14670 lines
346 KiB
Plaintext
14670 lines
346 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-09T10:12:48.665236Z",
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"start_time": "2026-02-09T10:12:48.507869Z"
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}
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},
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"cell_type": "code",
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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],
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"id": "89aa93e9dbedd113",
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"outputs": [],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-09T10:12:50.479637Z",
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"start_time": "2026-02-09T10:12:49.503680Z"
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}
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},
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"cell_type": "code",
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"source": [
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"%reload_ext autoreload\n",
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"%autoreload 2\n",
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"import time\n",
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"from numpy import nan\n",
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"import numpy as np\n",
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"import math\n",
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"import winkelumrechnungen as wu\n",
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"import os\n",
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"from contextlib import contextmanager, redirect_stdout, redirect_stderr\n",
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"import plotly.graph_objects as go\n",
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"import warnings\n",
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"import pickle\n",
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"import random\n",
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"from GHA_triaxial.gha1_ES import jacobi_konstante\n",
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"\n",
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"from ellipsoide import EllipsoidTriaxial\n",
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"from GHA_triaxial.utils import alpha_para2ell, alpha_ell2para\n",
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"\n",
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"from GHA_triaxial.gha1_num import gha1_num\n",
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"from GHA_triaxial.gha1_ana import gha1_ana\n",
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"from GHA_triaxial.gha1_ES import gha1_ES\n",
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"from GHA_triaxial.gha1_approx import gha1_approx\n",
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"\n",
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"from GHA_triaxial.gha2_num import gha2_num\n",
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"from GHA_triaxial.gha2_ES import gha2_ES\n",
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"from GHA_triaxial.gha2_approx import gha2_approx\n",
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"\n",
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"from GHA_triaxial.numeric_examples_panou import get_tables as get_tables_panou\n",
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"from GHA_triaxial.numeric_examples_karney import get_random_examples as get_examples_karney\n",
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"from GHA_triaxial.numeric_examples_karney import get_random_examples_gamma as get_examples_karney_gamma"
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],
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"id": "2005e5a8854eea1e",
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-09T09:02:39.843851Z",
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"start_time": "2026-02-09T09:02:38.998139Z"
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}
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},
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"cell_type": "code",
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"source": [
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"@contextmanager\n",
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"def suppress_print():\n",
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" with open(os.devnull, 'w') as fnull:\n",
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" with redirect_stdout(fnull), redirect_stderr(fnull):\n",
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" yield"
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],
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"id": "90f107a11ff0de7e",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n",
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"\n",
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"steps_gha1_num = [200, 500, 1000, 5000, 10000, 20000, 50000]\n",
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"maxM_gha1_ana = [20, 50, 80]\n",
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"parts_gha1_ana = [2, 8, 32]\n",
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"dsPart_gha1_ES = [60, 600, 1250]\n",
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"dsPart_gha1_approx = [600, 1250, 6000]\n",
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"\n",
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"steps_gha2_num = [200, 500, 1000, 5000, 10000]\n",
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"dsPart_gha2_ES = [60, 600, 1250]\n",
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"dsPart_gha2_approx = [600, 1250, 6000]"
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],
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"id": "fc6c39b8d358e54b",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-09T10:12:04.519634Z",
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"start_time": "2026-02-09T10:12:00.680678Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def build_examples(test):\n",
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" if test == \"Karney\":\n",
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" ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"KarneyTest2024\")\n",
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" num = 5\n",
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" seed = 42\n",
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" groups = [\"a_short\", \"a_long\", \"b_short\", \"b_long\", \"c_short\", \"c_long\", \"de_short\", \"de_long\", \"e_short\", \"e_long\"]\n",
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" group_indices = []\n",
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" examples = []\n",
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" for group in groups:\n",
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" split = group.split(\"_\")\n",
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" examples_group = get_examples_karney_gamma(split[0], num, seed, split[1])\n",
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" examples.extend(examples_group)\n",
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" group_indices.extend([group] * num)\n",
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"\n",
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" elif test == \"Panou\":\n",
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" ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"BursaSima1980round\")\n",
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" tables = get_tables_panou()\n",
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" group_indices = []\n",
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" examples = []\n",
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" for i, table in enumerate(tables):\n",
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" for example in table:\n",
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" group_indices.append(i+1)\n",
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" examples.append(example)\n",
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" examples = examples[:14]\n",
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" elif test == \"Random\":\n",
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" ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"BursaSima1980round\")\n",
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" examples = []\n",
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" random.seed(42)\n",
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" for _ in range(50):\n",
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" beta0 = wu.deg2rad(random.randint(-90, 90))\n",
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" lamb0 = wu.deg2rad(random.randint(-179, 180))\n",
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" alpha0_ell = wu.deg2rad(random.randint(0, 359))\n",
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" s = random.randint(10000, int(np.pi*ell.b))\n",
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" examples.append([beta0, lamb0, alpha0_ell, s])\n",
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" group_indices = None\n",
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" return ell, examples, group_indices\n",
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"test = \"Karney\"\n",
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"# test = \"Panou\"\n",
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"# test = \"Random\"\n",
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"ell, examples, group_indices = build_examples(test)\n",
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"pass"
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],
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"id": "6770dbd57d475127",
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"outputs": [],
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"execution_count": 14
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-08T18:15:50.911578Z",
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"start_time": "2026-02-08T18:15:50.683503Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def execute_results(test, ell, examples):\n",
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" results = {}\n",
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" for i, example in enumerate(examples):\n",
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" print(f\"----- Beispiel {i+1}/{len(examples)}\")\n",
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" example_results = {}\n",
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"\n",
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" if test != \"Random\":\n",
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" beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example\n",
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" P0 = ell.ell2cart(beta0, lamb0)\n",
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" P1 = ell.ell2cart(beta1, lamb1)\n",
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" _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)\n",
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" else:\n",
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" beta0, lamb0, alpha0_ell, s = example\n",
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" P0 = ell.ell2cart(beta0, lamb0)\n",
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" _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)\n",
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"\n",
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" # try:\n",
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" # P1, alpha1_para = gha1_ana(ell, P0, alpha0_para, s, maxM=80, maxPartCircum=8)\n",
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" # beta1, lamb1 = ell.cart2ell(P1)\n",
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" # u1, v1 = ell.cart2para(P1)\n",
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" # _, _, alpha1_ell = alpha_para2ell(ell, u1, v1, alpha1_para)\n",
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" # except Exception as e:\n",
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" # print(\"Referenz-Berechnung analytisch fehlgeschlagen: \", e)\n",
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" # continue\n",
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"\n",
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" try:\n",
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" P1, alpha1_ell = gha1_num(ell, P0, alpha0_ell, s, num=10000)\n",
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" beta1, lamb1 = ell.cart2ell(P1)\n",
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" except Exception as e:\n",
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" print(\"Referenz-Berechnung numerisch fehlgeschlagen: \", e)\n",
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" continue\n",
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"\n",
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" # for steps in steps_gha1_num:\n",
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" # start = time.perf_counter()\n",
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" # try:\n",
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" # P1_num, alpha1_num_1 = gha1_num(ell, P0, alpha0_ell, s, num=steps)\n",
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" # end = time.perf_counter()\n",
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" # beta1_num, lamb1_num = ell.cart2ell(P1_num)\n",
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" # d_beta1 = abs(beta1_num - beta1)\n",
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" # d_lamb1 = abs(lamb1_num - lamb1)\n",
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" # d_alpha1 = abs(alpha1_num_1 - alpha1_ell)\n",
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" # d_time = end - start\n",
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" # example_results[f\"GHA1_num_{steps}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n",
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" # except Exception as e:\n",
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" # print(e)\n",
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" # example_results[f\"GHA1_num_{steps}\"] = (nan, nan, nan, nan)\n",
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"\n",
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" for maxM in maxM_gha1_ana:\n",
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" for parts in parts_gha1_ana:\n",
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" start = time.perf_counter()\n",
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" try:\n",
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" P1_ana, alpha1_ana_para = gha1_ana(ell, P0, alpha0_para, s, maxM=maxM, maxPartCircum=parts)\n",
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" end = time.perf_counter()\n",
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" beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)\n",
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" u1_ana, v1_ana = ell.cart2para(P1_ana)\n",
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" _, _, alpha1_ana_ell = alpha_para2ell(ell, u1_ana, v1_ana, alpha1_ana_para)\n",
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" d_beta1 = abs(beta1_ana - beta1)\n",
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" d_lamb1 = abs(lamb1_ana - lamb1)\n",
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" d_alpha1 = abs(alpha1_ana_ell - alpha1_ell)\n",
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" d_time = end - start\n",
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" example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n",
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" except Exception as e:\n",
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" print(e)\n",
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" example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (nan, nan, nan, nan)\n",
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"\n",
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" # for dsPart in dsPart_gha1_ES:\n",
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" # ds = ell.ax/dsPart\n",
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" # start = time.perf_counter()\n",
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" # try:\n",
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" # P1_ES, alpha1_ES = gha1_ES(ell, beta0, lamb0, alpha0_ell, s, maxSegLen=ds)\n",
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" # end = time.perf_counter()\n",
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" # beta1_ES, lamb1_ES = ell.cart2ell(P1_ES)\n",
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" # d_beta1 = abs(beta1_ES - beta1)\n",
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" # d_lamb1 = abs(lamb1_ES - lamb1)\n",
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" # d_alpha1 = abs(alpha1_ES - alpha1_ell)\n",
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" # d_time = end - start\n",
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" # example_results[f\"GHA1_ES_{dsPart}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n",
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" # except Exception as e:\n",
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" # print(e)\n",
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" # example_results[f\"GHA1_ES_{dsPart}\"] = (nan, nan, nan, nan)\n",
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" #\n",
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" # for dsPart in dsPart_gha1_approx:\n",
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" # ds = ell.ax/dsPart\n",
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" # start = time.perf_counter()\n",
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" # try:\n",
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" # P1_approx, alpha1_approx = gha1_approx(ell, P0, alpha0_ell, s, ds=ds)\n",
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" # end = time.perf_counter()\n",
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" # beta1_approx, lamb1_approx = ell.cart2ell(P1_approx)\n",
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" # d_beta1 = abs(beta1_approx - beta1)\n",
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" # d_lamb1 = abs(lamb1_approx - lamb1)\n",
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" # d_alpha1 = abs(alpha1_approx - alpha1_ell)\n",
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" # d_time = end - start\n",
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" # example_results[f\"GHA1_approx_{dsPart}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n",
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" # except Exception as e:\n",
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" # print(e)\n",
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" # example_results[f\"GHA1_approx_{dsPart}\"] = (nan, nan, nan, nan)\n",
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" #\n",
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" # # ----------------------------------------------\n",
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" #\n",
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" # for steps in steps_gha2_num:\n",
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" # start = time.perf_counter()\n",
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" # try:\n",
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" # with warnings.catch_warnings():\n",
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" # warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
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" # alpha0_num, alpha1_num_2, s_num = gha2_num(ell, beta0, lamb0, beta1, lamb1, n=steps)\n",
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" # end = time.perf_counter()\n",
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" # d_alpha0 = abs(alpha0_num - alpha0_ell)\n",
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" # d_alpha1 = abs(alpha1_num_2 - alpha1_ell)\n",
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" # d_s = abs(s_num - s)\n",
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" # d_time = end - start\n",
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" # example_results[f\"GHA2_num_{steps}\"] = (d_alpha0, d_alpha1, d_s, d_time)\n",
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" # except Exception as e:\n",
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" # print(e)\n",
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" # example_results[f\"GHA2_num_{steps}\"] = (nan, nan, nan, nan)\n",
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" #\n",
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" # for dsPart in dsPart_gha2_ES:\n",
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" # ds = ell.ax/dsPart\n",
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" # start = time.perf_counter()\n",
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" # try:\n",
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" # with suppress_print():\n",
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" # alpha0_ES, alpha1_ES, s_ES = gha2_ES(ell, P0, P1, maxSegLen=ds)\n",
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" # end = time.perf_counter()\n",
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" # d_alpha0 = abs(alpha0_ES - alpha0_ell)\n",
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" # d_alpha1 = abs(alpha1_ES - alpha1_ell)\n",
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" # d_s = abs(s_ES - s)\n",
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" # d_time = end - start\n",
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" # example_results[f\"GHA2_ES_{dsPart}\"] = (d_alpha0, d_alpha1, d_s, d_time)\n",
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" # except Exception as e:\n",
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" # print(e)\n",
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" # example_results[f\"GHA2_ES_{dsPart}\"] = (nan, nan, nan, nan)\n",
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" #\n",
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" # for dsPart in dsPart_gha2_approx:\n",
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" # ds = ell.ax/dsPart\n",
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" # start = time.perf_counter()\n",
|
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" # try:\n",
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" # alpha0_approx, alpha1_approx, s_approx = gha2_approx(ell, P0, P1, ds=ds)\n",
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" # end = time.perf_counter()\n",
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" # d_alpha0 = abs(alpha0_approx - alpha0_ell)\n",
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" # d_alpha1 = abs(alpha1_approx - alpha1_ell)\n",
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" # d_s = abs(s_approx - s)\n",
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" # d_time = end - start\n",
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" # example_results[f\"GHA2_approx_{dsPart}\"] = (d_alpha0, d_alpha1, d_s, d_time)\n",
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" # except Exception as e:\n",
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" # print(e)\n",
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" # example_results[f\"GHA2_approx_{dsPart}\"] = (nan, nan, nan, nan)\n",
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"\n",
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" results[f\"beta0: {wu.rad2deg(beta0):.3f}, lamb0: {wu.rad2deg(lamb0):.3f}, alpha0: {wu.rad2deg(alpha0_ell):.3f}, s: {s}\"] = example_results\n",
|
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" return results\n",
|
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"# results = execute_results(test, ell, examples)"
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],
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"id": "fc45e0f618a0e4d8",
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"outputs": [],
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|
"execution_count": null
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|
},
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{
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"metadata": {
|
|
"ExecuteTime": {
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|
"end_time": "2026-02-08T18:20:36.069640Z",
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"start_time": "2026-02-08T18:20:35.873194Z"
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}
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},
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"cell_type": "code",
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"source": [
|
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"def save_results(test, results):\n",
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" with open(f\"gha_results{test}.pkl\", \"wb\") as f:\n",
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" pickle.dump(results, f)\n",
|
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"# save_results(test, results)"
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],
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"id": "74fbd4d33c288839",
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|
"outputs": [],
|
|
"execution_count": 10
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|
},
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|
{
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|
"metadata": {},
|
|
"cell_type": "code",
|
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"source": [
|
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"# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n",
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"\n",
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"steps_gha1_num = [1000, 10000, 50000]\n",
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"maxM_gha1_ana = [20, 60]\n",
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"parts_gha1_ana = [2, 8]\n",
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"dsPart_gha1_ES = [60, 600, 1250]\n",
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"dsPart_gha1_approx = [600, 1250, 6000]\n",
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"\n",
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"steps_gha2_num = [200, 500, 1000, 5000, 10000]\n",
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"dsPart_gha2_ES = [60, 600, 1250]\n",
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"dsPart_gha2_approx = [600, 1250, 6000]\n",
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"\n",
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"test = \"Panou\"\n",
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"ell_panou, examples_panou = build_examples(test)\n",
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"results_panou = execute_results(test, ell_panou, examples_panou)\n",
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"save_results(test, results_panou)"
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],
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|
"id": "58697308664fa539",
|
|
"outputs": [],
|
|
"execution_count": null
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "code",
|
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"source": [
|
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"# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n",
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"\n",
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"steps_gha1_num = [1000, 10000, 50000]\n",
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"maxM_gha1_ana = [20, 60]\n",
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"parts_gha1_ana = [8, 16, 64]\n",
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"dsPart_gha1_ES = [60, 600, 1250]\n",
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"dsPart_gha1_approx = [1250, 6000, 30000]\n",
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"\n",
|
|
"steps_gha2_num = [1000, 5000, 10000]\n",
|
|
"dsPart_gha2_ES = [60, 600, 1250]\n",
|
|
"dsPart_gha2_approx = [600, 1250, 6000]\n",
|
|
"\n",
|
|
"test = \"Karney\"\n",
|
|
"ell_karney, examples_karney, group_indices = build_examples(test)\n",
|
|
"results_karney = execute_results(test, ell_karney, examples_karney)\n",
|
|
"save_results(test, results_karney)"
|
|
],
|
|
"id": "5d35a01ba8d6e2b1",
|
|
"outputs": [],
|
|
"execution_count": null
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n",
|
|
"\n",
|
|
"steps_gha1_num = [200, 1000, 10000, 50000]\n",
|
|
"maxM_gha1_ana = [20, 60]\n",
|
|
"parts_gha1_ana = [2, 8]\n",
|
|
"dsPart_gha1_ES = [60, 600, 1250]\n",
|
|
"dsPart_gha1_approx = [600, 1250, 6000]\n",
|
|
"\n",
|
|
"steps_gha2_num = [200, 500, 1000, 5000, 10000]\n",
|
|
"dsPart_gha2_ES = [60, 600, 1250]\n",
|
|
"dsPart_gha2_approx = [600, 1250, 6000]\n",
|
|
"\n",
|
|
"test = \"Random\"\n",
|
|
"ell_random, examples_random = build_examples(test)\n",
|
|
"results_random = execute_results(test, ell_random, examples_random)\n",
|
|
"save_results(test, results_random)"
|
|
],
|
|
"id": "848eab44283945c6",
|
|
"outputs": [],
|
|
"execution_count": null
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2026-02-09T10:12:58.413282Z",
|
|
"start_time": "2026-02-09T10:12:58.243121Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"test = \"Karney\"\n",
|
|
"with open(f\"gha_results{test}.pkl\", \"rb\") as f:\n",
|
|
" results = pickle.load(f)"
|
|
],
|
|
"id": "4c20a0579c0f7038",
|
|
"outputs": [],
|
|
"execution_count": 3
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2026-02-09T10:12:59.354458Z",
|
|
"start_time": "2026-02-09T10:12:59.191379Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# results wrappen\n",
|
|
"metrics_gha1 = ['dBeta [\"]', 'dLambda [\"]', 'dAlpha1 [\"]', 'time [s]']\n",
|
|
"metrics_gha2 = ['dAlpha0 [\"]', 'dAlpha1 [\"]', 'dStrecke [m]', 'time [s]']\n",
|
|
"for example, example_metrics in results.items():\n",
|
|
" for method, method_metrics in example_metrics.items():\n",
|
|
" results[example][method] = list(results[example][method])\n",
|
|
" if \"GHA1\" in method:\n",
|
|
" for i, metric in enumerate(method_metrics):\n",
|
|
" if '[\"]' in metrics_gha1[i]:\n",
|
|
" if not np.isnan(metric):\n",
|
|
" metric %= (2*np.pi)\n",
|
|
" if not \"Beta\" in metrics_gha1[i]:\n",
|
|
" metric = min(metric, abs(metric - 2*np.pi))\n",
|
|
" if \"Alpha\" in metrics_gha1[i]:\n",
|
|
" metric = min(metric, abs(metric - np.pi))\n",
|
|
" results[example][method][i] = metric\n",
|
|
" if \"GHA2\" in method:\n",
|
|
" for i, metric in enumerate(method_metrics):\n",
|
|
" if abs(metric - np.pi) < 0.1:\n",
|
|
" metric -= np.pi\n",
|
|
" if '[\"]' in metrics_gha2[i]:\n",
|
|
" if not np.isnan(metric):\n",
|
|
" metric %= (2*np.pi)\n",
|
|
" metric = min(metric, abs(metric - 2*np.pi))\n",
|
|
" if \"Alpha\" in metrics_gha1[i]:\n",
|
|
" metric = min(metric, abs(metric - np.pi))\n",
|
|
" results[example][method][i] = metric\n",
|
|
" pass"
|
|
],
|
|
"id": "24ee93abe040e707",
|
|
"outputs": [],
|
|
"execution_count": 4
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2026-02-09T10:15:11.057178Z",
|
|
"start_time": "2026-02-09T10:15:10.842564Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# results nach Gruppe sortieren\n",
|
|
"metrics_gha1 = ['dBeta [\"]', 'dLambda [\"]', 'dAlpha1 [\"]', 'time [s]']\n",
|
|
"metrics_gha2 = ['dAlpha0 [\"]', 'dAlpha1 [\"]', 'dStrecke [m]', 'time [s]']\n",
|
|
"listed_results = {}\n",
|
|
"for example, example_metrics in results.items():\n",
|
|
" for method, method_metrics in example_metrics.items():\n",
|
|
" if \"GHA1\" in method:\n",
|
|
" if method not in listed_results.keys():\n",
|
|
" listed_results[method] = {metric: [] for metric in metrics_gha1}\n",
|
|
" for i, metric in enumerate(method_metrics):\n",
|
|
" if '[\"]' in metrics_gha1[i]:\n",
|
|
" metric = wu.rad2deg(metric)*3600\n",
|
|
" listed_results[method][metrics_gha1[i]].append(metric)\n",
|
|
" if \"GHA2\" in method:\n",
|
|
" if method not in listed_results.keys():\n",
|
|
" listed_results[method] = {metric: [] for metric in metrics_gha2}\n",
|
|
" for i, metric in enumerate(method_metrics):\n",
|
|
" if '[\"]' in metrics_gha2[i]:\n",
|
|
" metric = wu.rad2deg(metric)*3600\n",
|
|
" listed_results[method][metrics_gha2[i]].append(metric)\n",
|
|
" pass"
|
|
],
|
|
"id": "2086b5bcd8416e33",
|
|
"outputs": [],
|
|
"execution_count": 12
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "code",
|
|
"source": "pass",
|
|
"id": "8d46dd972dfdc66f",
|
|
"outputs": [],
|
|
"execution_count": null
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2026-02-09T10:13:44.482218Z",
|
|
"start_time": "2026-02-09T10:13:44.328266Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# group_indices Karney\n",
|
|
"group_indices = []\n",
|
|
"ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"KarneyTest2024\")\n",
|
|
"for example in results.keys():\n",
|
|
" long_short = 2\n",
|
|
" split = example.split(\",\")\n",
|
|
" beta0 = wu.deg2rad(float(split[0].split(\":\")[1]))\n",
|
|
" lamb0 = wu.deg2rad(float(split[1].split(\":\")[1]))\n",
|
|
" alpha0 = wu.deg2rad(float(split[2].split(\":\")[1]))\n",
|
|
" s = float(split[3].split(\":\")[1])\n",
|
|
" gamma = jacobi_konstante(beta0, lamb0, alpha0, ell)\n",
|
|
"\n",
|
|
" index = \"\"\n",
|
|
" if 1 >= gamma >= 0.01:\n",
|
|
" index = \"a\"\n",
|
|
" elif 0.01 > gamma > 1e-20:\n",
|
|
" index = \"b\"\n",
|
|
" elif abs(gamma) <= 1e-20:\n",
|
|
" index = \"c\"\n",
|
|
" elif -1e-20 > gamma > -1e-17:\n",
|
|
" index = \"d\"\n",
|
|
" elif -1e-17 >= gamma >= -1:\n",
|
|
" index = \"e\"\n",
|
|
"\n",
|
|
" if index != \"\":\n",
|
|
" if s < long_short:\n",
|
|
" index += \"_short\"\n",
|
|
" elif s >= long_short:\n",
|
|
" index += \"_long\"\n",
|
|
"\n",
|
|
" group_indices.append(index)"
|
|
],
|
|
"id": "567b66ef21126628",
|
|
"outputs": [],
|
|
"execution_count": 9
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2026-02-09T10:13:51.135041Z",
|
|
"start_time": "2026-02-09T10:13:47.712516Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# Tabellen ausgeben\n",
|
|
"def format_max(values, is_angle=False):\n",
|
|
" arr = np.array(values, dtype=float)\n",
|
|
" if arr.size==0:\n",
|
|
" return np.nan\n",
|
|
" maxi = np.nanmax(np.abs(arr))\n",
|
|
" if maxi is None or (isinstance(maxi,float) and (math.isnan(maxi))):\n",
|
|
" return \"nan\"\n",
|
|
" if is_angle:\n",
|
|
" maxi = wu.rad2deg(maxi)*3600\n",
|
|
" if f\"{maxi:.3g}\" == 0:\n",
|
|
" pass\n",
|
|
" return f\"{maxi:.3g}\"\n",
|
|
"\n",
|
|
"\n",
|
|
"def build_max_table(gha_prefix, title, group_value = None):\n",
|
|
" if gha_prefix==\"GHA1\":\n",
|
|
" metrics = ['dBeta [\"]', 'dLambda [\"]', 'dAlpha1 [\"]', 'time [s]']\n",
|
|
" angle_mask = [True, True, True, False]\n",
|
|
" else:\n",
|
|
" metrics = ['dAlpha0 [\"]', 'dAlpha1 [\"]', 'dStrecke [m]', 'time [s]']\n",
|
|
" angle_mask = [True, True, False, False]\n",
|
|
"\n",
|
|
" if group_value is None:\n",
|
|
" example_keys = [example_key for example_key in list(results.keys())]\n",
|
|
" else:\n",
|
|
" example_keys = [example_key for example_key, group_index in zip(results.keys(), group_indices) if group_index==group_value]\n",
|
|
" algorithms = sorted({algorithm for example_key in example_keys for algorithm in results[example_key].keys() if algorithm.startswith(gha_prefix)})\n",
|
|
"\n",
|
|
" header = [\"Algorithmus\", \"Parameter\", \"NaN\"] + list(metrics)\n",
|
|
" cells = [[] for i in range(len(metrics) + 3)]\n",
|
|
" for algorithm in algorithms:\n",
|
|
"\n",
|
|
" ghaNr, variant, params = algorithm.split(\"_\", 2)\n",
|
|
" cells[0].append(variant)\n",
|
|
" cells[1].append(params)\n",
|
|
" nan_values = []\n",
|
|
" for example_key in example_keys:\n",
|
|
" nan_values.append(results[example_key][algorithm][0])\n",
|
|
" cells[2].append(np.sum(np.isnan(nan_values)))\n",
|
|
" for i, metric in enumerate(metrics):\n",
|
|
" values = []\n",
|
|
" for example_key in example_keys:\n",
|
|
" values.append(results[example_key][algorithm][i])\n",
|
|
" cells[i+3].append(format_max(values, is_angle=angle_mask[i]))\n",
|
|
"\n",
|
|
" header = dict(\n",
|
|
" values=header,\n",
|
|
" align=\"center\",\n",
|
|
" fill_color=\"lightgrey\",\n",
|
|
" font=dict(size=13)\n",
|
|
" )\n",
|
|
" cells = dict(\n",
|
|
" values=cells,\n",
|
|
" align=\"center\"\n",
|
|
" )\n",
|
|
"\n",
|
|
" fig = go.Figure(data=[go.Table(header=header, cells=cells)])\n",
|
|
" fig.update_layout(title=title,\n",
|
|
" template=\"simple_white\",\n",
|
|
" width=800,\n",
|
|
" height=280,\n",
|
|
" margin=dict(l=20, r=20, t=60, b=20))\n",
|
|
" return fig\n",
|
|
"\n",
|
|
"figs = []\n",
|
|
"if test == \"Panou\" or test == \"Karney\":\n",
|
|
" for group_index in sorted(set(group_indices)):\n",
|
|
" fig1 = build_max_table(\"GHA1\", f\"{test} - Gruppe {group_index} - GHA1\", group_index)\n",
|
|
" fig2 = build_max_table(\"GHA2\", f\"{test} - Gruppe {group_index} - GHA2\", group_index)\n",
|
|
" figs.append(fig1)\n",
|
|
" figs.append(fig2)\n",
|
|
"else:\n",
|
|
" fig1 = build_max_table(\"GHA1\", f\"{test} - GHA1\")\n",
|
|
" fig2 = build_max_table(\"GHA2\", f\"{test} - GHA2\")\n",
|
|
" figs.append(fig1)\n",
|
|
" figs.append(fig2)\n",
|
|
"\n",
|
|
"# fig1 = build_max_table(\"GHA1\", f\"{test} - GHA1\")\n",
|
|
"# fig2 = build_max_table(\"GHA2\", f\"{test} - GHA2\")\n",
|
|
"# figs.append(fig1)\n",
|
|
"# figs.append(fig2)\n",
|
|
"\n",
|
|
"for fig in figs:\n",
|
|
" fig.show()"
|
|
],
|
|
"id": "eeb5a204cc4bbf7d",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"5\n",
|
|
"1\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_7832\\3781407476.py:6: RuntimeWarning:\n",
|
|
"\n",
|
|
"All-NaN slice encountered\n",
|
|
"\n",
|
|
"C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_7832\\3781407476.py:6: RuntimeWarning:\n",
|
|
"\n",
|
|
"All-NaN slice encountered\n",
|
|
"\n",
|
|
"C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_7832\\3781407476.py:6: RuntimeWarning:\n",
|
|
"\n",
|
|
"All-NaN slice encountered\n",
|
|
"\n",
|
|
"C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_7832\\3781407476.py:6: RuntimeWarning:\n",
|
|
"\n",
|
|
"All-NaN slice encountered\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"data": [
|
|
{
|
|
"cells": {
|
|
"align": "center",
|
|
"values": [
|
|
[
|
|
"ES",
|
|
"ES",
|
|
"ES",
|
|
"ana",
|
|
"ana",
|
|
"ana",
|
|
"ana",
|
|
"ana",
|
|
"ana",
|
|
"approx",
|
|
"approx",
|
|
"approx",
|
|
"num",
|
|
"num",
|
|
"num"
|
|
],
|
|
[
|
|
"1250",
|
|
"60",
|
|
"600",
|
|
"20_16",
|
|
"20_64",
|
|
"20_8",
|
|
"60_16",
|
|
"60_64",
|
|
"60_8",
|
|
"1250",
|
|
"30000",
|
|
"6000",
|
|
"1000",
|
|
"10000",
|
|
"50000"
|
|
],
|
|
[
|
|
0,
|
|
0,
|
|
0,
|
|
0,
|
|
0,
|
|
4,
|
|
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|
0,
|
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2,
|
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|
|
0,
|
|
0,
|
|
0,
|
|
0
|
|
],
|
|
[
|
|
"9.62e+04",
|
|
"4.95e+04",
|
|
"7.42e+04",
|
|
"0.00999",
|
|
"2.73e-09",
|
|
"8.89e-05",
|
|
"8.93e-10",
|
|
"2.73e-09",
|
|
"0.042",
|
|
"8.27e+03",
|
|
"1.72e+05",
|
|
"1.72e+05",
|
|
"0.373",
|
|
"0.373",
|
|
"0.373"
|
|
],
|
|
[
|
|
"3.28e+04",
|
|
"6.27e+04",
|
|
"8.06e+04",
|
|
"0.0177",
|
|
"3.46e-09",
|
|
"2.23e-05",
|
|
"1.2e-09",
|
|
"3.46e-09",
|
|
"0.00507",
|
|
"9.92e+03",
|
|
"1.16e+05",
|
|
"1.16e+05",
|
|
"0.547",
|
|
"0.547",
|
|
"0.547"
|
|
],
|
|
[
|
|
"2.18e+04",
|
|
"7.32e+04",
|
|
"1.08e+05",
|
|
"0.0327",
|
|
"3.66e-09",
|
|
"1.47e-05",
|
|
"1.47e-09",
|
|
"3.66e-09",
|
|
"0.839",
|
|
"1.05e+04",
|
|
"2.74e+05",
|
|
"2.74e+05",
|
|
"0.853",
|
|
"0.853",
|
|
"0.853"
|
|
],
|
|
[
|
|
"57.1",
|
|
"4.12",
|
|
"32.3",
|
|
"0.0606",
|
|
"0.248",
|
|
"0.0308",
|
|
"1.02",
|
|
"4.01",
|
|
"0.502",
|
|
"1.61",
|
|
"38.7",
|
|
"7.43",
|
|
"0.0388",
|
|
"0.413",
|
|
"2.08"
|
|
]
|
|
]
|
|
},
|
|
"header": {
|
|
"align": "center",
|
|
"fill": {
|
|
"color": "lightgrey"
|
|
},
|
|
"font": {
|
|
"size": 13
|
|
},
|
|
"values": [
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"end_time": "2026-02-09T09:19:29.235114Z",
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"start_time": "2026-02-09T09:19:29.040806Z"
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"cell_type": "code",
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"source": [
|
|
"# Latex-Tabelle ausgeben\n",
|
|
"from collections import defaultdict\n",
|
|
"\n",
|
|
"def to_latex_sci(x, decimals=3, exp_digits=2):\n",
|
|
" \"\"\"\n",
|
|
" Immer wissenschaftliche Schreibweise in LaTeX mit:\n",
|
|
" - fester Mantisse (z.B. 1.234)\n",
|
|
" - Exponent immer mit Vorzeichen (+/-)\n",
|
|
" - Exponent immer feste Stellenzahl (z.B. +03, -01)\n",
|
|
"\n",
|
|
" Beispiel:\n",
|
|
" 0.216 -> $2.160\\\\cdot10^{-01}$\n",
|
|
" 12.3 -> $1.230\\\\cdot10^{+01}$\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" if x is None:\n",
|
|
" return \"nan\"\n",
|
|
" try:\n",
|
|
" xf = float(x)\n",
|
|
" except Exception:\n",
|
|
" return str(x)\n",
|
|
"\n",
|
|
" if math.isnan(xf):\n",
|
|
" return \"nan\"\n",
|
|
"\n",
|
|
" if xf == 0.0:\n",
|
|
" exp_fmt = f\"+{0:0{exp_digits}d}\"\n",
|
|
" mant = f\"{0:.{decimals}f}\"\n",
|
|
" return f\"${mant}\\\\cdot10^{{{exp_fmt}}}$\"\n",
|
|
"\n",
|
|
" s = f\"{xf:.{decimals}e}\" # z.B. 2.160e-01\n",
|
|
" mant, exp = s.split(\"e\")\n",
|
|
" exp = int(exp)\n",
|
|
"\n",
|
|
" # Exponent formatieren: Vorzeichen + feste Länge\n",
|
|
" sign = \"+\" if exp >= 0 else \"-\"\n",
|
|
" exp_abs = abs(exp)\n",
|
|
" exp_fmt = f\"{sign}{exp_abs:0{exp_digits}d}\"\n",
|
|
"\n",
|
|
" return f\"${mant}\\\\cdot10^{{{exp_fmt}}}$\"\n",
|
|
"\n",
|
|
"\n",
|
|
"def nan_count_for_algorithm(results, example_keys, algorithm):\n",
|
|
" vals = []\n",
|
|
" for k in example_keys:\n",
|
|
" vals.append(results[k][algorithm][0])\n",
|
|
" return int(np.sum(np.isnan(np.array(vals, dtype=float))))\n",
|
|
"\n",
|
|
"\n",
|
|
"def max_abs_metric(results, example_keys, algorithm, metric_index, is_angle=False, wu=None, mask=None):\n",
|
|
" \"\"\"\n",
|
|
" Echter Max(|...|) über alle example_keys.\n",
|
|
" Wenn is_angle=True: Werte werden als rad angenommen und in Bogensekunden umgerechnet.\n",
|
|
" \"\"\"\n",
|
|
" arr = []\n",
|
|
" for i, k in enumerate(example_keys):\n",
|
|
" if mask is not None and not mask[i]:\n",
|
|
" continue\n",
|
|
" arr.append(results[k][algorithm][metric_index])\n",
|
|
" arr = np.array(arr, dtype=float)\n",
|
|
"\n",
|
|
" if arr.size == 0:\n",
|
|
" return np.nan\n",
|
|
" m = np.nanmax(np.abs(arr))\n",
|
|
" if is_angle:\n",
|
|
" if wu is None:\n",
|
|
" raise ValueError(\"wu wird benötigt für rad2deg, wenn is_angle=True\")\n",
|
|
" m = wu.rad2deg(m) * 3600.0\n",
|
|
" return m\n",
|
|
"\n",
|
|
"\n",
|
|
"def parse_variant_params(algorithm_key):\n",
|
|
" \"\"\"\n",
|
|
" Erwartet: GHA1_ana_20_4 -> variant='ana', params='20_4'\n",
|
|
" Gibt zusätzlich eine hübsche Parameterdarstellung zurück.\n",
|
|
" \"\"\"\n",
|
|
" ghaNr, variant, params = algorithm_key.split(\"_\", 2)\n",
|
|
"\n",
|
|
" # Standard: params 그대로\n",
|
|
" pretty = params\n",
|
|
"\n",
|
|
" # Für deine Tabellen: ana hat z.B. '20_4' -> '4, 20' (Segmentgröße, Ordnung)\n",
|
|
" if variant == \"ana\":\n",
|
|
" # bei dir: params = '20_4' oder '50_8' -> Ordnung_Segment\n",
|
|
" # du willst aber: Segment, Ordnung -> '4, 20'\n",
|
|
" a, b = params.split(\"_\")\n",
|
|
" order = a\n",
|
|
" seg = b\n",
|
|
" pretty = f\"{seg}, {order}\"\n",
|
|
" else:\n",
|
|
" # num: '2000' bleibt '2000'\n",
|
|
" # approx/ES: oft eine Zahl mit Punkt/Unterstrich? -> 그대로\n",
|
|
" pretty = params.replace(\"_\", \", \")\n",
|
|
"\n",
|
|
" return variant, params, pretty\n",
|
|
"\n",
|
|
"\n",
|
|
"def build_latex_table_from_results(\n",
|
|
" results,\n",
|
|
" gha_prefix=\"GHA1\",\n",
|
|
" example_keys=None,\n",
|
|
" caption=\"\",\n",
|
|
" label=\"tab:results_algorithms\",\n",
|
|
" include_nan_col=False,\n",
|
|
" wu=None\n",
|
|
"):\n",
|
|
" \"\"\"\n",
|
|
" Erzeugt LaTeX Tabular (inkl. table-Umgebung) im gewünschten Stil.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" if example_keys is None:\n",
|
|
" example_keys = list(results.keys())\n",
|
|
"\n",
|
|
" # Metriken & Winkel-Maske wie bei dir\n",
|
|
" if gha_prefix == \"GHA1\":\n",
|
|
" metric_headers = [\n",
|
|
" r\"$\\max(|\\Delta \\beta|)$ [$''$]\",\n",
|
|
" r\"$\\max(|\\Delta \\lambda|)$ [$''$]\",\n",
|
|
" r\"$\\max(|\\Delta \\alpha_1|)$ [$''$]\",\n",
|
|
" r\"time [s]\"\n",
|
|
" ]\n",
|
|
" angle_mask = [True, True, True, False]\n",
|
|
" else:\n",
|
|
" metric_headers = [\n",
|
|
" r\"$\\max(|\\Delta \\alpha_0|)$ [$''$]\",\n",
|
|
" r\"$\\max(|\\Delta \\alpha_1|)$ [$''$]\",\n",
|
|
" r\"$\\max(|\\Delta s|)$ [m]\",\n",
|
|
" r\"time [s]\"\n",
|
|
" ]\n",
|
|
" angle_mask = [True, True, False, False]\n",
|
|
"\n",
|
|
" # Alle Algorithmen sammeln\n",
|
|
" algorithms = sorted({\n",
|
|
" alg for k in example_keys\n",
|
|
" for alg in results[k].keys()\n",
|
|
" if alg.startswith(gha_prefix)\n",
|
|
" })\n",
|
|
"\n",
|
|
" # Gruppieren nach variant (ana, num, approx, ES, ...)\n",
|
|
" grouped = defaultdict(list)\n",
|
|
" for alg in algorithms:\n",
|
|
" variant, raw_params, pretty = parse_variant_params(alg)\n",
|
|
" grouped[variant].append((alg, pretty))\n",
|
|
"\n",
|
|
" # gewünschte Reihenfolge (falls vorhanden)\n",
|
|
" variant_order = [\"ana\", \"num\", \"approx\", \"ES\"]\n",
|
|
" ordered_variants = [v for v in variant_order if v in grouped] + [v for v in grouped.keys() if v not in variant_order]\n",
|
|
"\n",
|
|
" # LaTeX Header\n",
|
|
" cols = 2 + (1 if include_nan_col else 0) + len(metric_headers)\n",
|
|
" colspec = \"|\" + \"|\".join([\"c\"] * cols) + \"|\"\n",
|
|
"\n",
|
|
" header_cells = [\"Methode\", \"Parameter\"]\n",
|
|
" if include_nan_col:\n",
|
|
" header_cells.append(\"NaN\")\n",
|
|
" header_cells += metric_headers\n",
|
|
"\n",
|
|
" lines = []\n",
|
|
" lines.append(r\"\\begin{table}[H]\")\n",
|
|
" lines.append(r\"\\centering\")\n",
|
|
" if caption:\n",
|
|
" lines.append(rf\"\\caption{{{caption}}}\")\n",
|
|
" lines.append(rf\"\\label{{{label}}}\")\n",
|
|
" lines.append(rf\"\\begin{{tabular}}{{{colspec}}}\")\n",
|
|
" lines.append(r\"\\hline\")\n",
|
|
" lines.append(\" & \".join(header_cells) + r\" \\\\\")\n",
|
|
" lines.append(r\"\\Xhline{1.5pt}\")\n",
|
|
"\n",
|
|
" # Zeilen bauen\n",
|
|
" for variant in ordered_variants:\n",
|
|
" rows = grouped[variant]\n",
|
|
" # für stable output: sort by pretty param (numerisch)\n",
|
|
" # (du kannst das ändern, wenn du eine andere Reihenfolge willst)\n",
|
|
" def sort_key(t):\n",
|
|
" _, pretty = t\n",
|
|
" # versuche numerisch zu sortieren\n",
|
|
" try:\n",
|
|
" parts = [float(p.strip()) for p in pretty.split(\",\")]\n",
|
|
" return parts\n",
|
|
" except Exception:\n",
|
|
" return [pretty]\n",
|
|
" rows = sorted(rows, key=sort_key)\n",
|
|
"\n",
|
|
" multi_n = len(rows)\n",
|
|
" method_name = {\n",
|
|
" \"ana\": \"analytisch\",\n",
|
|
" \"num\": \"numerisch\",\n",
|
|
" \"approx\": \"approximiert\"\n",
|
|
" }.get(variant, variant)\n",
|
|
"\n",
|
|
" for idx, (alg, pretty_param) in enumerate(rows):\n",
|
|
" row_cells = []\n",
|
|
"\n",
|
|
" if multi_n > 1:\n",
|
|
" if idx == 0:\n",
|
|
" row_cells.append(rf\"\\multirow{{{multi_n}}}{{*}}{{{method_name}}}\")\n",
|
|
" else:\n",
|
|
" row_cells.append(\"\") # Multirow fortsetzen\n",
|
|
" else:\n",
|
|
" row_cells.append(method_name)\n",
|
|
"\n",
|
|
" row_cells.append(pretty_param)\n",
|
|
"\n",
|
|
" if include_nan_col:\n",
|
|
" nans = nan_count_for_algorithm(results, example_keys, alg)\n",
|
|
" row_cells.append(str(nans))\n",
|
|
"\n",
|
|
" # Metriken\n",
|
|
" for mi in range(len(metric_headers)):\n",
|
|
" other_GL_mask = None\n",
|
|
" if gha_prefix == \"GHA2\" and variant == \"num\":\n",
|
|
" other_GL_mask = [\n",
|
|
" (\n",
|
|
" np.isfinite(results[k][alg][2]) and\n",
|
|
" abs(results[k][alg][2]) <= 10.0\n",
|
|
" )\n",
|
|
" for k in example_keys\n",
|
|
" ]\n",
|
|
" if other_GL_mask is not None:\n",
|
|
" print(f\"{alg}: {other_GL_mask.count(False)} falsche Linien\")\n",
|
|
" m = max_abs_metric(results, example_keys, alg, mi, is_angle=angle_mask[mi], wu=wu, mask=other_GL_mask)\n",
|
|
" if mi == 3:\n",
|
|
" row_cells.append(f\"${m:.2f}$\")\n",
|
|
" else:\n",
|
|
" row_cells.append(to_latex_sci(m))\n",
|
|
"\n",
|
|
" # Zeile + Linienlogik wie in deinem Beispiel\n",
|
|
" latex_row = \" & \".join(row_cells) + r\" \\\\\"\n",
|
|
"\n",
|
|
" # nach letzter Zeile einer Methode eine \\hline (wie bei dir)\n",
|
|
" if idx == multi_n - 1:\n",
|
|
" latex_row += r\"\\hline\"\n",
|
|
" lines.append(latex_row)\n",
|
|
"\n",
|
|
" lines.append(r\"\\end{tabular}\")\n",
|
|
" lines.append(r\"\\end{table}\")\n",
|
|
" lines.append(r\"\\noindent\")\n",
|
|
"\n",
|
|
" return \"\\n\".join(lines)"
|
|
],
|
|
"id": "12591c3b707da905",
|
|
"outputs": [],
|
|
"execution_count": 31
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2026-02-09T09:19:47.264504Z",
|
|
"start_time": "2026-02-09T09:19:47.022633Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"example_keys = list(key for i, key in enumerate(results.keys()))\n",
|
|
"group = None\n",
|
|
"# group = \"a\"\n",
|
|
"# example_keys = list(key for i, key in enumerate(results.keys()) if group_indices[i] == group)\n",
|
|
"gha = \"GHA2\"\n",
|
|
"latex = build_latex_table_from_results(\n",
|
|
" results,\n",
|
|
" gha_prefix=gha,\n",
|
|
" example_keys=example_keys,\n",
|
|
" caption=f\"Ergebnisse der Lösungsmethoden der {gha[3]}. GHA ({test})\" if group is None else f\"Ergebnisse der Lösungsmethoden der {gha[3]}. GHA ({test}, Gruppe {group})\",\n",
|
|
" label=f\"tab:results_{test}_{gha}\" if group is None else f\"tab:results_{test}_{gha}_{group}\",\n",
|
|
" include_nan_col=True,\n",
|
|
" wu=wu\n",
|
|
")\n",
|
|
"print(latex)"
|
|
],
|
|
"id": "44f20396e6c5493c",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"GHA2_num_200: 22 falsche Linien\n",
|
|
"GHA2_num_200: 22 falsche Linien\n",
|
|
"GHA2_num_200: 22 falsche Linien\n",
|
|
"GHA2_num_200: 22 falsche Linien\n",
|
|
"GHA2_num_500: 21 falsche Linien\n",
|
|
"GHA2_num_500: 21 falsche Linien\n",
|
|
"GHA2_num_500: 21 falsche Linien\n",
|
|
"GHA2_num_500: 21 falsche Linien\n",
|
|
"GHA2_num_1000: 19 falsche Linien\n",
|
|
"GHA2_num_1000: 19 falsche Linien\n",
|
|
"GHA2_num_1000: 19 falsche Linien\n",
|
|
"GHA2_num_1000: 19 falsche Linien\n",
|
|
"GHA2_num_5000: 19 falsche Linien\n",
|
|
"GHA2_num_5000: 19 falsche Linien\n",
|
|
"GHA2_num_5000: 19 falsche Linien\n",
|
|
"GHA2_num_5000: 19 falsche Linien\n",
|
|
"GHA2_num_10000: 19 falsche Linien\n",
|
|
"GHA2_num_10000: 19 falsche Linien\n",
|
|
"GHA2_num_10000: 19 falsche Linien\n",
|
|
"GHA2_num_10000: 19 falsche Linien\n",
|
|
"\\begin{table}[H]\n",
|
|
"\\centering\n",
|
|
"\\caption{Ergebnisse der Lösungsmethoden der 2. GHA (Random)}\n",
|
|
"\\label{tab:results_Random_GHA2}\n",
|
|
"\\begin{tabular}{|c|c|c|c|c|c|c|}\n",
|
|
"\\hline\n",
|
|
"Methode & Parameter & NaN & $\\max(|\\Delta \\alpha_0|)$ [$''$] & $\\max(|\\Delta \\alpha_1|)$ [$''$] & $\\max(|\\Delta s|)$ [m] & time [s] \\\\\n",
|
|
"\\Xhline{1.5pt}\n",
|
|
"\\multirow{5}{*}{numerisch} & 200 & 4 & $1.490\\cdot10^{-02}$ & $1.276\\cdot10^{-02}$ & $1.317\\cdot10^{+00}$ & $0.21$ \\\\\n",
|
|
" & 500 & 3 & $3.833\\cdot10^{-02}$ & $4.408\\cdot10^{-02}$ & $1.355\\cdot10^{+00}$ & $0.52$ \\\\\n",
|
|
" & 1000 & 3 & $3.160\\cdot10^{-02}$ & $2.663\\cdot10^{-02}$ & $3.863\\cdot10^{+00}$ & $1.02$ \\\\\n",
|
|
" & 5000 & 3 & $1.275\\cdot10^{-04}$ & $2.077\\cdot10^{-05}$ & $6.141\\cdot10^{-02}$ & $4.67$ \\\\\n",
|
|
" & 10000 & 3 & $1.275\\cdot10^{-04}$ & $1.036\\cdot10^{-04}$ & $1.108\\cdot10^{-01}$ & $5.16$ \\\\\\hline\n",
|
|
"\\multirow{3}{*}{approximiert} & 600 & 0 & $8.033\\cdot10^{+03}$ & $8.043\\cdot10^{+03}$ & $1.730\\cdot10^{+02}$ & $0.18$ \\\\\n",
|
|
" & 1250 & 0 & $8.033\\cdot10^{+03}$ & $8.043\\cdot10^{+03}$ & $1.744\\cdot10^{+02}$ & $0.37$ \\\\\n",
|
|
" & 6000 & 0 & $8.033\\cdot10^{+03}$ & $8.043\\cdot10^{+03}$ & $1.749\\cdot10^{+02}$ & $2.61$ \\\\\\hline\n",
|
|
"\\multirow{3}{*}{ES} & 60 & 0 & $7.167\\cdot10^{+03}$ & $7.166\\cdot10^{+03}$ & $1.526\\cdot10^{+02}$ & $4.93$ \\\\\n",
|
|
" & 600 & 0 & $7.167\\cdot10^{+03}$ & $7.166\\cdot10^{+03}$ & $1.370\\cdot10^{+02}$ & $39.08$ \\\\\n",
|
|
" & 1250 & 0 & $7.167\\cdot10^{+03}$ & $7.166\\cdot10^{+03}$ & $1.384\\cdot10^{+02}$ & $73.40$ \\\\\\hline\n",
|
|
"\\end{tabular}\n",
|
|
"\\end{table}\n",
|
|
"\\noindent\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 33
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|