he2851/Masterprojekt
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
af8ac02e5bff9d8cea6635144b3b8ef349050745
Masterprojekt/GHA_triaxial/gha2_num.py
Tammo.Weber dd5cf2d6a8 merge
2026-02-05 21:43:26 +01:00

548 lines
18 KiB
Python
Raw Blame History

import numpy as np
from ellipsoide import EllipsoidTriaxial
import runge_kutta as rk
import GHA_triaxial.numeric_examples_karney as ne_karney
import GHA_triaxial.numeric_examples_panou as ne_panou
import winkelumrechnungen as wu
from typing import Tuple
from numpy.typing import NDArray
import ausgaben as aus
from utils_angle import cot, wrap_to_pi
def arccot(x):
x = np.asarray(x)
a = np.arctan2(1.0, x)
return np.where(x < 0.0, a - np.pi, a)
def normalize_alpha_0_pi(alpha):
if alpha < 0.0:
alpha += np.pi
return alpha
def sph_azimuth(beta1, lam1, beta2, lam2):
dlam = wrap_to_pi(lam2 - lam1)
y = np.sin(dlam) * np.cos(beta2)
x = np.cos(beta1) * np.sin(beta2) - np.sin(beta1) * np.cos(beta2) * np.cos(dlam)
a = np.arctan2(y, x)
if a < 0:
a += 2 * np.pi
return a
# Panou 2013
def gha2_num(
ell: EllipsoidTriaxial,
beta_1: float,
lamb_1: float,
beta_2: float,
lamb_2: float,
n: int = 16000,
epsilon: float = 10 ** -12,
iter_max: int = 30,
all_points: bool = False,
) -> Tuple[float, float, float] | Tuple[float, float, float, NDArray, NDArray]:
def BETA_LAMBDA(beta, lamb):
BETA = (ell.ay ** 2 * np.sin(beta) ** 2 + ell.b ** 2 * np.cos(beta) ** 2) / (
ell.Ex ** 2 - ell.Ey ** 2 * np.sin(beta) ** 2
)
LAMBDA = (ell.ax ** 2 * np.sin(lamb) ** 2 + ell.ay ** 2 * np.cos(lamb) ** 2) / (
ell.Ex ** 2 - ell.Ee ** 2 * np.cos(lamb) ** 2
)
BETA_ = (ell.ax ** 2 * ell.Ey ** 2 * np.sin(2 * beta)) / (
ell.Ex ** 2 - ell.Ey ** 2 * np.sin(beta) ** 2
) ** 2
LAMBDA_ = -(ell.b ** 2 * ell.Ee ** 2 * np.sin(2 * lamb)) / (
ell.Ex ** 2 - ell.Ee ** 2 * np.cos(lamb) ** 2
) ** 2
BETA__ = (
(2 * ell.ax ** 2 * ell.Ey ** 4 * np.sin(2 * beta) ** 2)
/ (ell.Ex ** 2 - ell.Ey ** 2 * np.sin(beta) ** 2) ** 3
+ (2 * ell.ax ** 2 * ell.Ey ** 2 * np.cos(2 * beta))
/ (ell.Ex ** 2 - ell.Ey ** 2 * np.sin(beta) ** 2) ** 2
)
LAMBDA__ = (
(2 * ell.b ** 2 * ell.Ee ** 4 * np.sin(2 * lamb) ** 2)
/ (ell.Ex ** 2 - ell.Ee ** 2 * np.cos(lamb) ** 2) ** 3
- (2 * ell.b ** 2 * ell.Ee ** 2 * np.sin(2 * lamb))
/ (ell.Ex ** 2 - ell.Ee ** 2 * np.cos(lamb) ** 2) ** 2
)
E = BETA * (ell.Ey ** 2 * np.cos(beta) ** 2 + ell.Ee ** 2 * np.sin(lamb) ** 2)
G = LAMBDA * (ell.Ey ** 2 * np.cos(beta) ** 2 + ell.Ee ** 2 * np.sin(lamb) ** 2)
E_beta = (
BETA_ * (ell.Ey ** 2 * np.cos(beta) ** 2 + ell.Ee ** 2 * np.sin(lamb) ** 2)
- BETA * ell.Ey ** 2 * np.sin(2 * beta)
)
E_lamb = BETA * ell.Ee ** 2 * np.sin(2 * lamb)
G_beta = -LAMBDA * ell.Ey ** 2 * np.sin(2 * beta)
G_lamb = (
LAMBDA_ * (ell.Ey ** 2 * np.cos(beta) ** 2 + ell.Ee ** 2 * np.sin(lamb) ** 2)
+ LAMBDA * ell.Ee ** 2 * np.sin(2 * lamb)
)
E_beta_beta = (
BETA__ * (ell.Ey ** 2 * np.cos(beta) ** 2 + ell.Ee ** 2 * np.sin(lamb) ** 2)
- 2 * BETA_ * ell.Ey ** 2 * np.sin(2 * beta)
- 2 * BETA * ell.Ey ** 2 * np.cos(2 * beta)
)
E_beta_lamb = BETA_ * ell.Ee ** 2 * np.sin(2 * lamb)
E_lamb_lamb = 2 * BETA * ell.Ee ** 2 * np.cos(2 * lamb)
G_beta_beta = -2 * LAMBDA * ell.Ey ** 2 * np.cos(2 * beta)
G_beta_lamb = -LAMBDA_ * ell.Ey ** 2 * np.sin(2 * beta)
G_lamb_lamb = (
LAMBDA__ * (ell.Ey ** 2 * np.cos(beta) ** 2 + ell.Ee ** 2 * np.sin(lamb) ** 2)
+ 2 * LAMBDA_ * ell.Ee ** 2 * np.sin(2 * lamb)
+ 2 * LAMBDA * ell.Ee ** 2 * np.cos(2 * lamb)
)
return (
BETA,
LAMBDA,
E,
G,
BETA_,
LAMBDA_,
BETA__,
LAMBDA__,
E_beta,
E_lamb,
G_beta,
G_lamb,
E_beta_beta,
E_beta_lamb,
E_lamb_lamb,
G_beta_beta,
G_beta_lamb,
G_lamb_lamb,
)
def p_coef(beta, lamb):
(
BETA,
LAMBDA,
E,
G,
BETA_,
LAMBDA_,
BETA__,
LAMBDA__,
E_beta,
E_lamb,
G_beta,
G_lamb,
E_beta_beta,
E_beta_lamb,
E_lamb_lamb,
G_beta_beta,
G_beta_lamb,
G_lamb_lamb,
) = BETA_LAMBDA(beta, lamb)
p_3 = -0.5 * (E_lamb / G)
p_2 = (G_beta / G) - 0.5 * (E_beta / E)
p_1 = 0.5 * (G_lamb / G) - (E_lamb / E)
p_0 = 0.5 * (G_beta / E)
p_33 = -0.5 * ((E_beta_lamb * G - E_lamb * G_beta) / (G**2))
p_22 = ((G * G_beta_beta - G_beta * G_beta) / (G**2)) - 0.5 * (
(E * E_beta_beta - E_beta * E_beta) / (E**2)
)
p_11 = 0.5 * ((G * G_beta_lamb - G_beta * G_lamb) / (G**2)) - (
(E * E_beta_lamb - E_beta * E_lamb) / (E**2)
)
p_00 = 0.5 * ((E * G_beta_beta - E_beta * G_beta) / (E**2))
return (BETA, LAMBDA, E, G, p_3, p_2, p_1, p_0, p_33, p_22, p_11, p_00)
def q_coef(beta, lamb):
(
BETA,
LAMBDA,
E,
G,
BETA_,
LAMBDA_,
BETA__,
LAMBDA__,
E_beta,
E_lamb,
G_beta,
G_lamb,
E_beta_beta,
E_beta_lamb,
E_lamb_lamb,
G_beta_beta,
G_beta_lamb,
G_lamb_lamb,
) = BETA_LAMBDA(beta, lamb)
q_3 = -0.5 * (G_beta / E)
q_2 = (E_lamb / E) - 0.5 * (G_lamb / G)
q_1 = 0.5 * (E_beta / E) - (G_beta / G)
q_0 = 0.5 * (E_lamb / G)
q_33 = -0.5 * ((E * G_beta_lamb - E_lamb * G_lamb) / (E**2))
q_22 = ((E * E_lamb_lamb - E_lamb * E_lamb) / (E**2)) - 0.5 * (
(G * G_lamb_lamb - G_lamb * G_lamb) / (G**2)
)
q_11 = 0.5 * ((E * E_beta_lamb - E_beta * E_lamb) / (E**2)) - (
(G * G_beta_lamb - G_beta * G_lamb) / (G**2)
)
q_00 = 0.5 * ((E_lamb_lamb * G - E_lamb * G_lamb) / (G**2))
return (BETA, LAMBDA, E, G, q_3, q_2, q_1, q_0, q_33, q_22, q_11, q_00)
def rk4_last(f, t0, y0, dt, N):
h = dt / N
t = t0
y = np.array(y0, dtype=float, copy=True)
for _ in range(N):
k1 = f(t, y)
k2 = f(t + 0.5 * h, y + 0.5 * h * k1)
k3 = f(t + 0.5 * h, y + 0.5 * h * k2)
k4 = f(t + h, y + h * k3)
y = y + (h / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4)
t = t + h
return t, y
def rk4_last_with_integral(f, t0, y0, dt, N, integrand_at):
h = dt / N
habs = abs(h)
t = t0
y = np.array(y0, dtype=float, copy=True)
if N % 2 == 0:
# Simpson streaming
f0 = integrand_at(t, y)
odd_sum = 0.0
even_sum = 0.0
for i in range(1, N + 1):
k1 = f(t, y)
k2 = f(t + 0.5 * h, y + 0.5 * h * k1)
k3 = f(t + 0.5 * h, y + 0.5 * h * k2)
k4 = f(t + h, y + h * k3)
y = y + (h / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4)
t = t + h
fi = integrand_at(t, y)
if i == N:
fN = fi
elif i % 2 == 1:
odd_sum += fi
else:
even_sum += fi
S = f0 + fN + 4.0 * odd_sum + 2.0 * even_sum
s = (habs / 3.0) * S
return t, y, s
# Trapez streaming
f_prev = integrand_at(t, y)
acc = 0.0
for _ in range(N):
k1 = f(t, y)
k2 = f(t + 0.5 * h, y + 0.5 * h * k1)
k3 = f(t + 0.5 * h, y + 0.5 * h * k2)
k4 = f(t + h, y + h * k3)
y = y + (h / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4)
t = t + h
f_cur = integrand_at(t, y)
acc += 0.5 * (f_prev + f_cur)
f_prev = f_cur
s = habs * acc
return t, y, s
def integrand_lambda(lamb, y):
beta = y[0]
beta_p = y[1]
(_, _, E, G, *_) = BETA_LAMBDA(beta, lamb)
return np.sqrt(E * beta_p**2 + G)
def integrand_beta(beta, y):
lamb = y[0]
lamb_p = y[1]
(_, _, E, G, *_) = BETA_LAMBDA(beta, lamb)
return np.sqrt(E + G * lamb_p**2)
if lamb_1 != lamb_2:
N = n
dlamb = lamb_2 - lamb_1
def buildODElamb():
def ODE(lamb, v):
beta, beta_p, X3, X4 = v
(_, _, _, _, p_3, p_2, p_1, p_0, p_33, p_22, p_11, p_00) = p_coef(beta, lamb)
dbeta = beta_p
dbeta_p = p_3 * beta_p**3 + p_2 * beta_p**2 + p_1 * beta_p + p_0
dX3 = X4
dX4 = (p_33 * beta_p**3 + p_22 * beta_p**2 + p_11 * beta_p + p_00) * X3 + (
3 * p_3 * beta_p**2 + 2 * p_2 * beta_p + p_1
) * X4
return np.array([dbeta, dbeta_p, dX3, dX4], dtype=float)
return ODE
ode_lamb = buildODElamb()
alpha0_sph = sph_azimuth(beta_1, lamb_1, beta_2, lamb_2)
(_, _, E1, G1, *_) = BETA_LAMBDA(beta_1, lamb_1)
beta_p0_sph = np.sqrt(G1 / E1) * cot(alpha0_sph) if abs(dlamb) >= 1e-15 else 0.0
N_newton = min(N, 4000)
def solve_newton(beta_p0_init: float):
beta_p0 = float(beta_p0_init)
for _ in range(iter_max):
startwerte = np.array([beta_1, beta_p0, 0.0, 1.0], dtype=float)
_, y_end = rk4_last(ode_lamb, lamb_1, startwerte, dlamb, N_newton)
beta_end, _, X3_end, _ = y_end
delta = beta_end - beta_2
if abs(delta) < epsilon:
return True, beta_p0
if abs(X3_end) < 1e-20:
return False, None
step = delta / X3_end
max_step = 0.5
if abs(step) > max_step:
step = np.sign(step) * max_step
beta_p0 = beta_p0 - step
return False, None
ok, beta_p0_sol = solve_newton(beta_p0_sph)
if not ok:
candidates = [-beta_p0_sph, 0.5 * beta_p0_sph, 2.0 * beta_p0_sph]
N_quick = min(N, 2000)
best = None
for g in candidates:
ok_g, beta_p0_sol_g = solve_newton(g)
if not ok_g:
continue
startwerte_g = np.array([beta_1, beta_p0_sol_g, 0.0, 1.0], dtype=float)
_, _, s_quick = rk4_last_with_integral(
ode_lamb, lamb_1, startwerte_g, dlamb, N_quick, integrand_lambda
)
if (best is None) or (s_quick < best[0]):
best = (s_quick, beta_p0_sol_g)
if best is None:
raise RuntimeError("Keine Startwert-Variante konvergiert.")
beta_p0_sol = best[1]
beta_0 = beta_p0_sol
startwerte_final = np.array([beta_1, beta_0, 0.0, 1.0], dtype=float)
if all_points:
lamb_list, states = rk.rk4(ode_lamb, lamb_1, startwerte_final, dlamb, N, False)
lamb_arr = np.array(lamb_list, dtype=float)
beta_arr = np.array([st[0] for st in states], dtype=float)
beta_p_arr = np.array([st[1] for st in states], dtype=float)
(_, _, E1, G1, *_) = BETA_LAMBDA(beta_arr[0], lamb_arr[0])
(_, _, E2, G2, *_) = BETA_LAMBDA(beta_arr[-1], lamb_arr[-1])
alpha_1 = arccot(np.sqrt(E1 / G1) * beta_p_arr[0])
alpha_2 = arccot(np.sqrt(E2 / G2) * beta_p_arr[-1])
alpha_1 = normalize_alpha_0_pi(float(alpha_1))
alpha_2 = normalize_alpha_0_pi(float(alpha_2))
# Distanz s aus Arrays (Simpson/Trapz)
integrand = np.zeros(N + 1, dtype=float)
for i in range(N + 1):
(_, _, Ei, Gi, *_) = BETA_LAMBDA(beta_arr[i], lamb_arr[i])
integrand[i] = np.sqrt(Ei * beta_p_arr[i] ** 2 + Gi)
h = abs(dlamb) / N
if N % 2 == 0:
S = integrand[0] + integrand[-1] + 4.0 * np.sum(integrand[1:-1:2]) + 2.0 * np.sum(integrand[2:-1:2])
s = h / 3.0 * S
else:
s = np.trapz(integrand, dx=h)
return alpha_1, alpha_2, s, beta_arr, lamb_arr
_, y_end, s = rk4_last_with_integral(ode_lamb, lamb_1, startwerte_final, dlamb, N, integrand_lambda)
beta_end, beta_p_end, _, _ = y_end
(_, _, E1, G1, *_) = BETA_LAMBDA(beta_1, lamb_1)
(_, _, E2, G2, *_) = BETA_LAMBDA(beta_2, lamb_2)
alpha_1 = arccot(np.sqrt(E1 / G1) * beta_0)
alpha_2 = arccot(np.sqrt(E2 / G2) * beta_p_end)
alpha_1 = normalize_alpha_0_pi(float(alpha_1))
alpha_2 = normalize_alpha_0_pi(float(alpha_2))
return alpha_1, alpha_2, s
N = n
dbeta = beta_2 - beta_1
if abs(dbeta) < 1e-15:
if all_points:
return 0.0, 0.0, 0.0, np.array([]), np.array([])
return 0.0, 0.0, 0.0
# ODE-System (lambda, lambda', Y3, Y4) in Abhängigkeit von beta
def buildODEbeta():
def ODE(beta, v):
lamb, lamb_p, Y3, Y4 = v
(_, _, _, _, q_3, q_2, q_1, q_0, q_33, q_22, q_11, q_00) = q_coef(beta, lamb)
dlamb = lamb_p
dlamb_p = q_3 * lamb_p**3 + q_2 * lamb_p**2 + q_1 * lamb_p + q_0
dY3 = Y4
dY4 = (q_33 * lamb_p**3 + q_22 * lamb_p**2 + q_11 * lamb_p + q_00) * Y3 + (
3 * q_3 * lamb_p**2 + 2 * q_2 * lamb_p + q_1
) * Y4
return np.array([dlamb, dlamb_p, dY3, dY4], dtype=float)
return ODE
ode_beta = buildODEbeta()
# Newton auf lambda'_0
lamb_0 = 0.0
for _ in range(iter_max):
startwerte = np.array([lamb_1, lamb_0, 0.0, 1.0], dtype=float)
beta_list, states = rk.rk4(ode_beta, beta_1, startwerte, dbeta, N, False)
lamb_end, lamb_p_end, Y3_end, _ = states[-1]
delta = lamb_end - lamb_2
if abs(delta) < epsilon:
break
if abs(Y3_end) < 1e-20:
raise RuntimeError("Abbruch (Ableitung ~ 0).")
step = delta / Y3_end
max_step = 1.0
if abs(step) > max_step:
step = np.sign(step) * max_step
lamb_0 = lamb_0 - step
startwerte_final = np.array([lamb_1, lamb_0, 0.0, 1.0], dtype=float)
if all_points:
beta_list, states = rk.rk4(ode_beta, beta_1, startwerte_final, dbeta, N, False)
beta_arr = np.array(beta_list, dtype=float)
lamb_arr = np.array([st[0] for st in states], dtype=float)
lamb_p_arr = np.array([st[1] for st in states], dtype=float)
# Azimute
(BETA1, LAMBDA1, _, _, *_) = BETA_LAMBDA(beta_arr[0], lamb_arr[0])
(BETA2, LAMBDA2, _, _, *_) = BETA_LAMBDA(beta_arr[-1], lamb_arr[-1])
alpha_1 = (np.pi / 2.0) - arccot(np.sqrt(LAMBDA1 / BETA1) * lamb_p_arr[0])
alpha_2 = (np.pi / 2.0) - arccot(np.sqrt(LAMBDA2 / BETA2) * lamb_p_arr[-1])
# optionaler Quadrantenfix (robust)
alpha_1 = normalize_alpha_0_pi(float(alpha_1))
alpha_2 = normalize_alpha_0_pi(float(alpha_2))
# Distanz
integrand = np.zeros(N + 1, dtype=float)
for i in range(N + 1):
(_, _, Ei, Gi, *_) = BETA_LAMBDA(beta_arr[i], lamb_arr[i])
integrand[i] = np.sqrt(Ei + Gi * lamb_p_arr[i] ** 2)
h = abs(dbeta) / N
if N % 2 == 0:
S = integrand[0] + integrand[-1] + 4.0 * np.sum(integrand[1:-1:2]) + 2.0 * np.sum(integrand[2:-1:2])
s = h / 3.0 * S
else:
s = np.trapz(integrand, dx=h)
return alpha_1, alpha_2, s, beta_arr, lamb_arr
# all_points == False: streaming Integral
_, y_end, s = rk4_last_with_integral(ode_beta, beta_1, startwerte_final, dbeta, N, integrand_beta)
lamb_end, lamb_p_end, _, _ = y_end
(BETA1, LAMBDA1, _, _, *_) = BETA_LAMBDA(beta_1, lamb_1)
(BETA2, LAMBDA2, _, _, *_) = BETA_LAMBDA(beta_2, lamb_2)
alpha_1 = (np.pi / 2.0) - arccot(np.sqrt(LAMBDA1 / BETA1) * lamb_0)
alpha_2 = (np.pi / 2.0) - arccot(np.sqrt(LAMBDA2 / BETA2) * lamb_p_end)
alpha_1 = normalize_alpha_0_pi(float(alpha_1))
alpha_2 = normalize_alpha_0_pi(float(alpha_2))
return alpha_1, alpha_2, s
if __name__ == "__main__":
ell = EllipsoidTriaxial.init_name("BursaSima1980round")
beta1 = np.deg2rad(75)
lamb1 = np.deg2rad(-90)
beta2 = np.deg2rad(75)
lamb2 = np.deg2rad(66)
a1, a2, s = gha2_num(ell, beta1, lamb1, beta2, lamb2, n=5000)
print(aus.gms("a1", a1, 4))
# # print(aus.gms("a2", a2, 4))
# # print(s)
# cart1 = ell.para2cart(0, 0)
# cart2 = ell.para2cart(0.4, 1.4)
# beta1, lamb1 = ell.cart2ell(cart1)
# beta2, lamb2 = ell.cart2ell(cart2)
#
# a1, a2, s = gha2_num(ell, beta1, lamb1, beta2, lamb2, n=5000)
# print(s)
# ell = EllipsoidTriaxial.init_name("BursaSima1980round")
# diffs_panou = []
# examples_panou = ne_panou.get_random_examples(4)
# for example in examples_panou:
# beta0, lamb0, beta1, lamb1, _, alpha0, alpha1, s = example
# P0 = ell.ell2cart(beta0, lamb0)
# try:
# alpha0_num, alpha1_num, s_num = gha2_num(ell, beta0, lamb0, beta1, lamb1, n=4000, iter_max=10)
# diffs_panou.append(
# (wu.rad2deg(abs(alpha0 - alpha0_num)), wu.rad2deg(abs(alpha1 - alpha1_num)), abs(s - s_num)))
# except:
# print(f"Fehler für {beta0}, {lamb0}, {beta1}, {lamb1}")
# diffs_panou = np.array(diffs_panou)
# print(diffs_panou)
#
# ell = EllipsoidTriaxial.init_name("KarneyTest2024")
# diffs_karney = []
# # examples_karney = ne_karney.get_examples((30500, 40500))
# examples_karney = ne_karney.get_random_examples(2)
# for example in examples_karney:
# beta0, lamb0, alpha0, beta1, lamb1, alpha1, s = example
#
# try:
# alpha0_num, alpha1_num, s_num = gha2_num(ell, beta0, lamb0, beta1, lamb1, n=4000, iter_max=10)
# diffs_karney.append((wu.rad2deg(abs(alpha0-alpha0_num)), wu.rad2deg(abs(alpha1-alpha1_num)), abs(s-s_num)))
# except:
# print(f"Fehler für {beta0}, {lamb0}, {beta1}, {lamb1}")
# diffs_karney = np.array(diffs_karney)
# print(diffs_karney)
pass
Reference in New Issue View Git Blame Copy Permalink