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Doc-Strings und Type-Hinting

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This commit is contained in:
Hendrik Möllersmann
2026-01-13 11:09:12 +01:00
parent 8507ca1afa
commit efd1b8c5fb
9 changed files with 235 additions and 135 deletions
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41
GHA_triaxial/ES_gha2.py
View File

@@ -14,7 +14,7 @@ P_next: NDArray = None
P_end: NDArray = None
stepLen: float = None
def Bogenlaenge(P1, P2):
def Bogenlaenge(P1: NDArray, P2: NDArray) -> float:
"""
Berechnung der mittleren Bogenlänge zwischen zwei kartesischen Punkten
:param P1: kartesische Koordinate Punkt 1
@@ -23,9 +23,9 @@ def Bogenlaenge(P1, P2):
"""
R1 = np.linalg.norm(P1)
R2 = np.linalg.norm(P2)
R = 0.5*(R1 + R2)
R = 0.5 * (R1 + R2)
theta = arccos(P1 @ P2 / (R1 * R2))
s = R * theta
s = float(R * theta)
return s
@@ -49,7 +49,7 @@ def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: flo
ell_ES = ell
P_start = P0
P_end = Pk
if stepLenTarget == None:
if stepLenTarget is None:
R0 = (ell.ax + ell.ay + ell.b) / 3
stepLenTarget = R0 * 1 / 600
stepLen = stepLenTarget
@@ -99,7 +99,7 @@ def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: flo
# % v0 = atan2(q(2), q(1));
# % xmean_init = [u0;
# v0];
xmean_init = ell.cartonell(P_prev + stepLen * (P_end - P_prev) / np.linalg.norm(P_end - P_prev))
xmean_init = ell.point_onto_ellipsoid(P_prev + stepLen * (P_end - P_prev) / np.linalg.norm(P_end - P_prev))
# [~, ~, aux] = geoLength(xmean_init);
# print('Startguess: d_step=%.3f (soll %.3f), d_to_target=%.3f\n', aux(1), stepLen, aux(2));
@@ -107,7 +107,7 @@ def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: flo
print(f'[Punkt {i}] Optimiere nächsten Punkt: Restdistanz = {round(d_remain, 3)} m')
xmean_init = np.array(ell_ES.cart2para(xmean_init))
u, v = escma(geoLength,2, xmean_init, sigmaStep, -np.inf, stopeval)
u, v = escma(geoLength, N=2, xmean=xmean_init, sigma=sigmaStep, stopfitness=-np.inf, stopeval=stopeval)
P_next = ell.para2cart(u, v)
@@ -128,26 +128,30 @@ def gha2_ES(ell: EllipsoidTriaxial, P0: NDArray, Pk: NDArray, stepLenTarget: flo
totalLen += d_step
P_prev = P_next
print('Maximale Schrittanzahl erreicht.')
P_all.append(P_end)
totalLen += Bogenlaenge(P_prev, P_end)
p0i = ell.cartonell(P0 + 10 * (P_all[1] - P0) / np.linalg.norm(P_all[1] - P0))
p0i = ell.point_onto_ellipsoid(P0 + 10 * (P_all[1] - P0) / np.linalg.norm(P_all[1] - P0))
sigma0 = (p0i - P0) / np.linalg.norm(p0i - P0)
alpha0 = sigma2alpha(ell_ES, sigma0, P0)
p1i = ell.cartonell(Pk - 10 * (Pk - P_all[-2]) / np.linalg.norm(Pk - P_all[-2]))
p1i = ell.point_onto_ellipsoid(Pk - 10 * (Pk - P_all[-2]) / np.linalg.norm(Pk - P_all[-2]))
sigma1 = (Pk - p1i) / np.linalg.norm(Pk - p1i)
alpha1 = sigma2alpha(ell_ES, sigma1, Pk)
if all_points:
return alpha0, alpha1, totalLen, np.array(P_all)
else:
return alpha0, alpha1, totalLen
return alpha0, alpha1, totalLen
def geoLength(P_candidate):
def geoLength(P_candidate: Tuple) -> float:
"""
Berechung der Fitness eines Kandidaten anhand der Strecken
:param P_candidate: Kandidat in parametrischen Koordinaten
:return: Fitness-Wert
"""
# P_candidate = [u;v] des naechsten Punktes.
# Ziel: Distanz zum Ziel minimieren, aber Schrittlaenge ~ stepLenTarget erzwingen.
u, v = P_candidate
@@ -165,7 +169,7 @@ def geoLength(P_candidate):
pen_step = ((d_step - stepLen) / stepLen)**2
# falls Punkt "weg" vom Ziel geht, extra bestrafen
pen_away = max(0, (d_to_target - d_prev_to_target) / stepLen)**2
pen_away = max(0.0, (d_to_target - d_prev_to_target) / stepLen)**2
# Gewichtungen
alpha = 1e2
@@ -175,13 +179,20 @@ def geoLength(P_candidate):
f = d_to_target * (1 + alpha * pen_step + gamma * pen_away)
# Für Debug / Extraktion
aux = [d_step, d_to_target]
# aux = [d_step, d_to_target]
return f # , P_candidate, aux
def show_points(points: NDArray, pointsNum:NDArray, p0: NDArray, p1: NDArray):
def show_points(points: NDArray, pointsES: NDArray, p0: NDArray, p1: NDArray):
"""
Anzeigen der Punkte
:param points: wahre Punkte der Linie
:param pointsES: Punkte der Linie aus ES
:param p0: wahrer Startpunkt
:param p1: wahrer Endpunkt
"""
fig = go.Figure()
fig.add_scatter3d(x=pointsNum[:, 0], y=pointsNum[:, 1], z=pointsNum[:, 2],
fig.add_scatter3d(x=pointsES[:, 0], y=pointsES[:, 1], z=pointsES[:, 2],
mode='lines', line=dict(color="green", width=3), name="Numerisch")
fig.add_scatter3d(x=points[:, 0], y=points[:, 1], z=points[:, 2],
mode='lines', line=dict(color="red", width=3), name="ES")

24
GHA_triaxial/approx_gha1.py
View File

@@ -4,7 +4,17 @@ from panou import louville_constant, func_sigma_ell, gha1_ana
import plotly.graph_objects as go
import winkelumrechnungen as wu
def gha1(ell: EllipsoidTriaxial, p0: np.ndarray, alpha0: float, s: float, ds: float, all_points: bool = False) -> Tuple[NDArray, float] | Tuple[NDArray, float, NDArray]:
def gha1_approx(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]
@@ -17,7 +27,7 @@ def gha1(ell: EllipsoidTriaxial, p0: np.ndarray, alpha0: float, s: float, ds: fl
alpha1 = alphas[-1]
sigma = func_sigma_ell(ell, p1, alpha1)
p2 = p1 + ds_step * sigma
p2 = ell.cartonell(p2)
p2 = ell.point_onto_ellipsoid(p2)
ds_step = np.linalg.norm(p2 - p1)
points.append(p2)
@@ -33,7 +43,13 @@ def gha1(ell: EllipsoidTriaxial, p0: np.ndarray, alpha0: float, s: float, ds: fl
else:
return points[-1], alphas[-1]
def show_points(points, p0, p1):
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],
@@ -59,6 +75,6 @@ if __name__ == '__main__':
alpha0 = wu.deg2rad(90)
s = 1000000
P1_ana, alpha1_ana = gha1_ana(ell, P0, alpha0, s, maxM=60, maxPartCircum=32)
P1_app, alpha1_app, points = gha1(ell, P0, alpha0, s, ds=5000, all_points=True)
P1_app, alpha1_app, points = gha1_approx(ell, P0, alpha0, s, ds=5000, all_points=True)
show_points(points, P0, P1_ana)
print(np.linalg.norm(P1_app - P1_ana))

13
GHA_triaxial/approx_gha2.py
View File

@@ -27,7 +27,7 @@ def gha2(ell: EllipsoidTriaxial, p0: NDArray, p1: NDArray, ds: float, all_points
new_points.append(points[i])
pi = points[i] + 1/2 * (points[i+1] - points[i])
pi = ell.cartonell(pi)
pi = ell.point_onto_ellipsoid(pi)
new_points.append(pi)
@@ -39,11 +39,11 @@ def gha2(ell: EllipsoidTriaxial, p0: NDArray, p1: NDArray, ds: float, all_points
if np.average(elements) < ds:
break
p0i = ell.cartonell(p0 + ds/100 * (points[1] - p0) / np.linalg.norm(points[1] - p0))
p0i = ell.point_onto_ellipsoid(p0 + ds / 100 * (points[1] - p0) / np.linalg.norm(points[1] - p0))
sigma0 = (p0i - p0) / np.linalg.norm(p0i - p0)
alpha0 = sigma2alpha(ell, sigma0, p0)
p1i = ell.cartonell(p1 - ds/100 * (p1 - points[-2]) / np.linalg.norm(p1 - points[-2]))
p1i = ell.point_onto_ellipsoid(p1 - ds / 100 * (p1 - points[-2]) / np.linalg.norm(p1 - points[-2]))
sigma1 = (p1 - p1i) / np.linalg.norm(p1 - p1i)
alpha1 = sigma2alpha(ell, sigma1, p1)
@@ -55,6 +55,13 @@ def gha2(ell: EllipsoidTriaxial, p0: NDArray, p1: NDArray, ds: float, all_points
return alpha0, alpha1, s
def show_points(points: NDArray, points_app: NDArray, p0: NDArray, p1: NDArray):
"""
Anzeigen der Punkte
:param points: wahre Punkte der Linie
:param points_app: approximierte Punkte der Linie
:param p0: wahrer Startpunkt
:param p1: wahrer Endpunkt
"""
fig = go.Figure()
fig.add_scatter3d(x=points[:, 0], y=points[:, 1], z=points[:, 2],

27
GHA_triaxial/numeric_examples_karney.py
View File

@@ -1,7 +1,13 @@
import random
import winkelumrechnungen as wu
from Typing import List, Tuple
def line2example(line):
def line2example(line: str) -> List:
"""
Line-String in Liste umwandeln
:param line: Line-String
:return: Liste mit Zahlenwerten
"""
split = line.split()
example = [float(value) for value in split[:7]]
for i, value in enumerate(example):
@@ -10,13 +16,16 @@ def line2example(line):
# example[i] = value
return example
def get_random_examples(num):
def get_random_examples(num: int, seed: int = None) -> List:
"""
Rückgabe zufälliger Beispiele
beta0, lamb0, alpha0, beta1, lamb1, alpha1, s12
:param num:
:return:
:param num: Anzahl zufälliger Beispiele
:param seed: Random-Seed
:return: Liste mit Beispielen
"""
# random.seed(42)
if seed is not None:
random.seed(seed)
with open("Karney_2024_Testset.txt") as datei:
lines = datei.readlines()
examples = []
@@ -25,11 +34,12 @@ def get_random_examples(num):
examples.append(example)
return examples
def get_examples(l_i):
def get_examples(l_i: List) -> List:
"""
Rückgabe ausgewählter Beispiele
beta0, lamb0, alpha0, beta1, lamb1, alpha1, s12
:param num:
:return:
:param l_i: Liste von Indizes
:return: Liste mit Beispielen
"""
with open("Karney_2024_Testset.txt") as datei:
lines = datei.readlines()
@@ -39,5 +49,6 @@ def get_examples(l_i):
examples.append(example)
return examples
if __name__ == "__main__":
get_random_examples(10)

146
GHA_triaxial/numeric_examples_panou.py
View File

@@ -1,113 +1,134 @@
from jedi.inference.gradual.typing import Tuple, List
import winkelumrechnungen as wu
import random
table1 = [
(wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(90), 1.00000000000,
wu.gms2rad([90,0,0.0000]), wu.gms2rad([90,0,0.0000]), 10018754.9569),
(wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(90), 1.00000000000,
wu.gms2rad([90, 0, 0.0000]), wu.gms2rad([90, 0, 0.0000]), 10018754.9569),
(wu.deg2rad(1), wu.deg2rad(0), wu.deg2rad(-80), wu.deg2rad(5), 0.05883743460,
wu.gms2rad([179,7,12.2719]), wu.gms2rad([174,40,13.8487]), 8947130.7221),
(wu.deg2rad(1), wu.deg2rad(0), wu.deg2rad(-80), wu.deg2rad(5), 0.05883743460,
wu.gms2rad([179, 7, 12.2719]), wu.gms2rad([174, 40, 13.8487]), 8947130.7221),
(wu.deg2rad(5), wu.deg2rad(0), wu.deg2rad(-60), wu.deg2rad(40), 0.34128138370,
wu.gms2rad([160,13,24.5001]), wu.gms2rad([137,26,47.0036]), 8004762.4330),
(wu.deg2rad(5), wu.deg2rad(0), wu.deg2rad(-60), wu.deg2rad(40), 0.34128138370,
wu.gms2rad([160, 13, 24.5001]), wu.gms2rad([137, 26, 47.0036]), 8004762.4330),
(wu.deg2rad(30), wu.deg2rad(0), wu.deg2rad(-30), wu.deg2rad(175), 0.86632464962,
wu.gms2rad([91,7,30.9337]), wu.gms2rad([91,7,30.8672]), 19547128.7971),
(wu.deg2rad(30), wu.deg2rad(0), wu.deg2rad(-30), wu.deg2rad(175), 0.86632464962,
wu.gms2rad([91, 7, 30.9337]), wu.gms2rad([91, 7, 30.8672]), 19547128.7971),
(wu.deg2rad(60), wu.deg2rad(0), wu.deg2rad(60), wu.deg2rad(175), 0.06207487624,
wu.gms2rad([2,52,26.2393]), wu.gms2rad([177,4,13.6373]), 6705715.1610),
(wu.deg2rad(60), wu.deg2rad(0), wu.deg2rad(60), wu.deg2rad(175), 0.06207487624,
wu.gms2rad([2, 52, 26.2393]), wu.gms2rad([177, 4, 13.6373]), 6705715.1610),
(wu.deg2rad(75), wu.deg2rad(0), wu.deg2rad(80), wu.deg2rad(120), 0.11708984898,
wu.gms2rad([23,20,34.7823]), wu.gms2rad([140,55,32.6385]), 2482501.2608),
(wu.deg2rad(75), wu.deg2rad(0), wu.deg2rad(80), wu.deg2rad(120), 0.11708984898,
wu.gms2rad([23, 20, 34.7823]), wu.gms2rad([140, 55, 32.6385]), 2482501.2608),
(wu.deg2rad(80), wu.deg2rad(0), wu.deg2rad(60), wu.deg2rad(90), 0.17478427424,
wu.gms2rad([72,26,50.4024]), wu.gms2rad([159,38,30.3547]), 3519745.1283)
(wu.deg2rad(80), wu.deg2rad(0), wu.deg2rad(60), wu.deg2rad(90), 0.17478427424,
wu.gms2rad([72, 26, 50.4024]), wu.gms2rad([159, 38, 30.3547]), 3519745.1283)
]
table2 = [
(wu.deg2rad(0), wu.deg2rad(-90), wu.deg2rad(0), wu.deg2rad(89.5), 1.00000000000,
wu.gms2rad([90,0,0.0000]), wu.gms2rad([90,0,0.0000]), 19981849.8629),
(wu.deg2rad(0), wu.deg2rad(-90), wu.deg2rad(0), wu.deg2rad(89.5), 1.00000000000,
wu.gms2rad([90, 0, 0.0000]), wu.gms2rad([90, 0, 0.0000]), 19981849.8629),
(wu.deg2rad(1), wu.deg2rad(-90), wu.deg2rad(1), wu.deg2rad(89.5), 0.18979826428,
wu.gms2rad([10,56,33.6952]), wu.gms2rad([169,3,26.4359]), 19776667.0342),
(wu.deg2rad(1), wu.deg2rad(-90), wu.deg2rad(1), wu.deg2rad(89.5), 0.18979826428,
wu.gms2rad([10, 56, 33.6952]), wu.gms2rad([169, 3, 26.4359]), 19776667.0342),
(wu.deg2rad(5), wu.deg2rad(-90), wu.deg2rad(5), wu.deg2rad(89), 0.09398403161,
wu.gms2rad([5,24,48.3899]), wu.gms2rad([174,35,12.6880]), 18889165.0873),
(wu.deg2rad(5), wu.deg2rad(-90), wu.deg2rad(5), wu.deg2rad(89), 0.09398403161,
wu.gms2rad([5, 24, 48.3899]), wu.gms2rad([174, 35, 12.6880]), 18889165.0873),
(wu.deg2rad(30), wu.deg2rad(-90), wu.deg2rad(30), wu.deg2rad(86), 0.06004022935,
wu.gms2rad([3,58,23.8038]), wu.gms2rad([176,2,7.2825]), 13331814.6078),
(wu.deg2rad(30), wu.deg2rad(-90), wu.deg2rad(30), wu.deg2rad(86), 0.06004022935,
wu.gms2rad([3, 58, 23.8038]), wu.gms2rad([176, 2, 7.2825]), 13331814.6078),
(wu.deg2rad(60), wu.deg2rad(-90), wu.deg2rad(60), wu.deg2rad(78), 0.06076096484,
wu.gms2rad([6,56,46.4585]), wu.gms2rad([173,11,5.9592]), 6637321.6350),
(wu.deg2rad(60), wu.deg2rad(-90), wu.deg2rad(60), wu.deg2rad(78), 0.06076096484,
wu.gms2rad([6, 56, 46.4585]), wu.gms2rad([173, 11, 5.9592]), 6637321.6350),
(wu.deg2rad(75), wu.deg2rad(-90), wu.deg2rad(75), wu.deg2rad(66), 0.05805851008,
wu.gms2rad([12,40,34.9009]), wu.gms2rad([168,20,26.7339]), 3267941.2812),
(wu.deg2rad(75), wu.deg2rad(-90), wu.deg2rad(75), wu.deg2rad(66), 0.05805851008,
wu.gms2rad([12, 40, 34.9009]), wu.gms2rad([168, 20, 26.7339]), 3267941.2812),
(wu.deg2rad(80), wu.deg2rad(-90), wu.deg2rad(80), wu.deg2rad(55), 0.05817384452,
wu.gms2rad([18,35,40.7848]), wu.gms2rad([164,25,34.0017]), 2132316.9048)
(wu.deg2rad(80), wu.deg2rad(-90), wu.deg2rad(80), wu.deg2rad(55), 0.05817384452,
wu.gms2rad([18, 35, 40.7848]), wu.gms2rad([164, 25, 34.0017]), 2132316.9048)
]
table3 = [
(wu.deg2rad(0), wu.deg2rad(0.5), wu.deg2rad(80), wu.deg2rad(0.5), 0.05680316848,
wu.gms2rad([0,-0,16.0757]), wu.gms2rad([0,1,32.5762]), 8831874.3717),
(wu.deg2rad(0), wu.deg2rad(0.5), wu.deg2rad(80), wu.deg2rad(0.5), 0.05680316848,
wu.gms2rad([0, 0, 16.0757]), wu.gms2rad([0, 1, 32.5762]), 8831874.3717),
(wu.deg2rad(-1), wu.deg2rad(5), wu.deg2rad(75), wu.deg2rad(5), 0.05659149555,
wu.gms2rad([0,-1,47.2105]), wu.gms2rad([0,6,54.0958]), 8405370.4947),
(wu.deg2rad(-1), wu.deg2rad(5), wu.deg2rad(75), wu.deg2rad(5), 0.05659149555,
wu.gms2rad([0, -1, 47.2105]), wu.gms2rad([0, 6, 54.0958]), 8405370.4947),
(wu.deg2rad(-5), wu.deg2rad(30), wu.deg2rad(60), wu.deg2rad(30), 0.04921108945,
wu.gms2rad([0,-4,22.3516]), wu.gms2rad([0,8,42.0756]), 7204083.8568),
(wu.deg2rad(-5), wu.deg2rad(30), wu.deg2rad(60), wu.deg2rad(30), 0.04921108945,
wu.gms2rad([0, -4, 22.3516]), wu.gms2rad([0, 8, 42.0756]), 7204083.8568),
(wu.deg2rad(-30), wu.deg2rad(45), wu.deg2rad(30), wu.deg2rad(45), 0.04017812574,
wu.gms2rad([0,-3,41.2461]), wu.gms2rad([0,3,41.2461]), 6652788.1287),
(wu.deg2rad(-30), wu.deg2rad(45), wu.deg2rad(30), wu.deg2rad(45), 0.04017812574,
wu.gms2rad([0, -3, 41.2461]), wu.gms2rad([0, 3, 41.2461]), 6652788.1287),
(wu.deg2rad(-60), wu.deg2rad(60), wu.deg2rad(5), wu.deg2rad(60), 0.02843082609,
wu.gms2rad([0,-8,40.4575]), wu.gms2rad([0,4,22.1675]), 7213412.4477),
(wu.deg2rad(-60), wu.deg2rad(60), wu.deg2rad(5), wu.deg2rad(60), 0.02843082609,
wu.gms2rad([0, -8, 40.4575]), wu.gms2rad([0, 4, 22.1675]), 7213412.4477),
(wu.deg2rad(-75), wu.deg2rad(85), wu.deg2rad(1), wu.deg2rad(85), 0.00497802414,
wu.gms2rad([0,-6,44.6115]), wu.gms2rad([0,1,47.0474]), 8442938.5899),
(wu.deg2rad(-75), wu.deg2rad(85), wu.deg2rad(1), wu.deg2rad(85), 0.00497802414,
wu.gms2rad([0, -6, 44.6115]), wu.gms2rad([0, 1, 47.0474]), 8442938.5899),
(wu.deg2rad(-80), wu.deg2rad(89.5), wu.deg2rad(0), wu.deg2rad(89.5), 0.00050178253,
wu.gms2rad([0,-1,27.9705]), wu.gms2rad([0,0,16.0490]), 8888783.7815)
(wu.deg2rad(-80),wu.deg2rad(89.5), wu.deg2rad(0), wu.deg2rad(89.5), 0.00050178253,
wu.gms2rad([0, -1, 27.9705]), wu.gms2rad([0, 0, 16.0490]), 8888783.7815)
]
table4 = [
(wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(90), 1.00000000000,
wu.gms2rad([90,0,0.0000]), wu.gms2rad([90,0,0.0000]), 10018754.1714),
(wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(90), 1.00000000000,
wu.gms2rad([90, 0, 0.0000]), wu.gms2rad([90, 0, 0.0000]), 10018754.1714),
(wu.deg2rad(1), wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(179.5), 0.30320665822,
wu.gms2rad([17,39,11.0942]), wu.gms2rad([162,20,58.9032]), 19884417.8083),
(wu.deg2rad(1), wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(179.5), 0.30320665822,
wu.gms2rad([17, 39, 11.0942]), wu.gms2rad([162, 20, 58.9032]), 19884417.8083),
(wu.deg2rad(5), wu.deg2rad(0), wu.deg2rad(-80), wu.deg2rad(170), 0.03104258442,
wu.gms2rad([178,12,51.5083]), wu.gms2rad([10,17,52.6423]), 11652530.7514),
(wu.deg2rad(5), wu.deg2rad(0), wu.deg2rad(-80), wu.deg2rad(170), 0.03104258442,
wu.gms2rad([178, 12, 51.5083]), wu.gms2rad([10, 17, 52.6423]), 11652530.7514),
(wu.deg2rad(30), wu.deg2rad(0), wu.deg2rad(-75), wu.deg2rad(120), 0.24135347134,
wu.gms2rad([163,49,4.4615]), wu.gms2rad([68,49,50.9617]), 14057886.8752),
(wu.deg2rad(30), wu.deg2rad(0), wu.deg2rad(-75), wu.deg2rad(120), 0.24135347134,
wu.gms2rad([163, 49, 4.4615]), wu.gms2rad([68, 49, 50.9617]), 14057886.8752),
(wu.deg2rad(60), wu.deg2rad(0), wu.deg2rad(-60), wu.deg2rad(40), 0.19408499032,
wu.gms2rad([157,9,33.5589]), wu.gms2rad([157,9,33.5589]), 13767414.8267),
(wu.deg2rad(60), wu.deg2rad(0), wu.deg2rad(-60), wu.deg2rad(40), 0.19408499032,
wu.gms2rad([157, 9, 33.5589]), wu.gms2rad([157, 9, 33.5589]), 13767414.8267),
(wu.deg2rad(75), wu.deg2rad(0), wu.deg2rad(-30), wu.deg2rad(0.5), 0.00202789418,
wu.gms2rad([179,33,3.8613]), wu.gms2rad([179,51,57.0077]), 11661713.4496),
(wu.deg2rad(75), wu.deg2rad(0), wu.deg2rad(-30), wu.deg2rad(0.5), 0.00202789418,
wu.gms2rad([179, 33, 3.8613]), wu.gms2rad([179, 51, 57.0077]), 11661713.4496),
(wu.deg2rad(80), wu.deg2rad(0), wu.deg2rad(-5), wu.deg2rad(120), 0.15201222384,
wu.gms2rad([61,5,33.9600]), wu.gms2rad([171,13,22.0148]), 11105138.2902),
(wu.deg2rad(80), wu.deg2rad(0), wu.deg2rad(-5), wu.deg2rad(120), 0.15201222384,
wu.gms2rad([61, 5, 33.9600]), wu.gms2rad([171, 13, 22.0148]), 11105138.2902),
(wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(60), wu.deg2rad(0), 0.00000000000,
wu.gms2rad([0,0,0.0000]), wu.gms2rad([0,0,0.0000]), 6663348.2060)
(wu.deg2rad(0), wu.deg2rad(0), wu.deg2rad(60), wu.deg2rad(0), 0.00000000000,
wu.gms2rad([0, 0, 0.0000]), wu.gms2rad([0, 0, 0.0000]), 6663348.2060)
]
tables = [table1, table2, table3, table4]
def get_example(table, example):
def get_example(table: int, example: int) -> Tuple:
"""
Rückgabe eines Beispiels
:param table: Tabellen-Nummer
:param example: Beispiel-Nummer
:return: Bespiel
"""
table -= 1
example -= 1
tables = get_tables()
return tables[table][example]
def get_tables():
def get_tables() -> List:
"""
Rückgabe aller Tabellen
:return: Alle Tabellen
"""
return tables
def get_random_examples(num):
random.seed(42)
def get_random_examples(num: int, seed: int = None) -> List:
"""
Rückgabe zufäliger Beispiele
:param num: Anzahl Beispiele
:param seed: Random-Seed
:return:
"""
if seed is not None:
random.seed(seed)
examples = []
for i in range(num):
table = random.randint(1, 4)
@@ -120,7 +141,6 @@ def get_random_examples(num):
return examples
if __name__ == "__main__":
# test = get_example(1, 4)
examples = get_random_examples(5)

4
GHA_triaxial/panou_2013_2GHA_num.py
View File

@@ -10,7 +10,7 @@ from numpy.typing import NDArray
# 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]:
) -> Tuple[float, float, float] | Tuple[float, float, float, NDArray, NDArray]:
"""
:param ell: triaxiales Ellipsoid
@@ -252,7 +252,7 @@ def gha2_num(ell: EllipsoidTriaxial, beta_1: float, lamb_1: float, beta_2: float
else:
return alpha_1, alpha_2, s
else: # lamb_1 == lamb_2
else: # lamb_1 == lamb_2
N = n
dbeta = beta_2 - beta_1