diff --git a/GHA_triaxial/numeric_examples_karney.py b/GHA_triaxial/numeric_examples_karney.py index e17ed57..ba204a1 100644 --- a/GHA_triaxial/numeric_examples_karney.py +++ b/GHA_triaxial/numeric_examples_karney.py @@ -1,6 +1,11 @@ import random import winkelumrechnungen as wu from typing import List, Tuple +import numpy as np +from ellipsoide import EllipsoidTriaxial +from GHA_triaxial.gha1_ES import jacobi_konstante + +ell = EllipsoidTriaxial.init_name("KarneyTest2024") def line2example(line: str) -> List: """ @@ -49,6 +54,116 @@ def get_examples(l_i: List) -> List: examples.append(example) return examples +# beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s + +def get_random_examples_simple_short(num: int, seed: int = None) -> List: + if seed is not None: + random.seed(seed) + with open(r"C:\Users\moell\OneDrive\Desktop\Vorlesungen\Master-Projekt\Python_Masterprojekt\GHA_triaxial\Karney_2024_Testset.txt") as datei: + lines = datei.readlines() + examples = [] + while len(examples) < num: + example = line2example(lines[random.randint(0, len(lines) - 1)]) + beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example + if s < 1 and abs(abs(beta0) - np.pi/2) > 1e-5 and lamb0 != 0 and abs(abs(lamb0) - np.pi) > 1e-5: + examples.append(example) + return examples + +def get_random_examples_umbilics_start(num: int, seed: int = None) -> List: + if seed is not None: + random.seed(seed) + with open(r"C:\Users\moell\OneDrive\Desktop\Vorlesungen\Master-Projekt\Python_Masterprojekt\GHA_triaxial\Karney_2024_Testset.txt") as datei: + lines = datei.readlines() + examples = [] + while len(examples) < num: + example = line2example(lines[random.randint(0, len(lines) - 1)]) + beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example + if abs(abs(beta0) - np.pi/2) < 1e-5 and (lamb0 == 0 or abs(abs(lamb0) - np.pi) < 1e-5): + examples.append(example) + return examples + +def get_random_examples_umbilics_end(num: int, seed: int = None) -> List: + if seed is not None: + random.seed(seed) + with open(r"C:\Users\moell\OneDrive\Desktop\Vorlesungen\Master-Projekt\Python_Masterprojekt\GHA_triaxial\Karney_2024_Testset.txt") as datei: + lines = datei.readlines() + examples = [] + while len(examples) < num: + example = line2example(lines[random.randint(0, len(lines) - 1)]) + beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example + if abs(abs(beta1) - np.pi/2) < 1e-5 and (lamb1 == 0 or abs(abs(lamb1) - np.pi) < 1e-5): + examples.append(example) + return examples + +def get_random_examples_gamma(group: str, num: int, seed: int = None, length: str = None) -> List: + eps = 1e-20 + long_short = 1.5 + if seed is not None: + random.seed(seed) + with open(r"C:\Users\moell\OneDrive\Desktop\Vorlesungen\Master-Projekt\Python_Masterprojekt\GHA_triaxial\Karney_2024_Testset.txt") as datei: + lines = datei.readlines() + examples = [] + while len(examples) < num: + example = line2example(lines[random.randint(0, len(lines) - 1)]) + beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example + gamma = jacobi_konstante(beta0, lamb0, alpha0_ell, ell) + if group == "a": + if 1 >= gamma >= 0.1: + if length == "short": + if example[6] < long_short: + examples.append(example) + elif length == "long": + if example[6] >= long_short: + examples.append(example) + else: + examples.append(example) + if group == "b": + if 0.1 > gamma > eps: + if length == "short": + if example[6] < long_short: + examples.append(example) + elif length == "long": + if example[6] >= long_short: + examples.append(example) + else: + examples.append(example) + if group == "c": + if abs(gamma) <= eps: + if length == "short": + if example[6] < long_short: + examples.append(example) + elif length == "long": + if example[6] >= long_short: + examples.append(example) + else: + examples.append(example) + if group == "de": + if eps > gamma > -1e-17: + if length == "short": + if example[6] < long_short: + examples.append(example) + elif length == "long": + if example[6] >= long_short: + examples.append(example) + else: + examples.append(example) + if group == "e": + if -1e-17 >= gamma >= -1: + if length == "short": + if example[6] < long_short: + examples.append(example) + elif length == "long": + if example[6] >= long_short: + examples.append(example) + else: + examples.append(example) + return examples + if __name__ == "__main__": - get_random_examples(10) \ No newline at end of file + examples_a = get_random_examples_gamma("a", 10, 42) + examples_b = get_random_examples_gamma("b", 10, 42) + examples_c = get_random_examples_gamma("c", 10, 42) + examples_d = get_random_examples_gamma("d", 10, 42) + examples_e = get_random_examples_gamma("e", 10, 42) + pass \ No newline at end of file diff --git a/Tests/algorithms_test.ipynb b/Tests/algorithms_test.ipynb index c15f166..a69ec18 100644 --- a/Tests/algorithms_test.ipynb +++ b/Tests/algorithms_test.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-08T14:37:43.639183Z", - "start_time": "2026-02-08T14:37:41.363837Z" + "end_time": "2026-02-08T18:12:51.803705Z", + "start_time": "2026-02-08T18:12:51.310195Z" } }, "cell_type": "code", @@ -14,13 +14,13 @@ ], "id": "89aa93e9dbedd113", "outputs": [], - "execution_count": 1 + "execution_count": 2 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-08T14:37:44.991629Z", - "start_time": "2026-02-08T14:37:43.654255Z" + "end_time": "2026-02-08T18:12:54.141970Z", + "start_time": "2026-02-08T18:12:52.406964Z" } }, "cell_type": "code", @@ -52,17 +52,18 @@ "from GHA_triaxial.gha2_approx import gha2_approx\n", "\n", "from GHA_triaxial.numeric_examples_panou import get_tables as get_tables_panou\n", - "from GHA_triaxial.numeric_examples_karney import get_random_examples as get_examples_karney" + "from GHA_triaxial.numeric_examples_karney import get_random_examples as get_examples_karney\n", + "from GHA_triaxial.numeric_examples_karney import get_random_examples_gamma as get_examples_karney_gamma" ], "id": "2005e5a8854eea1e", "outputs": [], - "execution_count": 2 + "execution_count": 3 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-08T09:56:48.946285Z", - "start_time": "2026-02-08T09:56:48.479496Z" + "end_time": "2026-02-08T18:12:55.013436Z", + "start_time": "2026-02-08T18:12:54.788777Z" } }, "cell_type": "code", @@ -75,15 +76,10 @@ ], "id": "90f107a11ff0de7e", "outputs": [], - "execution_count": 3 + "execution_count": 4 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-07T21:21:03.628386Z", - "start_time": "2026-02-07T21:21:03.410822Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n", @@ -100,33 +96,39 @@ ], "id": "fc6c39b8d358e54b", "outputs": [], - "execution_count": 7 + "execution_count": null }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-07T21:22:30.400129Z", - "start_time": "2026-02-07T21:22:30.193713Z" + "end_time": "2026-02-08T18:27:32.626200Z", + "start_time": "2026-02-08T18:27:28.458773Z" } }, "cell_type": "code", "source": [ - "test = \"Karney\"\n", - "# test = \"Panou\"\n", - "# test = \"Random\"\n", - "\n", "def build_examples(test):\n", " if test == \"Karney\":\n", " ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"KarneyTest2024\")\n", - " examples = get_examples_karney(20, seed=42)\n", + " num = 1\n", + " seed = 42\n", + " groups = [\"a_short\", \"a_long\", \"b_short\", \"b_long\", \"c_short\", \"c_long\", \"de_short\", \"de_long\"]\n", + " group_indices = []\n", + " examples = []\n", + " for group in groups:\n", + " split = group.split(\"_\")\n", + " examples_group = get_examples_karney_gamma(split[0], num, seed, split[1])\n", + " examples.extend(examples_group)\n", + " group_indices.extend([group] * num)\n", + "\n", " elif test == \"Panou\":\n", " ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"BursaSima1980round\")\n", " tables = get_tables_panou()\n", - " table_indices = []\n", + " group_indices = []\n", " examples = []\n", " for i, table in enumerate(tables):\n", " for example in table:\n", - " table_indices.append(i+1)\n", + " group_indices.append(i+1)\n", " examples.append(example)\n", " examples = examples[:14]\n", " elif test == \"Random\":\n", @@ -139,19 +141,23 @@ " alpha0_ell = wu.deg2rad(random.randint(0, 359))\n", " s = random.randint(10000, int(np.pi*ell.b))\n", " examples.append([beta0, lamb0, alpha0_ell, s])\n", - " pass\n", - " return ell, examples\n", - "# ell, examples = build_examples(test)" + " group_indices = None\n", + " return ell, examples, group_indices\n", + "test = \"Karney\"\n", + "# test = \"Panou\"\n", + "# test = \"Random\"\n", + "ell, examples, group_indices = build_examples(test)\n", + "pass" ], "id": "6770dbd57d475127", "outputs": [], - "execution_count": 15 + "execution_count": 23 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-07T21:22:03.254266Z", - "start_time": "2026-02-07T21:22:03.046527Z" + "end_time": "2026-02-08T18:15:50.911578Z", + "start_time": "2026-02-08T18:15:50.683503Z" } }, "cell_type": "code", @@ -188,39 +194,39 @@ " print(\"Referenz-Berechnung numerisch fehlgeschlagen: \", e)\n", " continue\n", "\n", - " for steps in steps_gha1_num:\n", - " start = time.perf_counter()\n", - " try:\n", - " P1_num, alpha1_num_1 = gha1_num(ell, P0, alpha0_ell, s, num=steps)\n", - " end = time.perf_counter()\n", - " beta1_num, lamb1_num = ell.cart2ell(P1_num)\n", - " d_beta1 = abs(beta1_num - beta1)\n", - " d_lamb1 = abs(lamb1_num - lamb1)\n", - " d_alpha1 = abs(alpha1_num_1 - alpha1_ell)\n", - " d_time = end - start\n", - " example_results[f\"GHA1_num_{steps}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n", - " except Exception as e:\n", - " print(e)\n", - " example_results[f\"GHA1_num_{steps}\"] = (nan, nan, nan, nan)\n", + " # for steps in steps_gha1_num:\n", + " # start = time.perf_counter()\n", + " # try:\n", + " # P1_num, alpha1_num_1 = gha1_num(ell, P0, alpha0_ell, s, num=steps)\n", + " # end = time.perf_counter()\n", + " # beta1_num, lamb1_num = ell.cart2ell(P1_num)\n", + " # d_beta1 = abs(beta1_num - beta1)\n", + " # d_lamb1 = abs(lamb1_num - lamb1)\n", + " # d_alpha1 = abs(alpha1_num_1 - alpha1_ell)\n", + " # d_time = end - start\n", + " # example_results[f\"GHA1_num_{steps}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n", + " # except Exception as e:\n", + " # print(e)\n", + " # example_results[f\"GHA1_num_{steps}\"] = (nan, nan, nan, nan)\n", + "\n", + " for maxM in maxM_gha1_ana:\n", + " for parts in parts_gha1_ana:\n", + " start = time.perf_counter()\n", + " try:\n", + " P1_ana, alpha1_ana_para = gha1_ana(ell, P0, alpha0_para, s, maxM=maxM, maxPartCircum=parts)\n", + " end = time.perf_counter()\n", + " beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)\n", + " u1_ana, v1_ana = ell.cart2para(P1_ana)\n", + " _, _, alpha1_ana_ell = alpha_para2ell(ell, u1_ana, v1_ana, alpha1_ana_para)\n", + " d_beta1 = abs(beta1_ana - beta1)\n", + " d_lamb1 = abs(lamb1_ana - lamb1)\n", + " d_alpha1 = abs(alpha1_ana_ell - alpha1_ell)\n", + " d_time = end - start\n", + " example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n", + " except Exception as e:\n", + " print(e)\n", + " example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (nan, nan, nan, nan)\n", "\n", - " # for maxM in maxM_gha1_ana:\n", - " # for parts in parts_gha1_ana:\n", - " # start = time.perf_counter()\n", - " # try:\n", - " # P1_ana, alpha1_ana_para = gha1_ana(ell, P0, alpha0_para, s, maxM=maxM, maxPartCircum=parts)\n", - " # end = time.perf_counter()\n", - " # beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)\n", - " # u1_ana, v1_ana = ell.cart2para(P1_ana)\n", - " # _, _, alpha1_ana_ell = alpha_para2ell(ell, u1_ana, v1_ana, alpha1_ana_para)\n", - " # d_beta1 = abs(beta1_ana - beta1)\n", - " # d_lamb1 = abs(lamb1_ana - lamb1)\n", - " # d_alpha1 = abs(alpha1_ana_ell - alpha1_ell)\n", - " # d_time = end - start\n", - " # example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n", - " # except Exception as e:\n", - " # print(e)\n", - " # example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (nan, nan, nan, nan)\n", - " #\n", " # for dsPart in dsPart_gha1_ES:\n", " # ds = ell.ax/dsPart\n", " # start = time.perf_counter()\n", @@ -309,13 +315,13 @@ ], "id": "fc45e0f618a0e4d8", "outputs": [], - "execution_count": 12 + "execution_count": null }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-07T21:22:06.184091Z", - "start_time": "2026-02-07T21:22:05.991850Z" + "end_time": "2026-02-08T18:20:36.069640Z", + "start_time": "2026-02-08T18:20:35.873194Z" } }, "cell_type": "code", @@ -327,32 +333,56 @@ ], "id": "74fbd4d33c288839", "outputs": [], - "execution_count": 13 + "execution_count": 10 }, { "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ + "# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n", + "\n", + "steps_gha1_num = [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 = \"Panou\"\n", "ell_panou, examples_panou = build_examples(test)\n", "results_panou = execute_results(test, ell_panou, examples_panou)\n", "save_results(test, results_panou)" ], - "id": "58697308664fa539" + "id": "58697308664fa539", + "outputs": [], + "execution_count": null }, { "metadata": { "ExecuteTime": { - "end_time": "2026-02-07T21:31:04.829637Z", - "start_time": "2026-02-07T21:29:14.982823Z" + "end_time": "2026-02-08T18:28:33.883389Z", + "start_time": "2026-02-08T18:27:55.460398Z" } }, "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 = [1000, 10000, 50000]\n", + "maxM_gha1_ana = [20, 60]\n", + "parts_gha1_ana = [8, 16, 64]\n", + "dsPart_gha1_ES = [60, 600, 1250]\n", + "dsPart_gha1_approx = [1250, 6000, 30000]\n", + "\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 = build_examples(test)\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)" ], @@ -362,104 +392,73 @@ "name": "stdout", "output_type": "stream", "text": [ - "----- Beispiel 1/20\n", - "----- Beispiel 2/20\n", - "----- Beispiel 3/20\n", - "----- Beispiel 4/20\n", - "----- Beispiel 5/20\n", - "----- Beispiel 6/20\n", - "----- Beispiel 7/20\n", - "----- Beispiel 8/20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\moell\\OneDrive\\Desktop\\Vorlesungen\\Master-Projekt\\Python_Masterprojekt\\GHA_triaxial\\utils.py:138: RuntimeWarning: invalid value encountered in sqrt\n", - " t2 = (-c1 + sqrt(c1 ** 2 - 4 * c0)) / 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----- Beispiel 9/20\n", - "----- Beispiel 10/20\n", - "----- Beispiel 11/20\n", - "----- Beispiel 12/20\n", - "----- Beispiel 13/20\n", - "----- Beispiel 14/20\n", - "----- Beispiel 15/20\n", - "----- Beispiel 16/20\n", - "----- Beispiel 17/20\n", - "----- Beispiel 18/20\n", - "----- Beispiel 19/20\n", - "----- Beispiel 20/20\n" + "----- Beispiel 1/10\n", + "----- Beispiel 2/10\n", + "Umrechnung cart2ell: Punktdifferenz\n", + "----- Beispiel 3/10\n", + "----- Beispiel 4/10\n", + "----- Beispiel 5/10\n", + "Umrechnung cart2ell: Punktdifferenz\n", + "Umrechnung cart2ell: Punktdifferenz\n", + "----- Beispiel 6/10\n", + "GHA1_ana: explodiert, Punkt liegt nicht mehr auf dem Ellipsoid\n", + "GHA1_ana: explodiert, Punkt liegt nicht mehr auf dem Ellipsoid\n", + "----- Beispiel 7/10\n", + "Umrechnung cart2ell: Punktdifferenz\n", + "Umrechnung cart2ell: Punktdifferenz\n", + "----- Beispiel 8/10\n", + "GHA1_ana: explodiert, Punkt liegt nicht mehr auf dem Ellipsoid\n", + "GHA1_ana: explodiert, Punkt liegt nicht mehr auf dem Ellipsoid\n", + "----- Beispiel 9/10\n", + "----- Beispiel 10/10\n", + "Umrechnung cart2ell: Punktdifferenz\n" ] } ], - "execution_count": 20 + "execution_count": 25 }, { "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, "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" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-08T15:00:05.614272Z", - "start_time": "2026-02-08T15:00:05.349014Z" - } - }, - "cell_type": "code", - "source": "test = \"Karney\"", - "id": "8be408707e46f165", + "id": "848eab44283945c6", "outputs": [], - "execution_count": 48 + "execution_count": null }, { "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "results = results_karney", - "id": "3a7c0416e1667b84" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-08T15:00:06.544175Z", - "start_time": "2026-02-08T15:00:06.215465Z" - } - }, - "cell_type": "code", "source": [ + "test = \"Random\"\n", "with open(f\"gha_results{test}.pkl\", \"rb\") as f:\n", " results = pickle.load(f)" ], "id": "4c20a0579c0f7038", "outputs": [], - "execution_count": 49 + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-08T15:00:28.798843Z", - "start_time": "2026-02-08T15:00:28.527486Z" - } - }, + "metadata": {}, "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", @@ -486,17 +485,13 @@ ], "id": "24ee93abe040e707", "outputs": [], - "execution_count": 50 + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-08T15:26:22.209167Z", - "start_time": "2026-02-08T15:00:31.424013Z" - } - }, + "metadata": {}, "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", @@ -525,12 +520,26 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-02-08T14:55:39.908336Z", - "start_time": "2026-02-08T14:55:39.447742Z" + "end_time": "2026-02-08T18:30:41.031480Z", + "start_time": "2026-02-08T18:30:40.781585Z" + } + }, + "cell_type": "code", + "source": "results = results_karney", + "id": "59f0b7f88002aff6", + "outputs": [], + "execution_count": 27 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-08T18:30:43.764880Z", + "start_time": "2026-02-08T18:30:42.661191Z" } }, "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", @@ -556,7 +565,7 @@ " 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(), table_indices) if group_index==group_value]\n", + " example_keys = [example_key for example_key, group_index in zip(results.keys(), group_indices) if group_index==group_value]\n", "\n", " algorithms = sorted({algorithm for example_key in example_keys for algorithm in results[example_key].keys() if algorithm.startswith(gha_prefix)})\n", "\n", @@ -597,23 +606,50 @@ " return fig\n", "\n", "figs = []\n", - "# if test == \"Panou\":\n", - "# for table_index in sorted(set(table_indices)):\n", - "# fig1 = build_max_table(\"GHA1\", f\"{test} - Gruppe {table_index} - GHA1\", table_index)\n", - "# fig2 = build_max_table(\"GHA2\", f\"{test} - Gruppe {table_index} - GHA2\", table_index)\n", - "# figs.append(fig1)\n", - "# figs.append(fig2)\n", - "# elif test == \"Karney\":\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", + "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": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_13876\\3822503802.py:6: RuntimeWarning:\n", + "\n", + "All-NaN slice encountered\n", + "\n", + "C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_13876\\3822503802.py:6: RuntimeWarning:\n", + "\n", + "All-NaN slice encountered\n", + "\n", + "C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_13876\\3822503802.py:6: RuntimeWarning:\n", + "\n", + "All-NaN slice encountered\n", + "\n", + "C:\\Users\\moell\\AppData\\Local\\Temp\\ipykernel_13876\\3822503802.py:6: RuntimeWarning:\n", + "\n", + "All-NaN slice encountered\n", + "\n" + ] + }, { "data": { "application/vnd.plotly.v1+json": { @@ -623,158 +659,60 @@ "align": "center", "values": [ [ - "ES", - "ES", - "ES", "ana", "ana", "ana", "ana", "ana", - "ana", - "ana", - "ana", - "ana", - "approx", - "approx", - "approx", - "num", - "num", - "num", - "num", - "num" + "ana" ], [ - "1250", - "60", - "600", - "20_2", - "20_32", + "20_16", + "20_64", "20_8", - "50_2", - "50_32", - "50_8", - "80_2", - "80_32", - "80_8", - "1250", - "600", - "6000", - "1000", - "10000", - "200", - "500", - "5000" + "60_16", + "60_64", + "60_8" ], [ 0, 0, - 0, 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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", @@ -2856,111 +18907,34 @@ " lines.append(r\"\\end{table}\")\n", " lines.append(r\"\\noindent\")\n", "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "# --- Beispielaufruf ---\n", + " return \"\\n\".join(lines)" + ], + "id": "12591c3b707da905", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ "example_keys = list(key for i, key in enumerate(results.keys()))\n", - "if test == \"Panou\":\n", - " example_keys = example_keys[:14]\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 = \"GHA1\"\n", - "# example_keys = list(key for i, key in enumerate(results.keys()) if table_indices[i] == 3) # gefiltert auf Panou Gruppe etc.\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})\",\n", - " label=f\"tab:results_{test}_{gha}\",\n", - " include_nan_col=True, # True, wenn du NaN-Spalte willst\n", - " wu=wu # wichtig, falls Winkelwerte rad sind\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", ")\n", "print(latex)" ], - "id": "12591c3b707da905", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\begin{table}[H]\n", - "\\centering\n", - "\\caption{Ergebnisse der Lösungsmethoden der 1. GHA (Random)}\n", - "\\label{tab:results_Random_GHA1}\n", - "\\begin{tabular}{|c|c|c|c|c|c|c|}\n", - "\\hline\n", - "Methode & Parameterwerte & NaN & $\\max(|\\Delta \\beta|)$ [$''$] & $\\max(|\\Delta \\lambda|)$ [$''$] & $\\max(|\\Delta \\alpha_1|)$ [$''$] & time [s] \\\\\n", - "\\Xhline{1.5pt}\n", - "\\multirow{9}{*}{analytisch} & 2, 20 & 32 & $1.037\\cdot10^{-08}$ & $5.633\\cdot10^{-09}$ & $3.298\\cdot10^{-09}$ & $0.01$ \\\\\n", - " & 2, 50 & 0 & $3.962\\cdot10^{-09}$ & $7.099\\cdot10^{-09}$ & $7.786\\cdot10^{-09}$ & $0.12$ \\\\\n", - " & 2, 80 & 0 & $3.962\\cdot10^{-09}$ & $7.099\\cdot10^{-09}$ & $7.786\\cdot10^{-09}$ & $0.43$ \\\\\n", - " & 8, 20 & 0 & $3.996\\cdot10^{-09}$ & $1.127\\cdot10^{-08}$ & $9.984\\cdot10^{-09}$ & $0.05$ \\\\\n", - " & 8, 50 & 0 & $3.996\\cdot10^{-09}$ & $1.127\\cdot10^{-08}$ & $9.984\\cdot10^{-09}$ & $0.52$ \\\\\n", - " & 8, 80 & 0 & $3.996\\cdot10^{-09}$ & $1.127\\cdot10^{-08}$ & $9.984\\cdot10^{-09}$ & $1.76$ \\\\\n", - " & 32, 20 & 0 & $4.775\\cdot10^{-09}$ & $1.081\\cdot10^{-08}$ & $9.526\\cdot10^{-09}$ & $0.18$ \\\\\n", - " & 32, 50 & 0 & $4.775\\cdot10^{-09}$ & $1.081\\cdot10^{-08}$ & $9.526\\cdot10^{-09}$ & $1.71$ \\\\\n", - " & 32, 80 & 0 & $4.775\\cdot10^{-09}$ & $1.081\\cdot10^{-08}$ & $9.526\\cdot10^{-09}$ & $7.03$ \\\\\\hline\n", - "\\multirow{5}{*}{numerisch} & 200 & 27 & $2.617\\cdot10^{-06}$ & $3.985\\cdot10^{-06}$ & $3.046\\cdot10^{-06}$ & $0.01$ \\\\\n", - " & 500 & 4 & $5.939\\cdot10^{-06}$ & $7.774\\cdot10^{-06}$ & $6.358\\cdot10^{-06}$ & $0.03$ \\\\\n", - " & 1000 & 0 & $4.543\\cdot10^{-07}$ & $1.972\\cdot10^{-06}$ & $1.796\\cdot10^{-06}$ & $0.07$ \\\\\n", - " & 5000 & 0 & $4.351\\cdot10^{-09}$ & $2.001\\cdot10^{-08}$ & $1.795\\cdot10^{-08}$ & $0.29$ \\\\\n", - " & 10000 & 0 & $0.000\\cdot10^{+00}$ & $0.000\\cdot10^{+00}$ & $0.000\\cdot10^{+00}$ & $0.57$ \\\\\\hline\n", - "\\multirow{3}{*}{approximiert} & 600 & 3 & $1.084\\cdot10^{+03}$ & $1.889\\cdot10^{+04}$ & $1.524\\cdot10^{+03}$ & $1.48$ \\\\\n", - " & 1250 & 3 & $1.257\\cdot10^{+02}$ & $8.470\\cdot10^{+03}$ & $1.136\\cdot10^{+03}$ & $3.18$ \\\\\n", - " & 6000 & 3 & $1.380\\cdot10^{+00}$ & $9.948\\cdot10^{+01}$ & $1.325\\cdot10^{+01}$ & $14.56$ \\\\\\hline\n", - "\\multirow{3}{*}{ES} & 60 & 0 & $3.354\\cdot10^{+04}$ & $3.007\\cdot10^{+05}$ & $6.480\\cdot10^{+05}$ & $7.97$ \\\\\n", - " & 600 & 0 & $1.372\\cdot10^{+04}$ & $6.354\\cdot10^{+04}$ & $6.480\\cdot10^{+05}$ & $52.69$ \\\\\n", - " & 1250 & 0 & $1.894\\cdot10^{+03}$ & $1.633\\cdot10^{+04}$ & $6.480\\cdot10^{+05}$ & $91.68$ \\\\\\hline\n", - "\\end{tabular}\n", - "\\end{table}\n", - "\\noindent\n" - ] - } - ], - "execution_count": 47 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-08T12:45:27.798160Z", - "start_time": "2026-02-08T12:45:27.247424Z" - } - }, - "cell_type": "code", - "source": [ - "def count_large_ds(results, example_keys, algorithm=\"GHA2_num\", threshold=10.0):\n", - " \"\"\"\n", - " Zählt Fälle, bei denen |Δs| > threshold ist.\n", - " \"\"\"\n", - " count = 0\n", - " for k in example_keys:\n", - " val = results[k][algorithm][2] # Δs\n", - " try:\n", - " if np.isfinite(val) and abs(val) > threshold:\n", - " count += 1\n", - " except Exception:\n", - " pass\n", - " return count\n", - "\n", - "example_keys = list(key for i, key in enumerate(results.keys()))\n", - "n_bad = count_large_ds(results, example_keys, algorithm=\"GHA2_num\", threshold=10.0)\n", - "print(f\"GHA2_num: |Δs| > 10 m in {n_bad} Fällen\")" - ], "id": "44f20396e6c5493c", - "outputs": [ - { - "ename": "KeyError", - "evalue": "'GHA2_num'", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mKeyError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[106]\u001B[39m\u001B[32m, line 16\u001B[39m\n\u001B[32m 13\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m count\n\u001B[32m 15\u001B[39m example_keys = \u001B[38;5;28mlist\u001B[39m(key \u001B[38;5;28;01mfor\u001B[39;00m i, key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(results.keys()))\n\u001B[32m---> \u001B[39m\u001B[32m16\u001B[39m n_bad = \u001B[43mcount_large_ds\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresults\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexample_keys\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43malgorithm\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mGHA2_num\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthreshold\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m10.0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 17\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mGHA2_num: |Δs| > 10 m in \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mn_bad\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m Fällen\u001B[39m\u001B[33m\"\u001B[39m)\n", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[106]\u001B[39m\u001B[32m, line 7\u001B[39m, in \u001B[36mcount_large_ds\u001B[39m\u001B[34m(results, example_keys, algorithm, threshold)\u001B[39m\n\u001B[32m 5\u001B[39m count = \u001B[32m0\u001B[39m\n\u001B[32m 6\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m example_keys:\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m val = \u001B[43mresults\u001B[49m\u001B[43m[\u001B[49m\u001B[43mk\u001B[49m\u001B[43m]\u001B[49m\u001B[43m[\u001B[49m\u001B[43malgorithm\u001B[49m\u001B[43m]\u001B[49m[\u001B[32m2\u001B[39m] \u001B[38;5;66;03m# Δs\u001B[39;00m\n\u001B[32m 8\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 9\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np.isfinite(val) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mabs\u001B[39m(val) > threshold:\n", - "\u001B[31mKeyError\u001B[39m: 'GHA2_num'" - ] - } - ], - "execution_count": 106 + "outputs": [], + "execution_count": null } ], "metadata": {