Umbenennung, Umstrukturierung, Doc-Strings

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2026-02-04 22:59:07 +01:00
parent 96e489a116
commit 4e2491d967
25 changed files with 3294 additions and 1690 deletions

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import numpy as np
from ellipsoide import EllipsoidTriaxial
from panou import louville_constant, func_sigma_ell, gha1_ana
import plotly.graph_objects as go
import winkelumrechnungen as wu
from numpy import sin, cos, arccos
def Bogenlaenge(P1: NDArray, P2: NDArray) -> float:
"""
Berechnung der mittleren Bogenlänge zwischen zwei kartesischen Punkten
:param P1: kartesische Koordinate Punkt 1
:param P2: kartesische Koordinate Punkt 2
:return: Bogenlänge s
"""
R1 = np.linalg.norm(P1)
R2 = np.linalg.norm(P2)
R = 0.5 * (R1 + R2)
if P1 @ P2 / (R1 * R2) > 1:
s = np.linalg.norm(P1 - P2)
else:
theta = arccos(P1 @ P2 / (R1 * R2))
s = float(R * theta)
return s
def gha1_approx2(ell: EllipsoidTriaxial, p0: np.ndarray, alpha0: float, s: float, ds: float, all_points: bool = False) -> Tuple[NDArray, float] | Tuple[NDArray, float, NDArray]:
"""
Berechung einer Näherungslösung der ersten Hauptaufgabe
:param ell: Ellipsoid
:param p0: Anfangspunkt
:param alpha0: Azimut im Anfangspunkt
:param s: Strecke bis zum Endpunkt
:param ds: Länge einzelner Streckenelemente
:param all_points: Ausgabe aller Punkte als Array?
:return: Endpunkt, Azimut im Endpunkt, optional alle Punkte
"""
l0 = louville_constant(ell, p0, alpha0)
points = [p0]
alphas = [alpha0]
s_curr = 0.0
while s_curr < s:
ds_target = min(ds, s - s_curr)
if ds_target < 1e-8:
break
p1 = points[-1]
alpha1 = alphas[-1]
alpha1_mid = alphas[-1]
p2 = points[-1]
alpha2 = alphas[-1]
i = 0
while i < 2:
i += 1
sigma = func_sigma_ell(ell, p1, alpha1_mid)
p2_new = p1 + ds_target * sigma
p2_new = ell.point_onto_ellipsoid(p2_new)
p2 = p2_new
j = 0
while j < 2:
j += 1
dalpha = 1e-6
l2 = louville_constant(ell, p2, alpha2)
dl_dalpha = (louville_constant(ell, p2, alpha2 + dalpha) - l2) / dalpha
alpha2_new = alpha2 + (l0 - l2) / dl_dalpha
alpha2 = alpha2_new
alpha1_mid = (alpha1 + alpha2) / 2
points.append(p2)
alphas.append(alpha2)
ds_actual = np.linalg.norm(p2 - p1)
s_curr += ds_actual
if s_curr > 10000000:
pass
if all_points:
return points[-1], alphas[-1], np.array(points)
else:
return points[-1], alphas[-1]
def show_points(points: NDArray, p0: NDArray, p1: NDArray):
"""
Anzeigen der Punkte
:param points: Array aller approximierten Punkte
:param p0: Startpunkt
:param p1: wahrer Endpunkt
"""
fig = go.Figure()
fig.add_scatter3d(x=points[:, 0], y=points[:, 1], z=points[:, 2],
mode='lines', line=dict(color="red", width=3), name="Approx")
fig.add_scatter3d(x=[p0[0]], y=[p0[1]], z=[p0[2]],
mode='markers', marker=dict(color="green"), name="P0")
fig.add_scatter3d(x=[p1[0]], y=[p1[1]], z=[p1[2]],
mode='markers', marker=dict(color="green"), name="P1")
fig.update_layout(
scene=dict(xaxis_title='X [km]',
yaxis_title='Y [km]',
zaxis_title='Z [km]',
aspectmode='data'),
title="CHAMP")
fig.show()
if __name__ == '__main__':
ell = EllipsoidTriaxial.init_name("BursaSima1980round")
P0 = ell.para2cart(0.2, 0.3)
alpha0 = wu.deg2rad(35)
s = 13000000
P1_app, alpha1_app, points = gha1_approx2(ell, P0, alpha0, s, ds=10000, all_points=True)
P1_ana, alpha1_ana = gha1_ana(ell, P0, alpha0, s, maxM=60, maxPartCircum=16)
show_points(points, P0, P1_ana)
print(np.linalg.norm(P1_app - P1_ana))

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GHA_triaxial/gha1_ana.py Normal file
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from math import comb
from typing import Tuple
import numpy as np
from numpy import sin, cos, arctan2
from numpy._typing import NDArray
from scipy.special import factorial as fact
from ellipsoide import EllipsoidTriaxial
from GHA_triaxial.utils import pq_para
def gha1_ana_step(ell: EllipsoidTriaxial, point: NDArray, alpha0: float, s: float, maxM: int) -> Tuple[NDArray, float]:
"""
Panou, Korakitits 2020, 5ff.
:param ell: Ellipsoid
:param point: Punkt in kartesischen Koordinaten
:param alpha0: Azimut im Startpunkt
:param s: Strecke
:param maxM: maximale Ordnung
:return: Zwischenpunkt, Azimut im Zwischenpunkt
"""
x, y, z = point
# S. 6
x_m = [x]
y_m = [y]
z_m = [z]
p, q = pq_para(ell, point)
# 48-50
x_m.append(p[0] * sin(alpha0) + q[0] * cos(alpha0))
y_m.append(p[1] * sin(alpha0) + q[1] * cos(alpha0))
z_m.append(p[2] * sin(alpha0) + q[2] * cos(alpha0))
# 34
H_ = lambda p: np.sum([comb(p, p - i) * (x_m[p - i] * x_m[i] +
1 / (1 - ell.ee ** 2) ** 2 * y_m[p - i] * y_m[i] +
1 / (1 - ell.ex ** 2) ** 2 * z_m[p - i] * z_m[i])
for i in range(0, p + 1)])
# 35
h_ = lambda q: np.sum([comb(q, q-j) * (x_m[q-j+1] * x_m[j+1] +
1 / (1 - ell.ee ** 2) * y_m[q-j+1] * y_m[j+1] +
1 / (1 - ell.ex ** 2) * z_m[q-j+1] * z_m[j+1])
for j in range(0, q+1)])
# 31
hH_ = lambda t: 1/H_(0) * (h_(t) - np.sum([comb(t, l-1) * H_(t+1-l) * hH_t[l-1] for l in range(1, t+1)]))
# 28-30
x_ = lambda m: - np.sum([comb(m-2, k) * hH_t[m-2-k] * x_m[k] for k in range(0, m-2+1)])
y_ = lambda m: -1 / (1-ell.ee**2) * np.sum([comb(m-2, k) * hH_t[m-2-k] * y_m[k] for k in range(0, m-2+1)])
z_ = lambda m: -1 / (1-ell.ex**2) * np.sum([comb(m-2, k) * hH_t[m-2-k] * z_m[k] for k in range(0, m-2+1)])
hH_t = []
a_m = []
b_m = []
c_m = []
for m in range(0, maxM+1):
if m >= 2:
hH_t.append(hH_(m-2))
x_m.append(x_(m))
y_m.append(y_(m))
z_m.append(z_(m))
fact_m = fact(m)
# 22-24
a_m.append(x_m[m] / fact_m)
b_m.append(y_m[m] / fact_m)
c_m.append(z_m[m] / fact_m)
# 19-21
x_s = 0
for a in reversed(a_m):
x_s = x_s * s + a
y_s = 0
for b in reversed(b_m):
y_s = y_s * s + b
z_s = 0
for c in reversed(c_m):
z_s = z_s * s + c
p1 = np.array([x_s, y_s, z_s])
p_s, q_s = pq_para(ell, p1)
# 57-59
dx_s = 0
for i, a in reversed(list(enumerate(a_m[1:], start=1))):
dx_s = dx_s * s + i * a
dy_s = 0
for i, b in reversed(list(enumerate(b_m[1:], start=1))):
dy_s = dy_s * s + i * b
dz_s = 0
for i, c in reversed(list(enumerate(c_m[1:], start=1))):
dz_s = dz_s * s + i * c
# 52-53
sigma = np.array([dx_s, dy_s, dz_s])
P = float(p_s @ sigma)
Q = float(q_s @ sigma)
# 51
alpha1 = arctan2(P, Q)
if alpha1 < 0:
alpha1 += 2 * np.pi
return p1, alpha1
def gha1_ana(ell: EllipsoidTriaxial, point: NDArray, alpha0: float, s: float, maxM: int, maxPartCircum: int = 16) -> Tuple[NDArray, float]:
"""
:param ell: Ellipsoid
:param point: Punkt in kartesischen Koordinaten
:param alpha0: Azimut im Startpunkt
:param s: Strecke
:param maxM: maximale Ordnung
:param maxPartCircum: maximale Aufteilung (1/x halber Ellipsoidumfang)
:return: Zielpunkt, Azimut im Zielpunkt
"""
if s > np.pi / maxPartCircum * ell.ax:
s /= 2
point_step, alpha_step = gha1_ana(ell, point, alpha0, s, maxM, maxPartCircum)
point_end, alpha_end = gha1_ana(ell, point_step, alpha_step, s, maxM, maxPartCircum)
else:
point_end, alpha_end = gha1_ana_step(ell, point, alpha0, s, maxM)
_, _, h = ell.cart2geod(point_end, "ligas3")
if h > 1e-5:
raise Exception("Analytische Methode ist explodiert, Punkt liegt nicht mehr auf dem Ellipsoid")
return point_end, alpha_end

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import numpy as np
from ellipsoide import EllipsoidTriaxial
from GHA_triaxial.panou import louville_constant, func_sigma_ell, gha1_ana
from GHA_triaxial.gha1_ana import gha1_ana
from GHA_triaxial.utils import func_sigma_ell, louville_constant
import plotly.graph_objects as go
import winkelumrechnungen as wu

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import numpy as np
from numpy import sin, cos, arctan2
import ellipsoide
import runge_kutta as rk
import winkelumrechnungen as wu
import GHA_triaxial.numeric_examples_karney as ne_karney
from GHA_triaxial.gha1_ana import gha1_ana
from ellipsoide import EllipsoidTriaxial
from typing import Callable, Tuple, List
from numpy.typing import NDArray
from GHA_triaxial.utils import alpha_ell2para, pq_ell
def gha1_num(ell: EllipsoidTriaxial, point: NDArray, alpha0: float, s: float, num: int, all_points: bool = False) -> Tuple[NDArray, float] | Tuple[NDArray, float, List]:
"""
Panou, Korakitits 2019
:param ell: Ellipsoid
:param point: Punkt in kartesischen Koordinaten
:param alpha0: Azimut im Startpunkt
:param s: Strecke
:param num: Anzahl Zwischenpunkte
:param all_points: Ausgabe aller Punkte?
:return: Zielpunkt, Azimut im Zielpunkt (, alle Punkte)
"""
phi, lam, _ = ell.cart2geod(point, "ligas3")
p0 = ell.geod2cart(phi, lam, 0)
x0, y0, z0 = p0
p, q = pq_ell(ell, p0)
dxds0 = p[0] * sin(alpha0) + q[0] * cos(alpha0)
dyds0 = p[1] * sin(alpha0) + q[1] * cos(alpha0)
dzds0 = p[2] * sin(alpha0) + q[2] * cos(alpha0)
v_init = np.array([x0, dxds0, y0, dyds0, z0, dzds0])
def buildODE(ell: EllipsoidTriaxial) -> Callable:
"""
Aufbau des DGL-Systems
:param ell: Ellipsoid
:return: DGL-System
"""
def ODE(s: float, v: NDArray) -> NDArray:
"""
DGL-System
:param s: unabhängige Variable
:param v: abhängige Variablen
:return: Ableitungen der abhängigen Variablen
"""
x, dxds, y, dyds, z, dzds = v
H = ell.func_H(np.array([x, y, z]))
h = dxds ** 2 + 1 / (1 - ell.ee ** 2) * dyds ** 2 + 1 / (1 - ell.ex ** 2) * dzds ** 2
ddx = -(h / H) * x
ddy = -(h / H) * y / (1 - ell.ee ** 2)
ddz = -(h / H) * z / (1 - ell.ex ** 2)
return np.array([dxds, ddx, dyds, ddy, dzds, ddz])
return ODE
ode = buildODE(ell)
_, werte = rk.rk4(ode, 0, v_init, s, num)
x1, dx1ds, y1, dy1ds, z1, dz1ds = werte[-1]
point1 = np.array([x1, y1, z1])
p1, q1 = pq_ell(ell, point1)
sigma = np.array([dx1ds, dy1ds, dz1ds])
P = float(p1 @ sigma)
Q = float(q1 @ sigma)
alpha1 = arctan2(P, Q)
if alpha1 < 0:
alpha1 += 2 * np.pi
if all_points:
return point1, alpha1, werte
else:
return point1, alpha1
if __name__ == "__main__":
# ell = ellipsoide.EllipsoidTriaxial.init_name("BursaSima1980round")
# diffs_panou = []
# examples_panou = ne_panou.get_random_examples(5)
# for example in examples_panou:
# beta0, lamb0, beta1, lamb1, _, alpha0_ell, alpha1_ell, s = example
# P0 = ell.ell2cart(beta0, lamb0)
#
# P1_num, alpha1_num = gha1_num(ell, P0, alpha0_ell, s, 100)
# beta1_num, lamb1_num = ell.cart2ell(P1_num)
#
# _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)
# P1_ana, alpha1_ana = gha1_ana(ell, P0, alpha0_para, s, 60)
# beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)
# diffs_panou.append((abs(beta1-beta1_num), abs(lamb1-lamb1_num), abs(beta1-beta1_ana), abs(lamb1-lamb1_ana)))
# diffs_panou = np.array(diffs_panou)
# mask_360 = (diffs_panou > 359) & (diffs_panou < 361)
# diffs_panou[mask_360] = np.abs(diffs_panou[mask_360] - 360)
# print(diffs_panou)
ell: EllipsoidTriaxial = ellipsoide.EllipsoidTriaxial.init_name("KarneyTest2024")
diffs_karney = []
# examples_karney = ne_karney.get_examples((30499, 30500, 40500))
examples_karney = ne_karney.get_random_examples(20)
for example in examples_karney:
beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example
P0 = ell.ell2cart(beta0, lamb0)
P1_num, alpha1_num = gha1_num(ell, P0, alpha0_ell, s, 10000)
beta1_num, lamb1_num = ell.cart2ell(P1_num)
try:
_, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)
P1_ana, alpha1_ana = gha1_ana(ell, P0, alpha0_para, s, 45, maxPartCircum=32)
beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)
except:
beta1_ana, lamb1_ana = np.inf, np.inf
diffs_karney.append((wu.rad2deg(abs(beta1-beta1_num)), wu.rad2deg(abs(lamb1-lamb1_num)), wu.rad2deg(abs(beta1-beta1_ana)), wu.rad2deg(abs(lamb1-lamb1_ana))))
diffs_karney = np.array(diffs_karney)
mask_360 = (diffs_karney > 359) & (diffs_karney < 361)
diffs_karney[mask_360] = np.abs(diffs_karney[mask_360] - 360)
print(diffs_karney)

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import numpy as np
from numpy import arccos
from Hansen_ES_CMA import escma
from ellipsoide import EllipsoidTriaxial
from numpy.typing import NDArray
import plotly.graph_objects as go
from GHA_triaxial.panou_2013_2GHA_num import gha2_num
from utils import sigma2alpha
from GHA_triaxial.gha2_num import gha2_num
from GHA_triaxial.utils import sigma2alpha
ell_ES: EllipsoidTriaxial = None
P_left: NDArray = None

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import numpy as np
from ellipsoide import EllipsoidTriaxial
from GHA_triaxial.panou_2013_2GHA_num import gha2_num
from GHA_triaxial.gha2_num import gha2_num
import plotly.graph_objects as go
import winkelumrechnungen as wu
from numpy.typing import NDArray
from typing import Tuple
from utils import sigma2alpha
from GHA_triaxial.utils import sigma2alpha
def gha2_approx(ell: EllipsoidTriaxial, p0: NDArray, p1: NDArray, ds: float, all_points: bool = False) -> Tuple[float, float, float] | Tuple[float, float, float, NDArray]:

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import numpy as np
from numpy import sin, cos, sqrt, arctan2
import ellipsoide
import runge_kutta as rk
import winkelumrechnungen as wu
from scipy.special import factorial as fact
from math import comb
import GHA_triaxial.numeric_examples_panou as ne_panou
import GHA_triaxial.numeric_examples_karney as ne_karney
from ellipsoide import EllipsoidTriaxial
from typing import Callable, Tuple, List
from numpy.typing import NDArray
def pq_ell(ell: EllipsoidTriaxial, point: NDArray) -> Tuple[NDArray, NDArray]:
"""
Berechnung von p und q in elliptischen Koordinaten
Panou, Korakitits 2019
:param ell: Ellipsoid
:param point: Punkt
:return: p und q
"""
x, y, z = point
n = ell.func_n(point)
beta, lamb = ell.cart2ell(point)
B = ell.Ex ** 2 * cos(beta) ** 2 + ell.Ee ** 2 * sin(beta) ** 2
L = ell.Ex ** 2 - ell.Ee ** 2 * cos(lamb) ** 2
c1 = x ** 2 + y ** 2 + z ** 2 - (ell.ax ** 2 + ell.ay ** 2 + ell.b ** 2)
c0 = (ell.ax ** 2 * ell.ay ** 2 + ell.ax ** 2 * ell.b ** 2 + ell.ay ** 2 * ell.b ** 2 -
(ell.ay ** 2 + ell.b ** 2) * x ** 2 - (ell.ax ** 2 + ell.b ** 2) * y ** 2 - (
ell.ax ** 2 + ell.ay ** 2) * z ** 2)
t2 = (-c1 + sqrt(c1 ** 2 - 4 * c0)) / 2
F = ell.Ey ** 2 * cos(beta) ** 2 + ell.Ee ** 2 * sin(lamb) ** 2
p1 = -sqrt(L / (F * t2)) * ell.ax / ell.Ex * sqrt(B) * sin(lamb)
p2 = sqrt(L / (F * t2)) * ell.ay * cos(beta) * cos(lamb)
p3 = 1 / sqrt(F * t2) * (ell.b * ell.Ee ** 2) / (2 * ell.Ex) * sin(beta) * sin(2 * lamb)
p = np.array([p1, p2, p3])
p = p / np.linalg.norm(p)
q = np.array([n[1] * p[2] - n[2] * p[1],
n[2] * p[0] - n[0] * p[2],
n[0] * p[1] - n[1] * p[0]])
q = q / np.linalg.norm(q)
return p, q
def buildODE(ell: EllipsoidTriaxial) -> Callable:
"""
Aufbau des DGL-Systems
:param ell: Ellipsoid
:return: DGL-System
"""
def ODE(s: float, v: NDArray) -> NDArray:
"""
DGL-System
:param s: unabhängige Variable
:param v: abhängige Variablen
:return: Ableitungen der abhängigen Variablen
"""
x, dxds, y, dyds, z, dzds = v
H = ell.func_H(np.array([x, y, z]))
h = dxds**2 + 1/(1-ell.ee**2)*dyds**2 + 1/(1-ell.ex**2)*dzds**2
ddx = -(h/H)*x
ddy = -(h/H)*y/(1-ell.ee**2)
ddz = -(h/H)*z/(1-ell.ex**2)
return np.array([dxds, ddx, dyds, ddy, dzds, ddz])
return ODE
def gha1_num(ell: EllipsoidTriaxial, point: NDArray, alpha0: float, s: float, num: int, all_points: bool = False) -> Tuple[NDArray, float] | Tuple[NDArray, float, List]:
"""
Panou, Korakitits 2019
:param ell:
:param point:
:param alpha0:
:param s:
:param num:
:param all_points:
:return:
"""
phi, lam, _ = ell.cart2geod(point, "ligas3")
p0 = ell.geod2cart(phi, lam, 0)
x0, y0, z0 = p0
p, q = pq_ell(ell, p0)
dxds0 = p[0] * sin(alpha0) + q[0] * cos(alpha0)
dyds0 = p[1] * sin(alpha0) + q[1] * cos(alpha0)
dzds0 = p[2] * sin(alpha0) + q[2] * cos(alpha0)
v_init = np.array([x0, dxds0, y0, dyds0, z0, dzds0])
ode = buildODE(ell)
_, werte = rk.rk4(ode, 0, v_init, s, num)
x1, dx1ds, y1, dy1ds, z1, dz1ds = werte[-1]
point1 = np.array([x1, y1, z1])
p1, q1 = pq_ell(ell, point1)
sigma = np.array([dx1ds, dy1ds, dz1ds])
P = float(p1 @ sigma)
Q = float(q1 @ sigma)
alpha1 = arctan2(P, Q)
if alpha1 < 0:
alpha1 += 2 * np.pi
if all_points:
return point1, alpha1, werte
else:
return point1, alpha1
# ---------------------------------------------------------------------------------------------------------------------
def pq_para(ell: EllipsoidTriaxial, point: NDArray) -> Tuple[NDArray, NDArray]:
"""
Berechnung von p und q in parametrischen Koordinaten
Panou, Korakitits 2020
:param ell: Ellipsoid
:param point: Punkt
:return: p und q
"""
n = ell.func_n(point)
u, v = ell.cart2para(point)
# 41-47
G = sqrt(1 - ell.ex ** 2 * cos(u) ** 2 - ell.ee ** 2 * sin(u) ** 2 * sin(v) ** 2)
q = np.array([-1 / G * sin(u) * cos(v),
-1 / G * sqrt(1 - ell.ee ** 2) * sin(u) * sin(v),
1 / G * sqrt(1 - ell.ex ** 2) * cos(u)])
p = np.array([q[1] * n[2] - q[2] * n[1],
q[2] * n[0] - q[0] * n[2],
q[0] * n[1] - q[1] * n[0]])
t1 = np.dot(n, q)
t2 = np.dot(n, p)
t3 = np.dot(p, q)
if not (t1 < 1e-10 or t1 > 1-1e-10) and not (t2 < 1e-10 or t2 > 1-1e-10) and not (t3 < 1e-10 or t3 > 1-1e-10):
raise Exception("Fehler in den normierten Vektoren")
p = p / np.linalg.norm(p)
q = q / np.linalg.norm(q)
return p, q
def gha1_ana_step(ell: EllipsoidTriaxial, point: NDArray, alpha0: float, s: float, maxM: int) -> Tuple[NDArray, float]:
"""
Panou, Korakitits 2020, 5ff.
:param ell:
:param point:
:param alpha0:
:param s:
:param maxM:
:return:
"""
x, y, z = point
# S. 6
x_m = [x]
y_m = [y]
z_m = [z]
p, q = pq_para(ell, point)
# 48-50
x_m.append(p[0] * sin(alpha0) + q[0] * cos(alpha0))
y_m.append(p[1] * sin(alpha0) + q[1] * cos(alpha0))
z_m.append(p[2] * sin(alpha0) + q[2] * cos(alpha0))
# 34
H_ = lambda p: np.sum([comb(p, p - i) * (x_m[p - i] * x_m[i] +
1 / (1 - ell.ee ** 2) ** 2 * y_m[p - i] * y_m[i] +
1 / (1 - ell.ex ** 2) ** 2 * z_m[p - i] * z_m[i])
for i in range(0, p + 1)])
# 35
h_ = lambda q: np.sum([comb(q, q-j) * (x_m[q-j+1] * x_m[j+1] +
1 / (1 - ell.ee ** 2) * y_m[q-j+1] * y_m[j+1] +
1 / (1 - ell.ex ** 2) * z_m[q-j+1] * z_m[j+1])
for j in range(0, q+1)])
# 31
hH_ = lambda t: 1/H_(0) * (h_(t) - np.sum([comb(t, l-1) * H_(t+1-l) * hH_t[l-1] for l in range(1, t+1)]))
# 28-30
x_ = lambda m: - np.sum([comb(m-2, k) * hH_t[m-2-k] * x_m[k] for k in range(0, m-2+1)])
y_ = lambda m: -1 / (1-ell.ee**2) * np.sum([comb(m-2, k) * hH_t[m-2-k] * y_m[k] for k in range(0, m-2+1)])
z_ = lambda m: -1 / (1-ell.ex**2) * np.sum([comb(m-2, k) * hH_t[m-2-k] * z_m[k] for k in range(0, m-2+1)])
hH_t = []
a_m = []
b_m = []
c_m = []
for m in range(0, maxM+1):
if m >= 2:
hH_t.append(hH_(m-2))
x_m.append(x_(m))
y_m.append(y_(m))
z_m.append(z_(m))
fact_m = fact(m)
# 22-24
a_m.append(x_m[m] / fact_m)
b_m.append(y_m[m] / fact_m)
c_m.append(z_m[m] / fact_m)
# 19-21
x_s = 0
for a in reversed(a_m):
x_s = x_s * s + a
y_s = 0
for b in reversed(b_m):
y_s = y_s * s + b
z_s = 0
for c in reversed(c_m):
z_s = z_s * s + c
p1 = np.array([x_s, y_s, z_s])
p_s, q_s = pq_para(ell, p1)
# 57-59
dx_s = 0
for i, a in reversed(list(enumerate(a_m[1:], start=1))):
dx_s = dx_s * s + i * a
dy_s = 0
for i, b in reversed(list(enumerate(b_m[1:], start=1))):
dy_s = dy_s * s + i * b
dz_s = 0
for i, c in reversed(list(enumerate(c_m[1:], start=1))):
dz_s = dz_s * s + i * c
# 52-53
sigma = np.array([dx_s, dy_s, dz_s])
P = float(p_s @ sigma)
Q = float(q_s @ sigma)
# 51
alpha1 = arctan2(P, Q)
if alpha1 < 0:
alpha1 += 2 * np.pi
return p1, alpha1
def gha1_ana(ell: EllipsoidTriaxial, point: NDArray, alpha0: float, s: float, maxM: int, maxPartCircum: int = 16) -> Tuple[NDArray, float]:
if s > np.pi / maxPartCircum * ell.ax:
s /= 2
point_step, alpha_step = gha1_ana(ell, point, alpha0, s, maxM, maxPartCircum)
point_end, alpha_end = gha1_ana(ell, point_step, alpha_step, s, maxM, maxPartCircum)
else:
point_end, alpha_end = gha1_ana_step(ell, point, alpha0, s, maxM)
_, _, h = ell.cart2geod(point_end, "ligas3")
if h > 1e-5:
raise Exception("Analytische Methode ist explodiert, Punkt liegt nicht mehr auf dem Ellipsoid")
return point_end, alpha_end
def alpha_para2ell(ell: EllipsoidTriaxial, u: float, v: float, alpha_para: float) -> Tuple[float, float, float]:
point = ell.para2cart(u, v)
beta, lamb = ell.para2ell(u, v)
p_para, q_para = pq_para(ell, point)
sigma_para = p_para * sin(alpha_para) + q_para * cos(alpha_para)
p_ell, q_ell = pq_ell(ell, point)
alpha_ell = arctan2(p_ell @ sigma_para, q_ell @ sigma_para)
sigma_ell = p_ell * sin(alpha_ell) + q_ell * cos(alpha_ell)
if np.linalg.norm(sigma_para - sigma_ell) > 1e-12:
raise Exception("Alpha Umrechnung fehlgeschlagen")
return beta, lamb, alpha_ell
def alpha_ell2para(ell: EllipsoidTriaxial, beta: float, lamb: float, alpha_ell: float) -> Tuple[float, float, float]:
point = ell.ell2cart(beta, lamb)
u, v = ell.ell2para(beta, lamb)
p_ell, q_ell = pq_ell(ell, point)
sigma_ell = p_ell * sin(alpha_ell) + q_ell * cos(alpha_ell)
p_para, q_para = pq_para(ell, point)
alpha_para = arctan2(p_para @ sigma_ell, q_para @ sigma_ell)
sigma_para = p_para * sin(alpha_para) + q_para * cos(alpha_para)
if np.linalg.norm(sigma_para - sigma_ell) > 1e-9:
raise Exception("Alpha Umrechnung fehlgeschlagen")
return u, v, alpha_para
def func_sigma_ell(ell: EllipsoidTriaxial, point: NDArray, alpha: float) -> NDArray:
p, q = pq_ell(ell, point)
sigma = p * sin(alpha) + q * cos(alpha)
return sigma
def func_sigma_para(ell: EllipsoidTriaxial, point: NDArray, alpha: float) -> NDArray:
p, q = pq_para(ell, point)
sigma = p * sin(alpha) + q * cos(alpha)
return sigma
def louville_constant(ell: EllipsoidTriaxial, p0: NDArray, alpha: float) -> float:
beta, lamb = ell.cart2ell(p0)
l = ell.Ey**2 * cos(beta)**2 * sin(alpha)**2 - ell.Ee**2 * sin(lamb)**2 * cos(alpha)**2
return l
def louville_l2c(ell: EllipsoidTriaxial, l: float) -> float:
return sqrt((l + ell.Ee**2) / ell.Ex**2)
def louville_c2l(ell: EllipsoidTriaxial, c: float) -> float:
return ell.Ex**2 * c**2 - ell.Ee**2
if __name__ == "__main__":
# ell = ellipsoide.EllipsoidTriaxial.init_name("BursaSima1980round")
# diffs_panou = []
# examples_panou = ne_panou.get_random_examples(5)
# for example in examples_panou:
# beta0, lamb0, beta1, lamb1, _, alpha0_ell, alpha1_ell, s = example
# P0 = ell.ell2cart(beta0, lamb0)
#
# P1_num, alpha1_num = gha1_num(ell, P0, alpha0_ell, s, 100)
# beta1_num, lamb1_num = ell.cart2ell(P1_num)
#
# _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)
# P1_ana, alpha1_ana = gha1_ana(ell, P0, alpha0_para, s, 60)
# beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)
# diffs_panou.append((abs(beta1-beta1_num), abs(lamb1-lamb1_num), abs(beta1-beta1_ana), abs(lamb1-lamb1_ana)))
# diffs_panou = np.array(diffs_panou)
# mask_360 = (diffs_panou > 359) & (diffs_panou < 361)
# diffs_panou[mask_360] = np.abs(diffs_panou[mask_360] - 360)
# print(diffs_panou)
ell: EllipsoidTriaxial = ellipsoide.EllipsoidTriaxial.init_name("KarneyTest2024")
diffs_karney = []
# examples_karney = ne_karney.get_examples((30499, 30500, 40500))
examples_karney = ne_karney.get_random_examples(20)
for example in examples_karney:
beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example
P0 = ell.ell2cart(beta0, lamb0)
P1_num, alpha1_num = gha1_num(ell, P0, alpha0_ell, s, 10000)
beta1_num, lamb1_num = ell.cart2ell(P1_num)
try:
_, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)
P1_ana, alpha1_ana = gha1_ana(ell, P0, alpha0_para, s, 45, maxPartCircum=32)
beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)
except:
beta1_ana, lamb1_ana = np.inf, np.inf
diffs_karney.append((wu.rad2deg(abs(beta1-beta1_num)), wu.rad2deg(abs(lamb1-lamb1_num)), wu.rad2deg(abs(beta1-beta1_ana)), wu.rad2deg(abs(lamb1-lamb1_ana))))
diffs_karney = np.array(diffs_karney)
mask_360 = (diffs_karney > 359) & (diffs_karney < 361)
diffs_karney[mask_360] = np.abs(diffs_karney[mask_360] - 360)
print(diffs_karney)

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from typing import Tuple
import numpy as np
from numpy import arctan2, sin, cos, sqrt
from numpy._typing import NDArray
from numpy.typing import NDArray
from ellipsoide import EllipsoidTriaxial
def sigma2alpha(ell: EllipsoidTriaxial, sigma: NDArray, point: NDArray) -> float:
"""
Berechnung des Azimuts an einem Punkt anhand der Ableitung zu den kartesischen Koordinaten
:param ell: Ellipsoid
:param sigma: Ableitungsvektor ver kartesischen Koordinaten
:param point: Punkt
:return: Azimuts
"""
p, q = pq_ell(ell, point)
P = float(p @ sigma)
Q = float(q @ sigma)
alpha = arctan2(P, Q)
return alpha
def alpha_para2ell(ell: EllipsoidTriaxial, u: float, v: float, alpha_para: float) -> Tuple[float, float, float]:
"""
Umrechnung des Azimuts bezogen auf parametrische Koordinaten zu ellipsoidischen
:param ell: Ellipsoid
:param u: parametrische Breite
:param v: parametrische Länge
:param alpha_para: Azimut bezogen auf parametrische Koordinaten
:return: Azimut bezogen auf ellipsoidische Koordinaten
"""
point = ell.para2cart(u, v)
beta, lamb = ell.para2ell(u, v)
p_para, q_para = pq_para(ell, point)
sigma_para = p_para * sin(alpha_para) + q_para * cos(alpha_para)
p_ell, q_ell = pq_ell(ell, point)
alpha_ell = arctan2(p_ell @ sigma_para, q_ell @ sigma_para)
sigma_ell = p_ell * sin(alpha_ell) + q_ell * cos(alpha_ell)
if np.linalg.norm(sigma_para - sigma_ell) > 1e-12:
raise Exception("Alpha Umrechnung fehlgeschlagen")
return beta, lamb, alpha_ell
def alpha_ell2para(ell: EllipsoidTriaxial, beta: float, lamb: float, alpha_ell: float) -> Tuple[float, float, float]:
"""
Umrechnung des Azimuts bezogen auf ellipsoidische Koordinaten zu parametrischen
:param ell: Ellipsoid
:param beta: ellipsoidische Breite
:param lamb: ellipsoidische Länge
:param alpha_ell: Azimut bezogen auf ellipsoidische Koordinaten
:return: Azimut bezogen auf parametrische Koordinaten
"""
point = ell.ell2cart(beta, lamb)
u, v = ell.ell2para(beta, lamb)
p_ell, q_ell = pq_ell(ell, point)
sigma_ell = p_ell * sin(alpha_ell) + q_ell * cos(alpha_ell)
p_para, q_para = pq_para(ell, point)
alpha_para = arctan2(p_para @ sigma_ell, q_para @ sigma_ell)
sigma_para = p_para * sin(alpha_para) + q_para * cos(alpha_para)
if np.linalg.norm(sigma_para - sigma_ell) > 1e-9:
raise Exception("Alpha Umrechnung fehlgeschlagen")
return u, v, alpha_para
def func_sigma_ell(ell: EllipsoidTriaxial, point: NDArray, alpha_ell: float) -> NDArray:
"""
Berechnung der Richtungsableitungen in kartesischen Koordinaten aus ellipsoidischem Azimut
Panou (2019) [6]
:param ell: Ellipsoid
:param point: Punkt in kartesischen Koordinaten
:param alpha_ell: ellipsoidischer Azimut
:return: Richtungsableitungen in kartesischen Koordinaten
"""
p, q = pq_ell(ell, point)
sigma = p * sin(alpha_ell) + q * cos(alpha_ell)
return sigma
def func_sigma_para(ell: EllipsoidTriaxial, point: NDArray, alpha_para: float) -> NDArray:
"""
Berechnung der Richtungsableitungen in kartesischen Koordinaten aus parametischem Azimut
Panou, Korakitis (2019) [6]
:param ell: Ellipsoid
:param point: Punkt in kartesischen Koordinaten
:param alpha_para: parametrischer Azimut
:return: Richtungsableitungen in kartesischen Koordinaten
"""
p, q = pq_para(ell, point)
sigma = p * sin(alpha_para) + q * cos(alpha_para)
return sigma
def louville_constant(ell: EllipsoidTriaxial, p0: NDArray, alpha_ell: float) -> float:
"""
Berechnung der Louville Konstanten
Panou, Korakitis (2019) [6]
:param ell: Ellipsoid
:param p0: Punkt in kartesischen Koordinaten
:param alpha_ell: ellipsoidischer Azimut
:return:
"""
beta, lamb = ell.cart2ell(p0)
l = ell.Ey**2 * cos(beta)**2 * sin(alpha_ell)**2 - ell.Ee**2 * sin(lamb)**2 * cos(alpha_ell)**2
return l
def pq_ell(ell: EllipsoidTriaxial, point: NDArray) -> Tuple[NDArray, NDArray]:
"""
Berechnung von p (Tangente entlang konstantem beta) und q (Tangente entlang konstantem lambda)
Panou, Korakitits (2019) [5f.]
:param ell: Ellipsoid
:param point: Punkt
:return: p und q
"""
x, y, z = point
n = ell.func_n(point)
beta, lamb = ell.cart2ell(point)
B = ell.Ex ** 2 * cos(beta) ** 2 + ell.Ee ** 2 * sin(beta) ** 2
L = ell.Ex ** 2 - ell.Ee ** 2 * cos(lamb) ** 2
c1 = x ** 2 + y ** 2 + z ** 2 - (ell.ax ** 2 + ell.ay ** 2 + ell.b ** 2)
c0 = (ell.ax ** 2 * ell.ay ** 2 + ell.ax ** 2 * ell.b ** 2 + ell.ay ** 2 * ell.b ** 2 -
(ell.ay ** 2 + ell.b ** 2) * x ** 2 - (ell.ax ** 2 + ell.b ** 2) * y ** 2 - (
ell.ax ** 2 + ell.ay ** 2) * z ** 2)
t2 = (-c1 + sqrt(c1 ** 2 - 4 * c0)) / 2
F = ell.Ey ** 2 * cos(beta) ** 2 + ell.Ee ** 2 * sin(lamb) ** 2
p1 = -sqrt(L / (F * t2)) * ell.ax / ell.Ex * sqrt(B) * sin(lamb)
p2 = sqrt(L / (F * t2)) * ell.ay * cos(beta) * cos(lamb)
p3 = 1 / sqrt(F * t2) * (ell.b * ell.Ee ** 2) / (2 * ell.Ex) * sin(beta) * sin(2 * lamb)
p = np.array([p1, p2, p3])
p = p / np.linalg.norm(p)
q = np.array([n[1] * p[2] - n[2] * p[1],
n[2] * p[0] - n[0] * p[2],
n[0] * p[1] - n[1] * p[0]])
q = q / np.linalg.norm(q)
return p, q
def pq_para(ell: EllipsoidTriaxial, point: NDArray) -> Tuple[NDArray, NDArray]:
"""
Berechnung von p (Tangente entlang konstantem u) und q (Tangente entlang konstantem v)
Panou, Korakitits (2020)
:param ell: Ellipsoid
:param point: Punkt
:return: p und q
"""
n = ell.func_n(point)
u, v = ell.cart2para(point)
# 41-47
G = sqrt(1 - ell.ex ** 2 * cos(u) ** 2 - ell.ee ** 2 * sin(u) ** 2 * sin(v) ** 2)
q = np.array([-1 / G * sin(u) * cos(v),
-1 / G * sqrt(1 - ell.ee ** 2) * sin(u) * sin(v),
1 / G * sqrt(1 - ell.ex ** 2) * cos(u)])
p = np.array([q[1] * n[2] - q[2] * n[1],
q[2] * n[0] - q[0] * n[2],
q[0] * n[1] - q[1] * n[0]])
t1 = np.dot(n, q)
t2 = np.dot(n, p)
t3 = np.dot(p, q)
if not (t1 < 1e-10 or t1 > 1-1e-10) and not (t2 < 1e-10 or t2 > 1-1e-10) and not (t3 < 1e-10 or t3 > 1-1e-10):
raise Exception("Fehler in den normierten Vektoren")
p = p / np.linalg.norm(p)
q = q / np.linalg.norm(q)
return p, q