRLFRVO/bDlzFjB/6g/xQ3GO1Tokkdl9x/FM5oE2S21ZENn9l8E57JV4J3SRE8I+0MIgDnPfcZOWl6ZOmP2L9heYu/Kz6o7D1bjGPanhg74wqHLie5X/AOtUomNanPsmXLMHDgQIwePdphj2mLj0I7zxTgye/SZKns4gk9cO3IBJeYyDl7iNJG5GsHls6uPpjd4Hsx7EEEM53BOuglAqnOLn+MDfHF9WMS63vfteaEOeuLk2absg4E2zLzzjJx9sz5Cny5SxmI1ni6LZEzcXiNbfGVTao2pFsInvb/GJs0S3Dkl68d+ti1hcof2UrfaIc+LrUe3Mnsdpm87nXY9s+HoQeexif6v2Kqbh93g5P0TpmE1JhrUW7ywbe/7EZ5taHJ75iMRny6ZqO8fvXIBNm4m6g5Rp3S0N9Ux8w7tVq6dCnS0tKQmprqwMy7rt2PKG/87Wd75f1cNaIbHps30GUqACyls6sOZKOqts6uj7X+SC5uezdVlulaDIwLlsMWnJF11zjzzl+vc4n98utpSvbdtlMF2NpKRiT73bXMOghry8y7vrFBzQagWTZLzrbugSn49M5xTFiwMQbvSNW0Wg16hnkhWFOJqgPfOvaxS5WDRUMAh1W4kjhz6ezA8lQUF+bb9L67lezBeF0aEoKcfzCuZoNvfhY3+fwLb5aMk2UmRmPDT8K7Vr2HZzJvw+/1/5UNu4laYqrPvGPwjhyg/k9H16J3f/hynyyzSwz3w2NzB7lEgMhiVFKYLLssqzbgp8O5dn2sO97fgR/NjxEd5IPDf5klJ8s6c3tYZ945u2TWIi7ED9eMUjIz399yusXf46TZllmXo9syeHfpoFg5cK3A3GvQgmWz5Gy9o4MwrmeEsxfD4zB4R6rnP/RyuQ16FWyEsc6+Z3mteVcqZ8m0IfGq3weupMeAUTitTZSls0dtWDornlvdDEovkqjkITa7X+o4MSzkTzfOlD1oRKP2f73/CaoqlF49R3dtQL9tj0CvqcOIpDCeMaRWMfOO3M26tBysScuBiCW8uXAUQvwbTjd1hZOq84Ypx0Xf7LVf6aw4aVNbZ2rQb08EVZzZ869x5p2rBO+EheOS5KXoES16EjbnZJ7yd5Rlck3ptfYpmxXlsVeYXy+Nbyciz8NXNqlev3GzUWryQySKcHT3eodtj4BqpUGyd5jz+8xQQ1kJSuns+ZO7bLZpsjOOw09TgxqTF+J6DOAmd7JRPcLx/HUpSNLk4PZTD6D02cE48PRUJH99JQI1lTjoPQQjFz7t7MUkF2cyl81qDMy8I7h02WxeaTU+3paOJe/vkN9fNSIBA+KC4YouT1FKZ384nIuSqlqb3/+ejCKkPLGmwW2WqarO1iB458RJs42J50pKYqgMeC636q1mUWMw4kxBhbzOnneOy7wTvJsJBjJ4R+SZGLwj1fP28cWR4PFyOxTu/Mph2yPUoJRkBkYrZzPJdcTOuBsTq17CPflXt3iGuaPyTil97s7pusFLby61I6f3VnppbjwqNX6IQiEGV++RGXe7/Sci4ddfyfcGotZkBQ7Ex4YZyAh0TnN7UidTB/vb3fjWVoz+2zo8umK/vG1i7wg8Ott1TyINiAuSpYAiILT6QMNeXrbw8Jf7UGrudypKdL+5ZyJG9wiHKwjw0blk5p1w/WhlcIWYaNp44nF6QTnqjCYEeOtkCTKhxTJWW2betRSoY887Is/E4B2R0H+2vEjIXieb1dubobYGEaZCeT0shsE7V5PcIxmRCb1hMJrwrY3KdirPHZCXhf49bHJ/ZBvDJs5CyCNp2DP5DWwf8jiOX7kSwx78DiFhkdzE1KZTEVPwqGEJ9oddzK1FLpd5JwIsj397EJtPXBgykJIQgg9uG4vwANc9iSR6zl2eYp/SWVEueyxXKe8Ugnz1GJoQClcR5Gs1sMLFgnfzUuLlMp3MK8fOM8oxbHOTZl2ph6Kr8LIum7Vx5l1zw1XY847IMzF4RyRid5OvQbVJj0RTJk4e3G73bZJXXos5NU/j9tqHEBatlIeQaxElRcKaHYdtcn9euUrwrjpyoE3uj2zHx8cPwy66HmOu/i16p0yUU4eJOlKuJDKEiOzOHBMxtSP3TmRB3fH+Tny9J1P2t3v5huH405wBePOWUbKvnKubb+7j9cvxfOSWVtnsftOySuS2sbh9UjJcSYCLls1aSnpnDoyp731nTQT0hJ6RAU5ZNldnHUzztXHmnbfVfV94PB7HEHkivrKJzA3s14deiedrr8Ga00ophT1ll9TgsKk7DgWOg07nWgdnpJg3NA6ve7+I9wtuwulDSo+grsiu8kK+KRj+3YdxExN5CF+NESEog666YRYKuadTp05h+vTpGDhwIIYMGYLyciUg4So6EnL7fEcG1h3KkYN5nrh8sMyaWjK5J2KC3aMdQFJEAIYlhkLE2b7fl9Xl+yuurMXlr/yMuS//LL+f0jcKX9w1HteMTHDZ4J2zh2c052JL8O5Qw+BdVnGlvEwI83fKcrk68Tq0V+Zdc2Wz1bWOG8BHRI7D4B2RWdX0x/Fy3VX47xFDk14etnauSDnIiQv14/Z3UeGBPogJ1MseaFkb3+nSfVXV1uE3ZYswqvo1xIyYb7NlJCLnGlSwBnt978Q1px/nrvAAixcvxpNPPom0tDRs2LABPj6u2burrUOU0qpaPL/miLwuettZJoW6m8ttOHVW9M7be7a4/vs7J/eUg4tcLQsxwPtC8K7OBRN6RdBTr9PITLvT+ReC21nFSnZkbIh7BIcdzToTztaZd81l2ZWZezoSkWdh8I7I7KIBMbIE6mR+OQ5lldp3uxz/AXfrvsYk35Pc/i7MlHK9vOyVtRJ1hs4fCB3NKZVlOuEBPogJYcCWyFPovMwftI3McnB3Bw8ehF6vx+TJk+X34eHh8LLsXzfqeXcspxQ3vrUN+WU1soRx4Xj3DNwJc4bGyZLf3elFSDdPMu2s9Udz66/P6B+NSX1cs6+pdbad0c4nkjsj2KpHoHXfu2xL8M5NMjs9KfOuubLZcgbviDwSg3dE1r08egdijnYrTvz0vl23S9S5tXhI/xnG1e3k9ndhg6ZegyIEIhoFSNv8bafv52B6nrwcGBfMRs5EHqS+7YGJwTt727hxI+bNm4f4+Hj5PvrVV02nw7/66qtITk6Gr68vRo4ciU2bNrX7/o8dO4bAwEDMnz8fI0aMwFNPPQVX1VrPuwe/2If955QMs0dmD2i2pM5dRAf5YkIvJcj23b7OT5011Bmx6Vi+vL7sxhF49aYRcAeuGLwThicqwbs9GUX1t2WXMPOuvcE7W2feNfca13JoCJFHcq1TikROtjjyEEad/hcyjsfDZLzbbo3r/crPykuvcE4edWU+vv7YHXkJxuWvQE3qe8CUKzt1P723/QmbfbZjl/8DAMbafDmJyDl0OkvmnQvWt3kY0X8uJSUFt956K66++uomP//ss89w//33ywDexIkT8cYbb+Cyyy6TJbDdu3eXvyMCetXV1U3+75o1a1BbWyuDfXv27EF0dDRmzZqF0aNH45JLLml2ecT9WN9XSUmJvBT3I75sTdyn5eN/ba2hyWOIdh9VtUYcMAfunpw/ANP6hNtlWRxpzpAY/Hw8H1/vzcR9vZXt0FGimqK0yiAnuV7ULwI6GFFb6/qvWRF0tKxv40tnGtotSF7uSi+QyyMG9uSXKa+FyAAvuz3/rS/djcZ04fnmpTF1eD1aW3/r+7YkI9w6PtFtt5Un7v+uUvP6q2Xda9u5fgzeEVnpP+VaVKc+ikRk4mRaKnoOtk+gJaxaab7sF9OT29/FRU25E1i+AkNKNiI/OwORsYkdvo+Y0v2I1xQgLzbKLstIRM6htQTvmHlndyIQJ75a8sILL+D222/HkiVL5PcvvvgiVq9ejddeew1PP/20vG3nzpaz3RMSEmSwLjFReY+fPXu2DOS1FLwT9/nEE080Gwj097dT036Nkun5888/40zghZsPFmrwzlEtao1KeC9Yb0Jw7n6sXLkf7k5jEMEOHU7mVyAzHli7dm2H72NHntguOkTra7F61f/g+pT3ldy8PKxcubLBTzqz/rZWKON0XjiUWYKvvl2JMoMIHntBpzFh6/p1sGfSlyusf2ecKbuwX7dv+QVnA2y3/seKlee3xQMDq7Bt4w/wRO66/21Fzevv6eteUdG+1hAM3hE1mjq7O2AMhlf8gpwtn9gleGesq0OMMVc2rwnv1pfb38X1GjoBR77pj36Gwzi2+jVELupYKVXx+Rx0N56T15OGTrPTUhKRM2i0ygcmLVw/i8eT1dTUyMDcww8/3OD2mTNnYvPmze26DxG4y8nJQWFhIUJCQmSZ7q9+9asWf/+RRx7BAw+IbOoLmXci8CceMzg4GPY4K//Yzh/l9UmTJmFQfLDMtlt1MAfL9x9GrbGm/ndH9ozCnDnuURraHj+W78GatFzszNdi8RUXyd6EHZG25hhw/BTGDeiO2bMHwNX9ZssaeRkeHoHZs0fX73/x4VUEkzu6/rYmnncvpP2EkioDBo2ZgpKqWmBXKuJC/TFnjtIz0tZcaf07Iy2rBC/s3yqvXzJjGpIi/G22/qL34CtpqfXfz7x4httMlVbL/u8qNa+/Wta9xJy93xYG74gaqRt4BbDjFyRkrobJaLR56Wxe1mnEaAyoNekQ3Y2Zd+4gf8S9+OiX7diaOR7/M5oaNJRuy6ndP2IYgAxNPBIjY+26nETkYObMOy0z75wqPz8fdeLEWExMg9vF99nZ7euVJoZTiD53U6ZMkcEJEYSbO3dui78vJtGKr2XLlskv8fiC+HBh7w8YolxbPMbGo3m477N9TX7eNybYoz7kzB/WTQbv9hZo5H7q6Lodz1OmovaPc4/tcs3IBHyx8yzuvahvk+V1xPOrPRLC/GVAKqu0BhU1ynM/PsTP7svmKuvfUb7e3vXXA/18Or0Oza2/r8+F+xb8fLzdcht58v63FTWvv6evu76d6+a+XWyJ7Fk6a9Ij0ZSJkwe32/z+89OPyMscbRS8PPhNyJOMvOQGfOMzF0eLtfjhUE6H/m/VUSVTIjN8jJ2WjoicpcYvDsvrJmGXj5IdQ84lBllYE0G4xre1RpTl7t+/HwcOHJBluO2xdOlS2VcvNfVC5oujBlYczLxwpv7yYfF4/eaRmNQ7ErdNSoYnmdYvGnqdBvlVmvpAXHscyS7F9/uycDi7VH7fN0bp1ebqnrtmKHb+6WJM7O2aE3GFxHA/eXm2sBJFlUqvprAAHtO2xPptyMfmAysavsd5ufGQGiJqHV/dRM2UzqYFKuWyuVs+svn2Kc88LC/P+3S8dxo5h69ehxvGKA3PX19/XGZktldsvlImoe8z3W7LR0TOURHeHw/U3o3PA2/mLnCiyMhIOfm3cZZdbm5uk2w8d2b5iG4ZQnoyTzbSwpS+UXjm6qGYNTgWHy4Z63Elc6IB/4SeEfL6D4dy2/3/bnhrK5Z+vAvniirl9/1i3SN4JwLOEYE+cGWJYUrZZ0ZBBSqqDfJ6gA8LulpSZzQ1OKa0Je9GwbrGwTwi8hwM3hE1wzjoGnlZmX0MRqs/uLbwk+8MzKx+BpuT7+W2dyO3TuyBK/Rb8VTOXTi0bXW7/k9e5mn0MKbDaNKg56iWG60TkXuyZHVZfzAjx/P29paTZBs3tBbfT5gwwa6PLUpmBw4cKHvmOSp7x/JsO5lfXl9maeuAgKu5eEC0vFx7uH3BOxFUKii/0AdwfM8IhPo3LC+kzksMNwfvCivqy2b9vT37OdgVhjqT3TLvGmfa6Zl5R+SxGLwjasag6dfhMtO/cFvFvdhxptCm2+jYeQOOmhIR2D2F296NRAf54obodPTXZsCw4fl2/Z/1R3LxpmEOfvGdjNBIz8n+ICKFDib4oAY6YxU3iZ2VlZXJ6a/iSzh16pS8np6eLr8XwyP+/e9/4z//+Q8OHTqE3/72t/Jnd911l12XyxllsxaWzLuekZ0cXelGZvRXprXvO1uCnJK2X29bTpxv8P0NY5XsebKNhLALZbMVNebMO29m3rWku9WACm0H+iZ3qmzWxvdPRK6DwTuiZvj6BWDQYDFmAPhqjzIp1FZO5SsH28mRgdz2biZhzkOoM2kwtCoVh3f80ObvLz9mxFOGm3B44ksOWT4icqzwgt044rsYLxbcw01vZzt27MDw4cPllyVYJ67/3//9n/x+wYIFePHFF/Hkk09i2LBhclrsypUrkZSU5DGZd9a9/P67IwOFFUqvsZ5Rnh+8iw7yQY9AJXtpXTt6z24+kV+frXj96ERcNpgDo+ySeVdQgfL6zDsG71oS4qfHpoemI/WPF8PWrMtmReCuI30+ici9MHhH1IIrhnWTl5v2HUNNdbVNtpOhtgZ3Fr+IX+m+RXIYD3LcTbeeg7AzTCl/1ax6BCajcsDanKziSmw7pZz5F32IiMgDaZUyMS3a3weTOmfatGkyaNX46913363/nbvvvhunT59GdXU1du7cKSfH2psjM+8sH8mP5ZTh918oU2a7hfqpJmjSL1QJ3u1sR0WEZUjF24tG4e9XD2UpoY1FmXvylVQZUGIeWBHgw7LZtgKeUUG272VoXTZr66w+InItDN4RtWB8rwg87/8u1hnvwMFNy22ynbJOH8IC7U+43+tLxIW5R+NkaqjndX9HuckX/QxHsPO7N1vcPLu+ewuTNXsxrkdo/RlqIvIsGkvwzsTgHTnOit0XKgKevWaoajZ9kjnzbm9GUau/J4K6IiNM6B7u+VmJzmA9nCKvVDnBrZYgsquxLptl6I7IszF4R9QCnVaDblHh8NEYYNzzqU22U96J3fLyrD4JWh3PULqjyPgk7Ot5u7yetOsZFJ9vWr5TXlqEccf+gfe8n8GDPU46YSmJyBG0zLxTPYcOrDBfbjmpZHW/fMNwTOwdqZp90N0cvBODOkqqlGyv5ohyYkspp6U3G9mWt5e2vlwzr8wSvONxrTNYD6jg6CQiz8bgHVErIsbfLC8Hlf6CkqKGzY87ozrzoLwsCuzN7e7Ghl/3R2Ro4hGFQnz9yWtNJhLv+/wviEAxzmpikTLjOqctJxE5JvNOw7JZ1XLowAqrtJqIAG9cPEBdg5CC9KJM2BcmE3DgbHGLv2fJuosJ9vH4KbzOZCmTzSth8M6ZGkyXZfSOyKMxeEfUit5DJ+K0NhG+mlocWvtOl7eVT8FheVkX2Z/b3c0HmlRd/iaWGn6L/zs7Gn/86gAMdUrZ3IGfv8GodOW5kjvmYei9bd/fhIhcrGwWLfe/JLIVg1V1tsi681NhptPg+GB5mZZV0uLvpJuDd4lhbFlhT5Yy2dJqQ5NSWnJspRARqQODd0St0Gi1yO6lZE6FH+l66WxEhVJCGZConh41nqrPsMmYec0dcpLdJ9vT8ejz/8Kuf8xD37WLodfUYUfQRRh+6SJnLyYR2ZGl/QF73pEjFNVoGvTlVaO+MYHy8miOMpCiORmFln53DN7ZU2CjYB3LZomI7IvBO6I29Jt5B2pMOvQxHMOJfZs7vb0qy0uRUKc0mY7tM4Lb3QNcPqwbXrtpJIJ9tVhc9h+MKNsIb00ddgVMxqC73pPBXyLyXCafYKyqG43NulHOXhRSQc876yCJRpw5UqE+0Urw7khOWYu/k1FQKS8TGLyzq8bTZZl553wm1s0SeTTmNxO1ISwqDjuDJmNk2Xrkb/w3eg2d0KltdvLwHvSDBnkIRVR8D253DzFrcCzGJ03GkTW3YktpDoL6TMLw8bMZuCNSgbrAeNxV+1t0C/DDbGcvDDmt5534KikpQUhIiEMec3C8Yx7HFfU2B++O55TKqbLNBTEPmUtqe0Vx0qw9NQ7WMfOOiMi+GLwjagfdhLvxl+8TsDZ/GtbU1nWqAfK2ygRcXf02rullwl+51T1KSFAgxlz9W2cvBhE5qddQXaOhNUT2NE6lJbNCjwh/6HUaOU32XFElEhr1tasxGOv74aUkhDppKdUhwNzzrnEPPCIisg/WdBG1w9BxM7Eq6GqkV/njfweyOrXN9p0tQhV8EN2T/e6IiDyB1pz1U2e0miRAZCfX96zDrEExuHtaL1VP1kyOVDLqjuc2LZ0VvfBEAC/ET4+kCPa8sydm3rkeMYmZiDwXg3dE7XmhaDVYMDpRXv9kW0anttnes8XycmiCestdiIg8iXdlDk763IQthgXOXhRSQc+78TEmvHx9Sqey/z2JZRDF2UKlt521vWeL6o+11NoX0FECrXreiSxkHy9+rCQisie+yxK103WjEjFXtx2PZt6NY3s2dWi75Wen45WS+/Anrw8xjME7IiKPoNVqodWYoAUz79RK9LtLS0tDamqqsxdFNbqF+snLzKKmwbuDmUrJ7OBuPFFqb/5WPe/UPESFiMhRGLwjaqfYEF8sjjiAYdqTKP7xxQ5ttzM712CQ9gym+xxGaIAPtzkRkQfQ6JQPr1ox4Y/1SkQOEW8O3omed42dMJfS9o1RBluQ/QRaBe8C2O/OJbBqlsizMXhH1AEhM+6XlynFPyH33Kl2/z/DSSVTLy9iFLc3EZGH0GmtyheNdc5cFCLV6BbWcubdibxyedkrisE7ewvwvvD+529VQktERPbB4B1RB/QZNhlp3kOg19Th5LfPtev/mIxGdCvYJq/79J7K7U1E5CG05sw7ycTgHZFDM+8a9bwrrqhFflm1vN6TwTu7sx5YYZ2FR0RE9sHgHVEH1Y67R16mZP1X9rJrS/qR3UgwZaHG5IU+4+ZwexMReQgNM++IHC7BHLzLLqmCoe5Cv8kT+UrJbGywL4NJDg7escegazCxfQORR2PwjqiDhk67Dke8+sFPU4PjXz7Z5u9nbv9SXh7yG47A4DBubyIiD6H1sioVY+adKjly2iwpIgN94K3TwmhSAnhCtaEOz646LK/3ig7gpnJw8G5czwhucyIiO2PwjqiDNFotaqb8UV4fkbsCWWdPtVoyG3vme3m9qqoSGJkAACM5SURBVNcsbmsiIg+i0+qxsW4I1teliD8Ozl4ccgJOm3U8rVaDqCBl+FduqVImu3J/FraeLJDXRyWFO2Gp1J3lNS6Z29wVcGAFkWdjgwKiThg8aR5Wb52Lz4r6Q/dDPt68pQc0Gk2T39t7Ogd7avoiQFeM/hct4rYmIvIgGi9v3FL7iLx+2pvZPkSOEhagl9Nmiytr5feHskrl5ZjkcPzmoj7cEQ4wKD4E4tC3W6gfooN9uc2JiOyMp4mJOpl9l3TLa9ikGYm1h3Lx7b6sZn/vP9uy8bhhMZ4d+AVCwqO4rYmIPIhOe+GkjVHU8BGRQ4T6edcPqRCO5SjBu8uHxcvMPLI/kf245eGLsOr+KdzcREQOwOAdUSf1jw3GPdOVs7uvfrUeOedONvj5wcxifLM3U15fPIlngYmIPI3OKuO6jo3CiRwmxF8vL4sqauTlsVxlWEWf6CDuBQeKDeFwEFfCP0NEno3BO6Iu+PW0XrgtIg2fGx9AxdtXIPec0v+utLgAFf+5AsM1xzAvJZ5TuIiIPJBWC+zyuROHfBbDWHzW2YtDpBqhfubgXWUtKmoMOFtYKb/vEx3o5CUjIiKyD/a8I+oCby8t7rhuPqrfWYZk4xkUvjUZ28OmIr5oB0absrHMJwNel93GbUxE5IG0Gg38UCOnj1cZ6py9OESqEVqfeVeLE7nl8npkoDfCApRyWiI1EUngIuuuZyR7rxJ5MmbeEXVRXFI/1NzyP5zS9kAYSjGm8DskmLKRi3CUX/EOokODuY2JiDy0512d+VCqzmhw9uKQEyxbtgwDBw7E6NGjuf2d0PNOlM1mFitZdwlh/twHpErfLJ2ESwfF4N+LRjl7UYjIjph5R2QD3XoOgOGRVOxc+wFqzu6BNjRRTpftExHD7UtE5MGZd0Zz8M5Yx+CdGi1dulR+lZSUICQkxNmLo76ed5W19X3vwsy3EanNkIQQvLGQgTsiT8fgHZGtXkx6b4ycfTu3JxGRSoihlpbMO5ORZbNEDu95VyGCd8rE2TB/lswSEZHnYtksERERURfLZo0GZt4ROUqoOVBXXFmLQnPwznIbERGRJ2LwjoiIiKgTNNZls8y8I3LCwIoaFFfWNLiNiIjIE7FsloiIiKiTdpv6INRUil5eftyGRA4umxWZdwXl7HlHRESez6My7woLC7Fw4ULZMFh8ietFRUWt/p/ly5fj0ksvRWRkpDyDvmfPnia/U11djXvvvVf+TkBAAObPn4+zZ8/acU2IiIjIHdxb9zssqPk/1IQkO3tRiFQj2By8M5qA9AJl2mwIy2aJiMiDeVTw7sYbb5TBt1WrVskvcV0E8FpTXl6OiRMn4u9//3uLv3P//fdjxYoV+PTTT/Hzzz+jrKwMc+fORV0dm1MTERGpmdZ8JFUnoghE5BC+ep3sOSlkFSvBO06bJSIiT+YxZbOHDh2SAbutW7di7Nix8ra33noL48ePx5EjR9CvX79m/58luHf69Olmf15cXIy3334bH3zwAS6++GJ524cffojExESsW7dOZu0RERGROuk0SgDBaGLwzp2JY8UFCxY0+P6TTz7BFVdc4dTlopZ5iYExRhOnzRIRkSp4TPBuy5YtslTWErgTxo0bJ2/bvHlzi8G7tuzcuRO1tbWYOXNm/W3x8fEYPHiwvN+Wgnei1FZ8WZSUlMhLcV/iy9Ys92mP+3YHXH/uf+vngdrw+a/e5z/3vfP3/fvax9DD5xwqMt5FbfBUuzyGGp/bjiaOEy2tU0SFRY8ePXDJJZc4e7GoFXqdFtUGY/33IeZSWiIiIk/kMcG77OxsREdHN7ld3CZ+1pX79fb2RlhYWIPbY2JiWr3fp59+Gk888UST29esWQN/f3/Yy9q1a6FmXH/ufzXj81+9z3/ue+ft+0GmUkRoSrF5ZyqMZ8rt8hgVFRV2uV9q3jfffIOLLrpI9jkm1+WlU7JeLcICvJ22LERERFB78O7xxx9vNghmLTU1VV6KgRONmUymZm/vqrbu95FHHsEDDzzQIPNOlNqKDL7g4GC7nJUXH97EWWK9Xn1nHrn+3P98/vP1r8b3P773Of+979TuP8vLoYMHI37ELLs8hiV7X802btyI5557TlZEZGVlyV7EjUtaX331Vfk74ueDBg3Ciy++iMmTJ3f4sT7//HPccsstNlx6sgcvS8NJcwltgLeOG5qIiDyWywfv7rnnHlx//fWt/o4obdi3bx9ycnKa/CwvL09myXVWbGwsampq5CRb6+y73NxcTJgwocX/5+PjI78aEx8u7PkBw9737+q4/tz/fP7z9a9GfO9z3nuf0Tz7SwOj3ZZBze9r1gPGUlJScOutt+Lqq69u8vPPPvtMDhgTATwxiOyNN97AZZddhrS0NHTv3l3+zsiRIxu0NLGuihAtUSyB0l9++UUOKSPXprfKvBMls/Y4WU9EROQqXD54FxkZKb/aIgZTiOES27dvx5gxY+Rt27Ztk7e1FmRrizjQEwfN4sz+ddddJ28TZ3QPHDiAZ599ttP3S0RERO7PqNECJpGRzwn09iQCceKrJS+88AJuv/12LFmyRH4vsu5Wr16N1157TbYyEUTWXlu+/vpr2c/Y19e31d9jb2Pn9/e0TJsVvL20Ht0bkv1Nnd/f1Jm4/7n/rZ8HaqKW535tO9fP5YN37TVgwADMmjULd9xxhzzbKtx5552YO3dug2EV/fv3lwdxV155pfy+oKAA6enpyMzMrJ8uZsm4E19i4IU4GPzd736HiIgIhIeH48EHH8SQIUPqp88SERGROlky70x1DN45i6iQEIG5hx9+uMHtolWJGC7W0ZJZcfzYFvY2dn5/z5pKUSarBPBqqiqxcuVKeDr2N1Vvb1uB+5/7X608/blf0c7exh4TvBM++ugj3HffffWTYefPn49XXnmlwe+I4JzIxrNuSixKMCwsJbqPPfaY7Lcn/POf/4SXl5fMvKusrJRNjN99913odOytQUREpGZGKMcCJiODd86Sn5+Purq6Jm1S2hou1pilguPLL79s83fZ29j5/T1fOfELcquUITEhQYGYPXsiPBX7mzq/v6kzcf9z/6v1+a+W535JO3sbe1TwTmTFffjhh20OmrC2ePFi+dUaUTrx8ssvyy8iIiIii3RtAky1NfDTB3GjOFnjnmcdHVomqi2a65/cWm/jZcuWyS8RPBTY29i+rLev3uokuiib9eQPdhbsb8rezmp4nreEz3/1Pv89fd/r27luF8Y0EREREVGH/MP3Hlxe81eUxI7nlnMS0RtZVEM0zrITw8W6MrSsPZYuXSqHYqSmptr1cagp64EVeh0/0hARkWfjXzoiIiKizh5ImZvm1xkbZvaT43h7e8sBY4174ojvuzK0rD1E1t3AgQMxevRouz4ONWU9sMI6kEdEROSJPKpsloiIiMiRdOayzLpGbTnItsrKynD8+PH670+dOoU9e/bIlindu3fHAw88gIULF2LUqFEYP3483nzzTTmQ7K677rJ75p34Ev1qRNktOY6XVbad9XUiIiJPxOAdERERUSc9UPkyhvjsRtnpx4Bet3A72smOHTswffr0C9v9gQfk5aJFi+QQsQULFuD8+fN48sknkZWVhcGDB8vpo0lJSXbdJ4173pHjWGfbeTN4R0REHo7BOyIiIqJOCkcREjT5OFpTym1oR9OmTWsydKyxu+++W345EjPvnMdLa515x7JZIiLybMwxJyIiIuokI5SJl0YjM6+IHIkDK4iISE0YvCMiIiLqJJNGCd5pGLxTJQ6scI3MOw6sICIiT8fgHREREVEnGTXKoZTJaOA2VCFRNpuWlobU1FRnL4rqWJfK6tnzjoiIPByDd0RERERdzLwzMfOOyKGsA3bWWXhERESeiH/piIiIiDrJZDmUYvCOyKG8tFbTZr04sIKIiDwbg3dEREREnVToFYnjxnjU6IO5DVWIPe+cx8sq845ls0RE5OkYvCMiIiLqpM9CbsfFNf/A6e5XcRuqEHveOY/1kAqWzRIRkadj8I6IiIiok3Tm0j2jycRtSOSE156gZ9ksERF5OAbviIiIiDp7IKUxB++M3IREjmRdKqvnwAoiIvJwDN4RERERddK8kk+wxvv3SD75IbehCrHnnWsMrGDPOyIi8nQM3hERERF1UqixEH215+BTlc9tqELseecaAyu8rPrfEREReSIG74iIiIg6yaQxH0qZ6rgNiRxIb5V5520VyCMiIvJE/EtHRERE1FkanXJpZPCOyFmZd9aTZ4mIiDwRg3dEREREnWSyBO+YeUfkUNYBO+tAHhERkSfiXzoiIiKirgbvmHlH5LSBFSybJSIiT8fgHREREVFnmXveaRi8UyVOm3UeDqwgIiI1YfCOiIiIqJMqdUE4a4pElVcQt6EKcdqsa5TN6lk2S0REHo7BOyIiIqJO+jlqASZV/wvbkpdyGxI5kJeWAyuIiEg9GLwjIiIi6iSdRsn+MZpM3IZEDuTFzDsiIlIRBu+IiIiIOnsgZW6abzQyeEfkrMw7TpslIiJPx+AdERERUWcPpMxtt+qYeUfkxMy7C9eJiIg8EYN3RERERJ2kY+YdkVNYB+y8ObCCiIg8HIN3RERERJ09kDL3vGPmHZFjsWyWiIjUhME7IiIioq5m3rHlnSotW7YMAwcOxOjRo529KKrDslkiIlITBu+IiIiIOnsgZa7c48AKdVq6dCnS0tKQmprq7EVRHb1Vqaz1dSIiIk/Ev3REREREnT2QMkfv6ph6R+RQXpbIOYN3RESkAgzeEREREXWSztzzjrE7IseyzrazDuQRERF5IgbviIiIiDp7IFUfvGPTOyJn9bzz9uJHGiIi8mz8S0dERETU2QMpls0SOYV1th0z74iIyNMxeEdERETUxbLZOmbeETmU9UtOz8w7IiLycAzeEREREXWSpe2WicE7t/fPf/4TgwYNwsCBA3Hfffdxn7o460J1b06bJSIiD8fgHREREVEnaSyZd5xY4dby8vLwyiuvYOfOndi/f7+83Lp1q7MXi9qJZbNEROTpvJy9AERERETuSlff887ZS0JdZTAYUFVVJa/X1tYiOjqaG9WFdQv1a/I6JCIi8lTMvCMiIiLqpDlD4vDRkrG4a2pPbkM72rhxI+bNm4f4+HiZ7fjVV181+Z1XX30VycnJ8PX1xciRI7Fp06Z2339UVBQefPBBdO/eXT7GxRdfjF69etl4LciWAny8sP3Ri7Drz5fUZ8ASERF5KmbeEREREXVSYri//CL7Ki8vR0pKCm699VZcffXVTX7+2Wef4f7775cBvIkTJ+KNN97AZZddhrS0NBmQE0RAr7q6usn/XbNmDfz8/PDdd9/h9OnT8rr4vyJgOGXKFO5aFxYd7OvsRSAiInIIBu+IiIiIyKWJYJr4askLL7yA22+/HUuWLJHfv/jii1i9ejVee+01PP300/I20ceuJf/973/Ru3dvhIeHy+/nzJkje961FLwTQUDrQGBJSUl9ua34sjXLfdrjvt0B15/73/p5oDZ8/vP5b/08UBO1PPdr27l+DN4RERERkduqqamRgbmHH364we0zZ87E5s2b23UfiYmJ8ndFzzu9Xo/169fjzjvvbPH3RUDwiSeeaDaLz9/ffpmYa9euhZpx/bn/1YzPfz7/1crTn/sVFRXt+j0G74iIiIjIbeXn56Ourg4xMTENbhffZ2dnt+s+xo0bh9mzZ2P48OHQarW46KKLMH/+/BZ//5FHHsEDDzzQIPNOBABFwDA4OBj2OCsvPrxccsklMrioNlx/7n8+//n65/uf+t7/1fLeX2LO3m8Lg3dERERE5PYaDy0wmUwdGmTwt7/9TX61h4+Pj/xatmyZ/BLBQ0F8uLDnBwx737+r4/pz//P5z9e/Wqn5/c/T113fznXjtFkiIiIicluRkZHQ6XRNsuxyc3ObZOPZ2tKlS+VQjNTUVLs+DhEREakbg3dERERE5La8vb3lJNnGPXHE9xMmTLDrY4usu4EDB2L06NF2fRwiIiJSN5bNEhEREZFLKysrw/Hjx+u/P3XqFPbs2SOnw3bv3l32n1u4cCFGjRqF8ePH480330R6ejruuusuu2feiS/RryYkJMSuj0VERETqxeAdEREREbm0HTt2YPr06fXfW4ZFLFq0CO+++y4WLFiA8+fP48knn0RWVhYGDx6MlStXIikpyYlLTURERGQbDN4RERERkUubNm2aHEDRmrvvvlt+OVLjgRVERERE9sCed0REREREncCBFUREROQIDN4RERERERERERG5KAbviIiIiIg6gdNmiYiIyBEYvCMiIiIi6gSWzRIREZEjMHhHRERERERERETkojht1kEsE9JKSkrscv+1tbWoqKiQ96/X66E2XH/ufz7/+fpX4/sf3/vU8d5nOXZoa9oqOZ5l2qzBYJDf8zjPPvhep473upZw/3P/8/mvzte/Wl77Je08ztOYeCToEGfPnkViYqJjHoyIiIg8TkZGBhISEpy9GNQMHucRERGRPY/zGLxzEKPRiMzMTAQFBUGj0dglWiuCg2KHBwcHQ224/tz/fP7z9a/G9z++96njvU+cZy0tLUV8fDy0WnY8cUU8zrMvvtep472uJdz/3P98/qvz9a+W176pncd5LJt1ELETHHG2XDypPfmJ3RauP/c/n/98/asR3/s8/70vJCTE2YtAreBxnmPwvc7z3+taw/3P/c/nvzpf/2p47Ye04ziPp2+JiIiIiIiIiIhcFIN3RERERERERERELorBOw/h4+ODxx57TF6qEdef+5/Pf77+1fj+x/c+db/3kXrwta7u1zr3P/c/n/98/avx/U/t732NcWAFERERERERERGRi2LmHRERERERERERkYti8I6IiIiIiIiIiMhFMXhHRERERERERETkohi8IyIiIiIiIiIiclEM3nmIV199FcnJyfD19cXIkSOxadMmuLunn34ao0ePRlBQEKKjo3HFFVfgyJEjDX5n8eLF0Gg0Db7GjRvX4Heqq6tx7733IjIyEgEBAZg/fz7Onj0LV/f44483WbfY2Nj6n5tMJvk78fHx8PPzw7Rp03Dw4EGPWHehR48eTdZffC1dutQj9/3GjRsxb948uT/Funz11VcNfm6r/V1YWIiFCxciJCREfonrRUVFcNV1r62txR/+8AcMGTJErpP4nVtuuQWZmZkN7kNsj8bPh+uvv97l1709+95Wz3V3Xf/m3gfE13PPPecR+5+oPXic5xl/663xOI/HeWo5zlP7sR6P83icZysM3nmAzz77DPfffz/++Mc/Yvfu3Zg8eTIuu+wypKenw51t2LBBBmq2bt2KtWvXwmAwYObMmSgvL2/we7NmzUJWVlb918qVKxv8XGybFStW4NNPP8XPP/+MsrIyzJ07F3V1dXB1gwYNarBu+/fvr//Zs88+ixdeeAGvvPIKUlNTZWDvkksuQWlpqUesu1gn63UXzwHh2muv9ch9L57XKSkpcn82x1b7+8Ybb8SePXuwatUq+SWuiwMbV133iooK7Nq1C3/+85/l5fLly3H06FF5wNrYHXfc0eD58MYbbzT4uSuue3v2va2e6+66/tbrLb7+85//yAP2q6++2iP2P1FbeJznOX/rG+NxHo/z1HCcp/ZjPR7n8TjPZkzk9saMGWO66667GtzWv39/08MPP2zyJLm5uSbxlN2wYUP9bYsWLTJdfvnlLf6foqIik16vN3366af1t507d86k1WpNq1atMrmyxx57zJSSktLsz4xGoyk2Ntb097//vf62qqoqU0hIiOn11193+3Vvzm9+8xtTr1695Lp7+r4Xz/MVK1bYfH+npaXJ+966dWv972zZskXedvjwYZMrrntztm/fLn/vzJkz9bdNnTpVPkda4g7r3tL62+K57s7r35jYFjNmzGhwm6fsf6Lm8DjPM//W8zivIR7nqeM4T+3HejzO43FeVzDzzs3V1NRg586dMiPNmvh+8+bN8CTFxcXyMjw8vMHt69evl2W1ffv2lWdjcnNz638mto1IxbbePiIVe/DgwW6xfY4dOyaXV5REi7TwkydPyttPnTqF7OzsBuvl4+ODqVOn1q+Xu6974+f5hx9+iNtuu01m3Khh31uz1f7esmWLLCEYO3Zs/e+I8iNxmzttE/FeIJ4HoaGhDW7/6KOPZCmJyGR48MEHG5ytdvd17+pz3d3X3yInJwfff/89br/99iY/8+T9T+rF4zzP/lvP4zwFj/N4nKf2Yz0e5yl4nNc6rzZ+Ti4uPz9fpkrHxMQ0uF18Lz7sewpxouKBBx7ApEmT5AGZhSgPFmWUSUlJMsAh0q1nzJghD+ZEcENsA29vb4SFhbnd9hF/eN5//315sCreyP76179iwoQJsv+FZdmb2+9nzpyR19153RsTfTFEvwrR+0sN+74xW+1vcSk+ADUmbnOXbVJVVYWHH35YlkUEBwfX337TTTfJILcoMzlw4AAeeeQR7N27t77c2p3X3RbPdXdef2vvvfee7IN61VVXNbjdk/c/qRuP8zz3bz2P8y7gcR6P89R8rMfjvAt4nNc6Bu88hHU2kiXY1fg2d3bPPfdg3759sr+DtQULFtRfF0G9UaNGyQM8kZnR+MOdu20f8UZuIRq4jh8/Hr169ZJvapZmzZ3Z7+6w7o29/fbbcnuIs+lq2PctscX+bu733WWbiOwKkYFqNBpl83ZrIhvD+vnQp08f+ZwQvVNGjBjh1utuq+e6u66/NdHvThy8i+FMatn/RAKP8zzvbz2P8y7gcZ5C7cd5aj3W43HeBTzOax3LZt2cSBvW6XRNziaIkoLGWTruSkxV+uabb/DTTz8hISGh1d+Ni4uTB3WiDEEQZ2ZEKr6YPOTu20dMXxJBPLFulqmzre13T1l3kVm2bt06LFmyRLX73lb7W/yOyOJsLC8vz+W3iTiYu+6662TmhTjDan0mtjniIE6v1zd4Prjrutviue4J6y+mqIuJ4229F3j6/id14XGeev7W8ziPx3lqPs4TeKyn4HEej/NawuCdmxOlAiNHjqxPF7YQ34sSS3cmzpKIjDsxcejHH3+UadJtOX/+PDIyMuSbniC2jfgAZ719xGQikWrtbttHjIc/dOiQXDdLyrj1eok/6GJCr2W9PGXd33nnHZnuPmfOHNXue1vtb5G9KXqIbN++vf53tm3bJm9z5W1iOZgTH9ZEIDciIqLN/yPKy8X/szwf3HXdbfVc94T1F5kZYl3FtDo1739SFx7nqedvPY/zeJyn1uM8gcd6F/A4j8d5LerSuAtyCWLqkJg+9Pbbb8spO/fff78pICDAdPr0aZM7+/Wvfy2nLK1fv96UlZVV/1VRUSF/Xlpaavrd735n2rx5s+nUqVOmn376yTR+/HhTt27dTCUlJfX3IybxJiQkmNatW2fatWuXnFIoprgaDAaTKxPrJtb95MmTcmrS3LlzTUFBQfX7VUykEttn+fLlpv3795tuuOEGU1xcnEesu0VdXZ2pe/fupj/84Q8NbvfEfS/Waffu3fJLvDW/8MIL8rplypat9vesWbNMQ4cOldO3xNeQIUPkc8tV1722ttY0f/58uV579uxp8F5QXV0t///x48dNTzzxhCk1NVU+H77//ns5cXv48OEuv+5trb8tn+vuuP4WxcXFJn9/f9Nrr73W5P+7+/4naguP8zznb701HufxOE8tx3lqP9bjcR6P82yFwTsPsWzZMlNSUpLJ29vbNGLECNOGDRtM7k68sTf39c4778ifiyDezJkzTVFRUTJ4KYI8ixYtMqWnpze4n8rKStM999xjCg8PN/n5+ck38Ma/44oWLFgg/2iLdYuPjzddddVVpoMHD9b/3Gg0mh577DFTbGysycfHxzRlyhT5x94T1t1i9erVcp8fOXKkwe2euO/Fh5Lmnu9ivWy5v8+fP2+66aabZCBYfInrhYWFJlddd3GA1tJ7gfh/glhHsT3Eeov3wF69epnuu+8+ua6uvu5trb8tn+vuuP4Wb7zxhlyvoqKiJv/f3fc/UXvwOM8z/tZb43Eej/PUcpyn9mM9HufxOM9WNOKflvPyiIiIiIiIiIiIyFnY846IiIiIiIiIiMhFMXhHRERERERERETkohi8IyIiIiIiIiIiclEM3hEREREREREREbkoBu+IiIiIiIiIiIhcFIN3RERERERERERELorBOyIiIiIiIiIiIhfF4B0REREREREREZGLYvCOiIiIiIiIiIjIRTF4R0RERERERERE5KIYvCMicpCDBw/Cy8sLa9asscn9ffHFF/Dx8cHJkydtcn9ERERE1Dk8ziMie9KYTCaTXR+BiEhF3nvvPRQVFeE3v/lNk5/NmjULZWVl+PnnnxvcXlJSgtDQUIi34xEjRmDnzp1N/m95eTn69++Ps2fPyoBdaWmpDAQOHz4cvXv3loE8IiIiIrIfHucRkbMw846IyIb+8Ic/NJtZt3XrVqxevRr3339/k5/t2rVLBu78/PyQlpaGurq6Jr/z9NNPIzc3V14fPHgw9Ho9NBqNvL8vv/xSnu0lIiIiIvvhcR4ROQuDd0RENnL69Gnk5ORgzJgxTX722muvyey6efPmNRu8E6666ipUVVXh+PHjDX5+6tQpPP/887jyyivl9yNHjqz/2dVXXw1/f395/0RERERkHzzOIyJnYvCOiMgGrrjiCiQnJ8vrjz/+uMyKE1+///3vYTAYsHz5clx00UWy5LUxS5nsbbfdJi/379/f4Oe/+93vEB4eLstuBVFaaxEUFITJkyfj888/l9l7RERERGRbPM4jImfzcvYCEBF5gjvvvFOWu3733Xd45ZVXEBISIm8XWXgis070uhs7dmyz/1f8PCEhAVOmTIGvry8OHDiAa665Rv7shx9+wIoVK/D+++/j0KFDTTLvhPHjx8uSXFE6K0pqiYiIiIjHeUTkOZh5R0RkA7Nnz4ZWq0VYWBiWLl2Km2++WX717du3vh9dr169mvw/EdQ7evSozKYTAygGDRpUn3knMvbE4Itx48bJ+xIZeqLX3ZAhQxrch+V+2feOiIiIyPZ4nEdEzsbMOyIiGxEZdGL6a2N5eXnyUpS+NrZnzx4Yjcb6Uthhw4Zh48aN8vqrr74qB1hs27ZNluCK+xfBvcaltxEREfLSMtCCiIiIiGyLx3lE5EzMvCMisoH8/HycPXu2QT86CxF4E5rrSWfpd2cphRXBuxMnTiAjI0P2zlu8eDFGjx6N9PR0+RjN3b/lfi2PQ0RERES2w+M8InI2Bu+IiGzAEoRrLrgWFRUlLwsLC1ucNGudeScy8RYsWCB76D399NMN7r9xvzuhoKCgweMQERERke3wOI+InI1ls0RENrB79+4Wg3eWIRLHjx9v9mAwJiYG8fHx8vuUlBSZQbdlyxY899xz8mfNBfmsWe6XwyqIiIiIbI/HeUTkbAzeERHZwMmTJ+VlcnJyk5+JPnjBwcHYvn17g9srKytx+PBhXHrppfW3BQUF4dlnn0VVVZUcVmEd5NPpdDK419jWrVsRGRmJgQMHcl8SERER2RiP84jI2Ri8IyKygZ49e8rL++67DxMmTJCBthtuuEFOoBXXr7rqKnz99deorq6uHzghhlWI0tjG2XQPPvhgk/sXmXcDBgyAn59fg9tLS0uxadMmLFq0iD3viIiIiOyAx3lE5GzseUdEZAMiaLdw4UJ8+eWXMpD20EMPycCdxa9//WvZ8+67776rv621Ulhr586dQ05OTrO/Jx6voqJC3j8RERER2R6P84jI2TSm5sYfEhGRzc2aNQvl5eUyU84WxNu3COiJs8EiiEdEREREzsHjPCKyJ2beERE5yPPPPy8HUaxZs8Ym97d8+XKkpaXJHnlERERE5Dw8ziMie2LmHRERERERERERkYti5h0REREREREREZGLYvCOiIiIiIiIiIjIRTF4R0RERERERERE5KIYvCMiIiIiIiIiInJRDN4RERERERERERG5KAbviIiIiIiIiIiIXBSDd0RERERERERERC6KwTsiIiIiIiIiIiIXxeAdERERERERERGRi2LwjoiIiIiIiIiICK7p/wE1UfoyAMiXzgAAAABJRU5ErkJggg==", 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" + "
" ] }, "metadata": {}, @@ -989,55 +1000,124 @@ } ], "source": [ - "plt.plot(time_py,e_py,label='python')\n", - "plt.plot(time_c,e_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$e (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\n", + "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", - "plt.plot(time_py,x_py,label='python')\n", - "plt.plot(time_c,x_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$x (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\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", - "plt.plot(time_py,meanan_py,label='python')\n", - "plt.plot(time_c,meanan_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$l (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\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", - "plt.plot(time_py,phi_py,label='python')\n", - "plt.plot(time_c,phi_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$\\\\phi (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\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", - "plt.plot(time_py,phidot_py,label='python')\n", - "plt.plot(time_c,phidot_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$\\\\dot{\\\\phi} (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\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", - "plt.plot(time_py,r_py,label='python')\n", - "plt.plot(time_c,r_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$r (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\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", - "plt.plot(time_py,rdot_py,label='python')\n", - "plt.plot(time_c,rdot_c,ls='--',label='C')\n", - "plt.xlabel('$t (M)$',fontsize=13)\n", - "plt.ylabel('$\\\\dot{r} (t)$',fontsize=13)\n", - "plt.legend(), plt.grid()\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": {