Abgabe fertig

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2026-02-11 12:08:46 +01:00
parent 5a293a823a
commit 59ad560f36
38 changed files with 3419 additions and 8763 deletions

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{
"cells": [
{
"metadata": {},
"cell_type": "code",
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
],
"id": "a78faf7f4883772f",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"import numpy as np\n",
"\n",
"import winkelumrechnungen as wu\n",
"from GHA_triaxial.utils import alpha_ell2para, alpha_para2ell\n",
"from ellipsoid_triaxial import EllipsoidTriaxial"
],
"id": "46aa84a937fea491",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"ell = EllipsoidTriaxial.init_name(\"KarneyTest2024\")\n",
"diffs = []\n",
"for beta_deg in range(-90, 91, 15):\n",
" for lamb_deg in range(-180, 180, 15):\n",
" for alpha_deg in range(0, 360, 15):\n",
" beta = wu.deg2rad(beta_deg)\n",
" lamb = wu.deg2rad(lamb_deg)\n",
" u, v = ell.ell2para(beta, lamb)\n",
" alpha = wu.deg2rad(alpha_deg)\n",
"\n",
" alpha_para_1, *_ = alpha_ell2para(ell, beta, lamb, alpha)\n",
" alpha_ell_1, *_ = alpha_para2ell(ell, u, v, alpha_para_1)\n",
" diff_1 = wu.deg2rad(abs(alpha_ell_1 - alpha))/3600\n",
"\n",
" alpha_ell_2, *_ = alpha_para2ell(ell, u, v, alpha)\n",
" alpha_para_2, *_ = alpha_ell2para(ell, beta, lamb, alpha_ell_2)\n",
" diff_2 = wu.deg2rad(abs(alpha_para_2 - alpha))/3600\n",
"\n",
" diffs.append((beta_deg, lamb_deg, alpha_deg, diff_1, diff_2))\n",
"diffs = np.array(diffs)"
],
"id": "82fc6cbbe7d5abcb",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"i_max_ell = np.argmax(diffs[:, 3])\n",
"max_ell = diffs[i_max_ell, 3]\n",
"point_max_ell = diffs[i_max_ell, :3]\n",
"\n",
"i_max_para = np.argmax(diffs[:, 4])\n",
"max_para = diffs[i_max_para, 4]\n",
"point_max_para = diffs[i_max_para, :4]\n",
"\n",
"print(f'Für elliptisches Alpha = {point_max_ell[2]}° und beta = {point_max_ell[0]}°, lamb = {point_max_ell[1]}°: diff = {max_ell}\"')\n",
"print(f'Für parametrisches Alpha = {point_max_para[2]}° und beta = {point_max_para[0]}°, lamb = {point_max_para[1]}°: diff = {max_ell}\"')\n",
"pass"
],
"id": "97b5b8c9ca5377ab",
"outputs": [],
"execution_count": null
}
],
"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
}

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{
"cells": [
{
"metadata": {},
"cell_type": "code",
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
],
"id": "746c5b9e4c0226e7",
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true
},
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"from itertools import product\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"\n",
"import winkelumrechnungen as wu\n",
"from ellipsoid_triaxial import EllipsoidTriaxial"
],
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"# ellips = \"KarneyTest2024\"\n",
"ellips = \"BursaSima1980\"\n",
"# ellips = \"Fiction\"\n",
"ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(ellips)"
],
"id": "7b05ca89fcd7b331",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"def deg_range(start, stop, step):\n",
" return [float(x) for x in range(start, stop + step, step)]\n",
"\n",
"def asymptotic_range(start, direction=\"up\", max_decimals=4):\n",
" values = []\n",
" for d in range(0, max_decimals + 1):\n",
" step = 10 ** -d\n",
" if direction == \"up\":\n",
" values.append(start + (1 - step))\n",
" else:\n",
" values.append(start - (1 - step))\n",
" return values"
],
"id": "61a6b14fef0180ad",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"beta_5_85 = deg_range(5, 85, 5)\n",
"lambda_5_85 = deg_range(5, 85, 5)\n",
"beta_5_90 = deg_range(5, 90, 5)\n",
"lambda_5_90 = deg_range(5, 90, 5)\n",
"beta_0_90 = deg_range(0, 90, 5)\n",
"lambda_0_90 = deg_range(0, 90, 5)\n",
"beta_90 = [90.0]\n",
"lambda_90 = [90.0]\n",
"beta_0 = [0.0]\n",
"lambda_0 = [0.0]\n",
"beta_asym_89 = asymptotic_range(89.0, direction=\"up\")\n",
"lambda_asym_0 = asymptotic_range(1.0, direction=\"down\")"
],
"id": "f7184980a4b930b7",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"groups = {\n",
" 1: list(product(beta_5_85, lambda_5_85)),\n",
" 2: list(product(beta_0, lambda_0_90)),\n",
" 3: list(product(beta_5_85, lambda_0)),\n",
" 4: list(product(beta_90, lambda_5_90)),\n",
" 5: list(product(beta_asym_89, lambda_asym_0)),\n",
" 6: list(product(beta_5_85, lambda_90)),\n",
" 7: list(product(lambda_asym_0, lambda_0_90)),\n",
" 8: list(product(beta_0_90, lambda_asym_0)),\n",
" 9: list(product(beta_asym_89, lambda_0_90)),\n",
" 10: list(product(beta_0_90, beta_asym_89)),\n",
"}"
],
"id": "cea9fd9cce6a4fd1",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"for nr, points in groups.items():\n",
" points_cart = []\n",
" for point in points:\n",
" beta, lamb = point\n",
" cart = ell.ell2cart(wu.deg2rad(beta), wu.deg2rad(lamb))\n",
" points_cart.append(cart)\n",
" groups[nr] = points_cart"
],
"id": "17a6a130782a89ce",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"results = {}\n",
"\n",
"for nr, points in groups.items():\n",
" group_results = {\"ell\": [],\n",
" \"para\": [],\n",
" \"geod\": []}\n",
" for point in points:\n",
" elli = ell.cart2ell(point)\n",
" cart_elli = ell.ell2cart(elli[0], elli[1])\n",
" group_results[\"ell\"].append(np.linalg.norm(point - cart_elli, axis=-1))\n",
"\n",
" para = ell.cart2para(point)\n",
" cart_para = ell.para2cart(para[0], para[1])\n",
" group_results[\"para\"].append(np.linalg.norm(point - cart_para, axis=-1))\n",
"\n",
" geod = ell.cart2geod(point, \"ligas3\")\n",
" cart_geod = ell.geod2cart(geod[0], geod[1], geod[2])\n",
" group_results[\"geod\"].append(np.linalg.norm(point - cart_geod, axis=-1))\n",
"\n",
" group_results[\"ell\"] = np.array(group_results[\"ell\"])\n",
" group_results[\"para\"] = np.array(group_results[\"para\"])\n",
" group_results[\"geod\"] = np.array(group_results[\"geod\"])\n",
" results[nr] = group_results"
],
"id": "c3298ea233bca274",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"# with open(f\"conversion_results_{ellips}.pkl\", \"wb\") as f:\n",
"# pickle.dump(results, f)"
],
"id": "e1285860be416ad3",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"# with open(f\"conversion_results_{ellips}.pkl\", \"rb\") as f:\n",
"# results = pickle.load(f)"
],
"id": "d26720e34595ccbc",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"df = pd.DataFrame({\n",
" \"Gruppe\": [nr for nr in results.keys()],\n",
" \"max_Δr_ell\": [f\"{max(result[\"ell\"]):.3g}\" for result in results.values()],\n",
" \"max_Δr_para\": [f\"{max(result[\"para\"]):.3g}\" for result in results.values()],\n",
" \"max_Δr_geod\": [f\"{max(result[\"geod\"]):.3g}\" for result in results.values()]\n",
"})"
],
"id": "4e2e55e4699ec81e",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"fig = go.Figure(data=[go.Table(\n",
" header=dict(\n",
" values=list(df.columns),\n",
" fill_color=\"lightgrey\",\n",
" align=\"left\"\n",
" ),\n",
" cells=dict(\n",
" values=[df[col] for col in df.columns],\n",
" align=\"left\"\n",
" )\n",
")])\n",
"fig.update_layout(\n",
" template=\"simple_white\",\n",
" width=650,\n",
" height=len(groups)*20+80,\n",
" margin=dict(l=20, r=20, t=20, b=20))\n",
"\n",
"fig.show()\n",
"# fig.write_image(f\"conversion_results_{ellips}.png\", width=650, height=len(groups)*20+80, scale=2)"
],
"id": "c2fa82afef2d6e0e",
"outputs": [],
"execution_count": null
}
],
"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
}

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