{ "cells": [ { "cell_type": "code", "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2026-01-18T19:42:01.269561Z", "start_time": "2026-01-18T19:42:01.073272Z" } }, "source": [ "%load_ext autoreload\n", "%autoreload 2" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "execution_count": 8 }, { "metadata": { "ExecuteTime": { "end_time": "2026-01-18T20:09:36.868596Z", "start_time": "2026-01-18T20:09:35.203686Z" } }, "cell_type": "code", "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "import time\n", "import pickle\n", "import numpy as np\n", "from numpy import nan\n", "import winkelumrechnungen as wu\n", "import os\n", "from contextlib import contextmanager, redirect_stdout, redirect_stderr\n", "import pandas as pd\n", "import plotly.graph_objects as go\n", "import math\n", "\n", "from ellipsoide import EllipsoidTriaxial\n", "from GHA_triaxial.panou import alpha_ell2para, alpha_para2ell\n", "\n", "from GHA_triaxial.panou import gha1_num, gha1_ana\n", "from GHA_triaxial.approx_gha1 import gha1_approx\n", "\n", "from GHA_triaxial.panou_2013_2GHA_num import gha2_num\n", "from GHA_triaxial.ES_gha2 import gha2_ES\n", "from GHA_triaxial.approx_gha2 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" ], "id": "961cb22764c5bcb9", "outputs": [], "execution_count": 24 }, { "metadata": { "ExecuteTime": { "end_time": "2026-01-18T19:42:04.334272Z", "start_time": "2026-01-18T19:42:04.054284Z" } }, "cell_type": "code", "source": [ "@contextmanager\n", "def suppress_print():\n", " with open(os.devnull, 'w') as fnull:\n", " with redirect_stdout(fnull), redirect_stderr(fnull):\n", " yield" ], "id": "e4740625a366ac13", "outputs": [], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2026-01-18T19:42:04.638454Z", "start_time": "2026-01-18T19:42:04.347686Z" } }, "cell_type": "code", "source": [ "# steps_gha1_num = [2000, 5000, 10000, 20000]\n", "# maxM_gha1_ana = [20, 40, 60]\n", "# parts_gha1_ana = [4, 8, 16]\n", "# dsPart_gha1_approx = [600, 1250, 6000, 60000] # entspricht bei der Erde ca. 10000, 5000, 1000, 100\n", "#\n", "# steps_gha2_num = [2000, 5000, 10000, 20000]\n", "# dsPart_gha2_ES = [600, 1250, 6000] # entspricht bei der Erde ca. 10000, 5000, 1000\n", "# dsPart_gha2_approx = [600, 1250, 6000, 60000] # entspricht bei der Erde ca. 10000, 5000, 1000, 100\n", "\n", "steps_gha1_num = [2000, 5000]\n", "maxM_gha1_ana = [20, 40]\n", "parts_gha1_ana = [4, 8]\n", "dsPart_gha1_approx = [600, 1250]\n", "\n", "steps_gha2_num = [2000, 5000]\n", "dsPart_gha2_ES = [20]\n", "dsPart_gha2_approx = [600, 1250]" ], "id": "96093cdde03f8d57", "outputs": [], "execution_count": 11 }, { "metadata": { "ExecuteTime": { "end_time": "2026-01-18T20:14:41.299711Z", "start_time": "2026-01-18T20:14:40.590759Z" } }, "cell_type": "code", "source": [ "# test = \"Karney\"\n", "test = \"Panou\"\n", "if test == \"Karney\":\n", " ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"KarneyTest2024\")\n", " examples = get_examples(2, 42)\n", "elif test == \"Panou\":\n", " ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(\"BursaSima1980round\")\n", " tables = get_tables_panou()\n", " table_indices = []\n", " examples = []\n", " for i, table in enumerate(tables):\n", " for example in table:\n", " table_indices.append(i+1)\n", " examples.append(example)" ], "id": "6e384cc01c2dbe", "outputs": [], "execution_count": 29 }, { "metadata": {}, "cell_type": "code", "source": [ "results = {}\n", "for example in examples:\n", " example_results = {}\n", "\n", " beta0, lamb0, alpha0_ell, beta1, lamb1, alpha1_ell, s = example\n", " P0 = ell.ell2cart(beta0, lamb0)\n", " P1 = ell.ell2cart(beta1, lamb1)\n", " _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)\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(wu.rad2deg(beta1_num - beta1)) / 3600\n", " d_lamb1 = abs(wu.rad2deg(lamb1_num - lamb1)) / 3600\n", " d_alpha1 = abs(wu.rad2deg(alpha1_num_1 - alpha1_ell)) / 3600\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", " _, _, alpha1_ana_ell = alpha_para2ell(ell, beta1_ana, lamb1_ana, alpha1_ana_para)\n", " d_beta1 = abs(wu.rad2deg(beta1_ana - beta1)) / 3600\n", " d_lamb1 = abs(wu.rad2deg(lamb1_ana - lamb1)) / 3600\n", " d_alpha1 = abs(wu.rad2deg(alpha1_ana_ell - alpha1_ell)) / 3600\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_approx:\n", " ds = ell.ax/dsPart\n", " start = time.perf_counter()\n", " try:\n", " P1_approx, alpha1_approx = gha1_approx(ell, P0, alpha0_ell, s, ds=ds)\n", " end = time.perf_counter()\n", " beta1_approx, lamb1_approx = ell.cart2ell(P1_approx)\n", " d_beta1 = abs(wu.rad2deg(beta1_approx - beta1)) / 3600\n", " d_lamb1 = abs(wu.rad2deg(lamb1_approx - lamb1)) / 3600\n", " d_alpha1 = abs(wu.rad2deg(alpha1_approx - alpha1_ell)) / 3600\n", " d_time = end - start\n", " example_results[f\"GHA1_approx_{ds:.3f}\"] = (d_beta1, d_lamb1, d_alpha1, d_time)\n", " except Exception as e:\n", " print(e)\n", " example_results[f\"GHA1_approx_{ds:.3f}\"] = (nan, nan, nan, nan)\n", "\n", " for steps in steps_gha2_num:\n", " start = time.perf_counter()\n", " try:\n", " alpha0_num, alpha1_num_2, s_num = gha2_num(ell, beta0, lamb0, beta1, lamb1, n=steps)\n", " end = time.perf_counter()\n", " d_alpha0 = abs(wu.rad2deg(alpha0_num - alpha0_ell)) / 3600\n", " d_alpha1 = abs(wu.rad2deg(alpha1_num_2 - alpha1_ell)) / 3600\n", " d_s = abs(s_num - s) / 1000\n", " d_time = end - start\n", " example_results[f\"GHA2_num_{steps}\"] = (d_alpha0, d_alpha1, d_s, d_time)\n", " except Exception as e:\n", " print(e)\n", " example_results[f\"GHA2_num_{steps}\"] = (nan, nan, nan, nan)\n", "\n", " for dsPart in dsPart_gha2_ES:\n", " ds = ell.ax/dsPart\n", " start = time.perf_counter()\n", " try:\n", " with suppress_print():\n", " alpha0_ES, alpha1_ES, s_ES = gha2_ES(ell, P0, P1, stepLenTarget=ds)\n", " end = time.perf_counter()\n", " d_alpha0 = abs(wu.rad2deg(alpha0_ES - alpha0_ell)) / 3600\n", " d_alpha1 = abs(wu.rad2deg(alpha1_ES - alpha1_ell)) / 3600\n", " d_s = abs(s_ES - s) / 1000\n", " d_time = end - start\n", " example_results[f\"GHA2_ES_{ds:.3f}\"] = (d_alpha0, d_alpha1, d_s, d_time)\n", " except Exception as e:\n", " print(e)\n", " example_results[f\"GHA2_ES_{ds:.3f}\"] = (nan, nan, nan, nan)\n", "\n", " for dsPart in dsPart_gha2_approx:\n", " ds = ell.ax/dsPart\n", " start = time.perf_counter()\n", " try:\n", " alpha0_approx, alpha1_approx, s_approx = gha2_approx(ell, P0, P1, ds=ds)\n", " end = time.perf_counter()\n", " d_alpha0 = abs(wu.rad2deg(alpha0_approx - alpha0_ell)) / 3600\n", " d_alpha1 = abs(wu.rad2deg(alpha1_approx - alpha1_ell)) / 3600\n", " d_s = abs(s_approx - s) / 1000\n", " d_time = end - start\n", " example_results[f\"GHA2_approx_{ds:.3f}\"] = (d_alpha0, d_alpha1, d_s, d_time)\n", " except Exception as e:\n", " print(e)\n", " example_results[f\"GHA2_approx_{ds:.3f}\"] = (nan, nan, nan, nan)\n", "\n", " results[f\"beta0: {wu.rad2deg(beta0):.3f}, lamb0: {wu.rad2deg(lamb0):.3f}, alpha0: {wu.rad2deg(alpha0_ell):.3f}, s: {s}\"] = example_results" ], "id": "ef8849908ed3231e", "outputs": [], "execution_count": null }, { "metadata": { "ExecuteTime": { "end_time": "2026-01-18T19:56:06.685013Z", "start_time": "2026-01-18T19:56:06.424513Z" } }, "cell_type": "code", "source": [ "with open(f\"gha_results{test}.pkl\", \"wb\") as f:\n", " pickle.dump(results, f)" ], "id": "664105ea35d50a7b", "outputs": [], "execution_count": 15 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": [ "with open(f\"gha_results{test}.pkl\", \"rb\") as f:\n", " results = pickle.load(f)" ], "id": "ad8aa9dcc8af4a05" }, { "metadata": { "ExecuteTime": { "end_time": "2026-01-18T21:28:08.893680Z", "start_time": "2026-01-18T21:28:07.904493Z" } }, "cell_type": "code", "source": [ "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(group_value, gha_prefix, title):\n", " # metrics\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", " # collect keys in this group\n", " example_keys = [example_key for example_key, group_index in zip(results.keys(), table_indices) if group_index==group_value]\n", " # all variant keys (inner dict keys) matching prefix\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\"] + list(metrics)\n", " cells = [[] for i in range(len(metrics) + 2)]\n", " for algorithm in algorithms:\n", " ghaNr, variant, params = algorithm.split(\"_\", 2)\n", " cells[0].append(variant)\n", " cells[1].append(params)\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+2].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", "for table_index in sorted(set(table_indices))[:-1]:\n", " fig1 = build_max_table(table_index, \"GHA1\", f\"Gruppe {table_index} – GHA1\")\n", " fig2 = build_max_table(table_index, \"GHA2\", f\"Gruppe {table_index} – GHA2\")\n", " 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