From c989730fb8aee0a4a7a3858ab1ececb6b1ed1f33 Mon Sep 17 00:00:00 2001 From: Jrenaud-Desk Date: Fri, 26 Apr 2024 20:30:29 -0400 Subject: [PATCH] refactors --- Benchmarks & Performance/Earth_LNs.ipynb | 793 ----------------------- 1 file changed, 793 deletions(-) delete mode 100644 Benchmarks & Performance/Earth_LNs.ipynb diff --git a/Benchmarks & Performance/Earth_LNs.ipynb b/Benchmarks & Performance/Earth_LNs.ipynb deleted file mode 100644 index cfcb3b3e..00000000 --- a/Benchmarks & Performance/Earth_LNs.ipynb +++ /dev/null @@ -1,793 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "f1687545-aa85-4859-aabf-b9a4b5e3f27e", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from TidalPy.RadialSolver import radial_solver\n", - "from TidalPy.utilities.spherical_helper import calculate_mass_gravity_arrays" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c33e0ace-0c4b-400f-b8db-2e914105923d", - "metadata": {}, - "outputs": [], - "source": [ - "# recreate results of Guo+2004\n", - "# uses PREM model of earth with averaged continental and ocean crust\n", - "# LNs taken from table 1 of Guo+2004 GJI\n", - "\n", - "r = np.array([1.0000e+00, 1.0100e+02, 2.0100e+02, 3.0100e+02, 4.0100e+02,\n", - " 5.0100e+02, 6.0100e+02, 7.0100e+02, 8.0100e+02, 9.0100e+02,\n", - " 1.0010e+03, 1.1010e+03, 1.2010e+03, 1.2181e+03, 1.2225e+03,\n", - " 1.2225e+03, 1.3010e+03, 1.4010e+03, 1.5010e+03, 1.6010e+03,\n", - " 1.7010e+03, 1.8010e+03, 1.9010e+03, 2.0010e+03, 2.1010e+03,\n", - " 2.2010e+03, 2.2225e+03, 2.3010e+03, 2.4010e+03, 2.5010e+03,\n", - " 2.6010e+03, 2.7010e+03, 2.8010e+03, 2.9010e+03, 3.0010e+03,\n", - " 3.1010e+03, 3.2010e+03, 3.3010e+03, 3.4010e+03, 3.4810e+03,\n", - " 3.4810e+03, 3.4867e+03, 3.5367e+03, 3.5867e+03, 3.6310e+03,\n", - " 3.6310e+03, 3.6367e+03, 3.6867e+03, 3.7367e+03, 3.7867e+03,\n", - " 3.8367e+03, 3.8867e+03, 3.9367e+03, 3.9867e+03, 4.0367e+03,\n", - " 4.0867e+03, 4.1367e+03, 4.1867e+03, 4.2367e+03, 4.2867e+03,\n", - " 4.3367e+03, 4.3867e+03, 4.4367e+03, 4.4867e+03, 4.5367e+03,\n", - " 4.5867e+03, 4.6367e+03, 4.6867e+03, 4.7367e+03, 4.7867e+03,\n", - " 4.8367e+03, 4.8867e+03, 4.9367e+03, 4.9867e+03, 5.0367e+03,\n", - " 5.0867e+03, 5.1367e+03, 5.1867e+03, 5.2367e+03, 5.2867e+03,\n", - " 5.3367e+03, 5.3867e+03, 5.4367e+03, 5.4867e+03, 5.5367e+03,\n", - " 5.5867e+03, 5.6010e+03, 5.6010e+03, 5.6367e+03, 5.6867e+03,\n", - " 5.7020e+03, 5.7020e+03, 5.7120e+03, 5.7220e+03, 5.7320e+03,\n", - " 5.7420e+03, 5.7520e+03, 5.7620e+03, 5.7720e+03, 5.7720e+03,\n", - " 5.7820e+03, 5.7920e+03, 5.8020e+03, 5.8120e+03, 5.8220e+03,\n", - " 5.8320e+03, 5.8420e+03, 5.8520e+03, 5.8620e+03, 5.8720e+03,\n", - " 5.8820e+03, 5.8920e+03, 5.9020e+03, 5.9120e+03, 5.9220e+03,\n", - " 5.9320e+03, 5.9420e+03, 5.9520e+03, 5.9620e+03, 5.9720e+03,\n", - " 5.9720e+03, 5.9820e+03, 5.9920e+03, 6.0020e+03, 6.0120e+03,\n", - " 6.0220e+03, 6.0320e+03, 6.0420e+03, 6.0520e+03, 6.0620e+03,\n", - " 6.0720e+03, 6.0820e+03, 6.0920e+03, 6.1020e+03, 6.1120e+03,\n", - " 6.1220e+03, 6.1320e+03, 6.1420e+03, 6.1520e+03, 6.1520e+03,\n", - " 6.1620e+03, 6.1720e+03, 6.1820e+03, 6.1920e+03, 6.2020e+03,\n", - " 6.2120e+03, 6.2220e+03, 6.2320e+03, 6.2420e+03, 6.2520e+03,\n", - " 6.2620e+03, 6.2720e+03, 6.2820e+03, 6.2920e+03, 6.2920e+03,\n", - " 6.3020e+03, 6.3120e+03, 6.3220e+03, 6.3270e+03, 6.3320e+03,\n", - " 6.3370e+03, 6.3380e+03, 6.3390e+03, 6.3400e+03, 6.3410e+03,\n", - " 6.3420e+03, 6.3430e+03, 6.3440e+03, 6.3450e+03, 6.3460e+03,\n", - " 6.3470e+03, 6.3476e+03, 6.3476e+03, 6.3490e+03, 6.3500e+03,\n", - " 6.3510e+03, 6.3520e+03, 6.3530e+03, 6.3540e+03, 6.3550e+03,\n", - " 6.3560e+03, 6.3570e+03, 6.3570e+03, 6.3580e+03, 6.3590e+03,\n", - " 6.3600e+03, 6.3610e+03, 6.3620e+03, 6.3630e+03, 6.3640e+03,\n", - " 6.3650e+03, 6.3660e+03, 6.3670e+03, 6.3680e+03, 6.3690e+03,\n", - " 6.3690e+03, 6.3700e+03, 6.3710e+03])\n", - "\n", - "vp = np.array([11.2622 , 11.26063, 11.25593, 11.24809, 11.23711, 11.223 ,\n", - " 11.20576, 11.18537, 11.16186, 11.1352 , 11.10541, 11.07249,\n", - " 11.03642, 11.02994, 11.02826, 10.35572, 10.30975, 10.24963,\n", - " 10.18747, 10.12295, 10.05576, 9.98558, 9.9121 , 9.835 ,\n", - " 9.75397, 9.66869, 9.64977, 9.57885, 9.48413, 9.38422,\n", - " 9.2788 , 9.16756, 9.05019, 8.92636, 8.79577, 8.65809,\n", - " 8.51302, 8.36024, 8.19943, 8.06479, 13.71662, 13.71522,\n", - " 13.70302, 13.69098, 13.68044, 13.68042, 13.6735 , 13.61313,\n", - " 13.55341, 13.49426, 13.4356 , 13.37735, 13.31944, 13.26179,\n", - " 13.20432, 13.14696, 13.08963, 13.03224, 12.97473, 12.91702,\n", - " 12.85902, 12.80067, 12.74188, 12.68258, 12.62269, 12.56213,\n", - " 12.50083, 12.4387 , 12.37567, 12.31167, 12.24662, 12.18043,\n", - " 12.11304, 12.04435, 11.97431, 11.90283, 11.82983, 11.75523,\n", - " 11.67896, 11.60095, 11.5211 , 11.43936, 11.35563, 11.26984,\n", - " 11.18192, 11.09179, 11.06559, 11.0656 , 10.95459, 10.79897,\n", - " 10.75132, 10.26617, 10.25069, 10.2352 , 10.21971, 10.20422,\n", - " 10.18874, 10.17325, 10.15776, 10.15783, 10.10663, 10.05543,\n", - " 10.00424, 9.95304, 9.90185, 9.85065, 9.79946, 9.74826,\n", - " 9.69707, 9.64587, 9.59468, 9.54348, 9.49228, 9.44109,\n", - " 9.38989, 9.3387 , 9.2875 , 9.23631, 9.18511, 9.13392,\n", - " 8.90524, 8.886 , 8.86677, 8.84753, 8.82829, 8.80905,\n", - " 8.78981, 8.77057, 8.75133, 8.7321 , 8.71286, 8.69362,\n", - " 8.67438, 8.65514, 8.6359 , 8.61666, 8.59743, 8.57819,\n", - " 7.98971, 7.98971, 7.99589, 8.00207, 8.00825, 8.01443,\n", - " 8.02062, 8.0268 , 8.03298, 8.03916, 8.04534, 8.05152,\n", - " 8.0577 , 8.06389, 8.07007, 8.07625, 8.07625, 8.08243,\n", - " 8.08861, 8.09479, 8.09788, 8.10097, 8.10406, 8.10468,\n", - " 8.1053 , 8.10592, 8.10654, 8.10716, 8.10777, 8.10839,\n", - " 8.10901, 8.10963, 8.11025, 8.11062, 6.8 , 6.8 ,\n", - " 6.8 , 6.8 , 6.8 , 6.8 , 6.8 , 6.8 ,\n", - " 6.8 , 6.8 , 6.18904, 6.18904, 6.18904, 6.18904,\n", - " 6.18904, 6.18904, 6.18904, 6.18904, 6.18904, 6.18904,\n", - " 6.18904, 6.18904, 6.18904, 6.18904, 6.18904, 6.18904])\n", - "\n", - "vs = np.array([3.6678 , 3.6667 , 3.66342, 3.65794, 3.65027, 3.64041, 3.62835,\n", - " 3.61411, 3.59767, 3.57905, 3.55823, 3.53522, 3.51002, 3.50549,\n", - " 3.50431, 0. , 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. , 7.26465, 7.26471,\n", - " 7.2652 , 7.26563, 7.26597, 7.26593, 7.26331, 7.24048, 7.21781,\n", - " 7.19529, 7.17287, 7.15055, 7.12828, 7.10605, 7.08382, 7.06157,\n", - " 7.03927, 7.0169 , 6.99442, 6.97182, 6.94906, 6.92611, 6.90296,\n", - " 6.87957, 6.85592, 6.83197, 6.80771, 6.78311, 6.75813, 6.73276,\n", - " 6.70696, 6.68071, 6.65398, 6.62674, 6.59897, 6.57064, 6.54173,\n", - " 6.5122 , 6.48204, 6.4512 , 6.41967, 6.38742, 6.35442, 6.32065,\n", - " 6.28608, 6.25067, 6.24039, 6.24054, 6.13609, 5.98986, 5.94513,\n", - " 5.57021, 5.56247, 5.55473, 5.54699, 5.53924, 5.5315 , 5.52376,\n", - " 5.51602, 5.51593, 5.48676, 5.45759, 5.42841, 5.39924, 5.37007,\n", - " 5.3409 , 5.31173, 5.28255, 5.25338, 5.22421, 5.19504, 5.16586,\n", - " 5.13669, 5.10752, 5.07835, 5.04918, 5.02 , 4.99083, 4.96166,\n", - " 4.93249, 4.7699 , 4.7629 , 4.7559 , 4.7489 , 4.7419 , 4.7349 ,\n", - " 4.7279 , 4.7209 , 4.7139 , 4.7069 , 4.6999 , 4.6929 , 4.6859 ,\n", - " 4.6789 , 4.6719 , 4.6649 , 4.6579 , 4.6509 , 4.41892, 4.41892,\n", - " 4.4226 , 4.42629, 4.42997, 4.43366, 4.43734, 4.44103, 4.44472,\n", - " 4.4484 , 4.45209, 4.45577, 4.45946, 4.46314, 4.46683, 4.47052,\n", - " 4.47052, 4.4742 , 4.47789, 4.48157, 4.48341, 4.48526, 4.4871 ,\n", - " 4.48747, 4.48784, 4.48821, 4.48857, 4.48894, 4.48931, 4.48968,\n", - " 4.49005, 4.49042, 4.49079, 4.49101, 3.9 , 3.9 , 3.9 ,\n", - " 3.9 , 3.9 , 3.9 , 3.9 , 3.9 , 3.9 , 3.9 ,\n", - " 3.41464, 3.41464, 3.41464, 3.41464, 3.41464, 3.41464, 3.41464,\n", - " 3.41464, 3.41464, 3.41464, 3.41464, 3.41464, 3.41464, 3.41464,\n", - " 3.41464, 3.41464])\n", - "\n", - "rho = np.array([13.0885 , 13.08632, 13.07979, 13.0689 , 13.05366, 13.03406,\n", - " 13.01011, 12.98181, 12.94914, 12.91213, 12.87076, 12.82503,\n", - " 12.77495, 12.76595, 12.76361, 12.16633, 12.12499, 12.06923,\n", - " 12.00988, 11.94681, 11.87989, 11.809 , 11.73401, 11.65478,\n", - " 11.57119, 11.48311, 11.46358, 11.39042, 11.29298, 11.19067,\n", - " 11.08336, 10.97092, 10.85322, 10.73013, 10.60153, 10.46729,\n", - " 10.32727, 10.18136, 10.02942, 9.90344, 5.56646, 5.5636 ,\n", - " 5.53856, 5.51358, 5.49148, 5.49148, 5.48863, 5.46372,\n", - " 5.43883, 5.41395, 5.38908, 5.3642 , 5.33931, 5.3144 ,\n", - " 5.28945, 5.26447, 5.23944, 5.21434, 5.18919, 5.16395,\n", - " 5.13863, 5.11322, 5.0877 , 5.06208, 5.03633, 5.01045,\n", - " 4.98443, 4.95827, 4.93195, 4.90547, 4.87881, 4.85196,\n", - " 4.82493, 4.7977 , 4.77025, 4.74259, 4.71469, 4.68656,\n", - " 4.65819, 4.62956, 4.60067, 4.5715 , 4.54206, 4.51232,\n", - " 4.48228, 4.45194, 4.4432 , 4.4432 , 4.42128, 4.39029,\n", - " 4.38074, 3.99212, 3.98979, 3.98746, 3.98514, 3.98281,\n", - " 3.98048, 3.97815, 3.97582, 3.97582, 3.96322, 3.95061,\n", - " 3.93801, 3.92541, 3.9128 , 3.9002 , 3.88759, 3.87499,\n", - " 3.86239, 3.84978, 3.83718, 3.82458, 3.81197, 3.79937,\n", - " 3.78677, 3.77416, 3.76156, 3.74895, 3.73635, 3.72375,\n", - " 3.54326, 3.53729, 3.53132, 3.52535, 3.51938, 3.51341,\n", - " 3.50743, 3.50146, 3.49549, 3.48952, 3.48355, 3.47758,\n", - " 3.4716 , 3.46563, 3.45966, 3.45369, 3.44772, 3.44175,\n", - " 3.43577, 3.35949, 3.36058, 3.36166, 3.36275, 3.36384,\n", - " 3.36492, 3.36601, 3.3671 , 3.36818, 3.36927, 3.37036,\n", - " 3.37145, 3.37253, 3.37362, 3.37471, 3.37471, 3.37579,\n", - " 3.37688, 3.37797, 3.37851, 3.37905, 3.3796 , 3.3797 ,\n", - " 3.37981, 3.37992, 3.38003, 3.38014, 3.38025, 3.38036,\n", - " 3.38047, 3.38057, 3.38068, 3.38075, 2.9 , 2.9 ,\n", - " 2.9 , 2.9 , 2.9 , 2.9 , 2.9 , 2.9 ,\n", - " 2.9 , 2.9 , 2.2834 , 2.2834 , 2.2834 , 2.2834 ,\n", - " 2.2834 , 2.2834 , 2.2834 , 2.2834 , 2.2834 , 2.2834 ,\n", - " 2.2834 , 2.2834 , 2.2834 , 2.2834 , 2.2834 , 2.2834 ])\n", - "\n", - "\n", - "# convert to SI\n", - "r = np.multiply(r,1000.)\n", - "vp = np.multiply(vp,1000.)\n", - "vs = np.multiply(vs,1000.)\n", - "rho = np.multiply(rho,1000.)\n", - "\n", - "# Convert Seismic Velocities to Elastic Moduli\n", - "mu = np.multiply(np.square(vs),rho)\n", - "K = np.subtract(np.multiply(np.square(vp),rho),mu*(4./3.))\n", - "lam = np.subtract(K, mu*(2./3.))\n", - "\n", - "visc = 1E30\n", - "eta = np.full(len(mu),visc)\n", - "\n", - "# Define planetary parameters\n", - "planet_mass = 5.972e24\n", - "planet_radius = 6.371e6\n", - "planet_volume = (4. / 3.) * np.pi * (planet_radius**3)\n", - "planet_bulk_density = planet_mass / planet_volume" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0272eaff-879c-4e38-8a36-445f481df7f7", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:80: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_i[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:81: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_i[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:82: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_i[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_i[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:121: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_c[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:122: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_c[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:123: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_c[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:124: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_c[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_i[i-1] = radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:86: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_i[i-1] = radial_solution.h[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:87: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_i[i-1] = radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:88: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_i[i-1] = radial_solution.h[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:126: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_c[i-1] = radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:127: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_c[i-1] = radial_solution.h[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:128: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_c[i-1] = radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/3971989821.py:129: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_c[i-1] = radial_solution.h[1]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "forcing_frequency = 2. * np.pi / (24 * 60 * 60)\n", - "\n", - "N = 10\n", - "\n", - "radius_array = np.ones((len(r)-1)*N)\n", - "shear_array = np.ones((len(r)-1)*N)\n", - "viscosity_array = np.ones((len(r)-1)*N)\n", - "bulk_mod_array = np.ones((len(r)-1)*N)\n", - "density_array = np.ones((len(r)-1)*N)\n", - "\n", - "for i in range(0,len(r)-1):\n", - " radius_array[i*N:(i+1)*N] = np.linspace(r[i],r[i+1],N)\n", - " shear_array[i*N:(i+1)*N] = np.linspace(mu[i],mu[i+1],N)\n", - " viscosity_array[i*N:(i+1)*N] = np.linspace(eta[i],eta[i+1],N)\n", - " bulk_mod_array[i*N:(i+1)*N] = np.linspace(K[i],K[i+1],N)\n", - " density_array[i*N:(i+1)*N] = np.linspace(rho[i],rho[i+1],N)\n", - "\n", - "\n", - "volume_array, mass_array, gravity_array = \\\n", - " calculate_mass_gravity_arrays(radius_array, density_array)\n", - "\n", - "\n", - "# Purely Elastic Body\n", - "from TidalPy.rheology import Elastic\n", - "\n", - "elastic_rheology = Elastic()\n", - "# Calculate the \"complex\" shear (really all Im[mu] = 0)\n", - "complex_shear = np.empty(radius_array.shape, dtype=np.complex128)\n", - "shear_array = np.ascontiguousarray(shear_array)\n", - "viscosity_array = np.ascontiguousarray(viscosity_array)\n", - "elastic_rheology.vectorize_modulus_viscosity(forcing_frequency, shear_array, viscosity_array, complex_shear)\n", - "\n", - "\n", - "nmax=18\n", - "\n", - "degs = np.arange(1,nmax+1)\n", - "kp_i = np.zeros(nmax)\n", - "hp_i = np.zeros(nmax)\n", - "kl_i = np.zeros(nmax)\n", - "hl_i = np.zeros(nmax)\n", - "\n", - "kp_c = np.zeros(nmax)\n", - "hp_c = np.zeros(nmax)\n", - "kl_c = np.zeros(nmax)\n", - "hl_c = np.zeros(nmax)\n", - "\n", - "\n", - "for i in range(1,nmax+1):\n", - " radial_solution = \\\n", - " radial_solver(\n", - " radius_array,\n", - " density_array,\n", - " gravity_array,\n", - " bulk_mod_array,\n", - " complex_shear,\n", - " forcing_frequency,\n", - " planet_bulk_density,\n", - " layer_types=(\"liquid\",\"solid\",\"solid\"),\n", - " is_static_by_layer=(True,False,False),\n", - " is_incompressible_by_layer=(True,True,True),\n", - " upper_radius_by_layer=(3473E3,4500E3, 6371E3),\n", - " degree_l=i,\n", - " solve_for=('tidal','loading'),\n", - " use_kamata=True,\n", - " integration_method='DOP853',\n", - " integration_rtol = 1.0e-6,\n", - " integration_atol = 1.0e-6,\n", - " scale_rtols_by_layer_type = False,\n", - " max_num_steps = 10_000_000,\n", - " expected_size = 1000,\n", - " max_ram_MB = 1000,\n", - " max_step = 0,\n", - " limit_solution_to_radius = True,\n", - " nondimensionalize = True,\n", - " verbose = False,\n", - " raise_on_fail = True\n", - " )\n", - "\n", - " if i==1:\n", - " kp_i[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - " hp_i[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - " kl_i[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - " hl_i[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - " else:\n", - " kp_i[i-1] = radial_solution.k[0]\n", - " hp_i[i-1] = radial_solution.h[0]\n", - " kl_i[i-1] = radial_solution.k[1]\n", - " hl_i[i-1] = radial_solution.h[1]\n", - " \n", - " radial_solution = \\\n", - " radial_solver(\n", - " radius_array,\n", - " density_array,\n", - " gravity_array,\n", - " bulk_mod_array,\n", - " complex_shear,\n", - " forcing_frequency,\n", - " planet_bulk_density,\n", - " layer_types=(\"liquid\",\"solid\",\"solid\"),\n", - " is_static_by_layer=(True,False,False),\n", - " is_incompressible_by_layer=(False, False, False),\n", - " upper_radius_by_layer=(3473E3,4500E3, 6371E3),\n", - " degree_l=i,\n", - " solve_for=('tidal','loading'),\n", - " use_kamata=True,\n", - " integration_method='DOP853',\n", - " integration_rtol = 1.0e-6,\n", - " integration_atol = 1.0e-6,\n", - " scale_rtols_by_layer_type = False,\n", - " max_num_steps = 10_000_000,\n", - " expected_size = 1000,\n", - " max_ram_MB = 1000,\n", - " max_step = 500,\n", - " limit_solution_to_radius = True,\n", - " nondimensionalize = True,\n", - " verbose = False,\n", - " raise_on_fail = True\n", - " )\n", - "\n", - " if i==1:\n", - " kp_c[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - " hp_c[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - " kl_c[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - " hl_c[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - " else:\n", - " kp_c[i-1] = radial_solution.k[0]\n", - " hp_c[i-1] = radial_solution.h[0]\n", - " kl_c[i-1] = radial_solution.k[1]\n", - " hl_c[i-1] = radial_solution.h[1]\n", - " \n", - " print(i)\n", - "\n", - "\n", - "guo_n = np.array([1, 2, 3, 4, 5, 6, 8, 10, 18])\n", - "guo_h = -np.array([0.2856, 0.9909, 1.0501, 1.0528, 1.0857, 1.1433, 1.2833, 1.4226, 1.8733])\n", - "guo_kn = -np.array([0, 0.6103, 0.5876, 0.5341, 0.5228, 0.5411, 0.61107, 0.6893, 0.961])\n", - "\n", - "plt.figure(figsize=(12,6))\n", - "plt.subplot(1,2,1)\n", - "plt.plot(degs,hl_i,'-.',label=\"incompressible\",ms=8,color='red')\n", - "plt.plot(degs,hl_c,'-.',label=\"compressible\",ms=8,color='blue')\n", - "plt.plot(guo_n,guo_h,'x',label=\"Guo+ (2004)\",ms=8,color='black')\n", - "plt.xticks(np.arange(0, nmax,1))\n", - "plt.xlim([1,nmax])\n", - "plt.tick_params(labelsize='large')\n", - "plt.title('Load Love Numbers $(h^\\prime)$',fontsize = 'xx-large')\n", - "plt.xlabel(r'$n$',fontsize='xx-large')\n", - "plt.grid()\n", - "\n", - "plt.subplot(1,2,2)\n", - "plt.plot(degs,kl_i,'-.',label=\"incompressible\",ms=8,color='red')\n", - "plt.plot(degs,kl_c,'-.',label=\"compressible\",ms=8,color='blue')\n", - "plt.plot(guo_n,guo_kn/guo_n,'x',label=\"Guo+ (2004)\",ms=8,color='black')\n", - "\n", - "plt.xticks(np.arange(0, nmax,1))\n", - "plt.xlim([2,nmax])\n", - "plt.tick_params(labelsize='large')\n", - "\n", - "plt.legend(loc='best',fontsize='12',ncol=1)\n", - "plt.title('Load Love Numbers $(k^\\prime)$',fontsize = 'xx-large')\n", - "plt.xlabel(r'$n$',fontsize='xx-large')\n", - "plt.grid()\n", - "plt.tight_layout()\n", - "plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "72e67ea8-8b29-4390-8139-b27c4c637aca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ":: Reversing model to start from core\n" - ] - } - ], - "source": [ - "# now lets do an older model, from Farrell (1972) using the Gutenberg Earth model of Alterman+ 1961\n", - "\n", - "r = np.array([0, 19, 38, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225,\n", - " 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1200,\n", - " 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 2898, 3000,\n", - " 3500, 4000, 4500, 4982, 5121, 6370])\n", - "\n", - "\n", - "rho = np.array([2.74, 3.00, 3.32, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.41,\n", - " 3.43, 3.46, 3.48, 3.50, 3.53, 3.58, 3.62, 3.69, 3.82, 4.01,\n", - " 4.21, 4.40, 4.56, 4.63, 4.74, 4.85, 4.96, 5.07, 5.19, 5.29,\n", - " 5.39, 5.49, 5.59, 5.69, 9.40, 9.55, 10.15, 10.7, 11.2, 11.5,\n", - " 12.0, 12.3])\n", - "\n", - "vp = np.array([6.14, 6.58, 8.20, 8.17, 8.14, 8.10, 8.07, 8.02, 7.93, 7.85, \n", - " 7.89, 7.98, 8.10, 8.21, 8.38, 8.62, 8.87, 9.15, 9.45, 9.88, \n", - " 10.30, 10.71, 11.10, 11.35, 11.60, 11.93, 12.17, 12.43, 12.67,\n", - " 12.90, 13.10, 13.32, 13.59, 13.70, 8.10, 8.23, 8.90, 9.50, \n", - " 9.97, 10.44, 10.75, 11.31])\n", - "\n", - "vs = np.array([3.55, 3.80, 4.65, 4.62, 4.57, 4.51, 4.46, 4.41, 4.37, 4.35, \n", - " 4.36, 4.38, 4.42, 4.46, 4.45, 4.68, 4.85, 5.04, 5.21, 5.45, \n", - " 5.76, 6.03, 6.23, 6.32, 6.42, 6.55, 6.69, 6.80, 6.90, 6.97, \n", - " 7.05, 7.15, 7.23, 7.20, 0., 0., 0., 0., 0., 0., 0., 0.])\n", - "\n", - "\n", - "if (r[0] < r[-1]):\n", - " print(\":: Reversing model to start from core\")\n", - " r = r[::-1]\n", - " vp = vp[::-1]\n", - " vs = vs[::-1]\n", - " rho = rho[::-1]\n", - "r = 6371-r\n", - "\n", - "r = np.multiply(r,1000.)\n", - "vp = np.multiply(vp,1000.)\n", - "vs = np.multiply(vs,1000.)\n", - "rho = np.multiply(rho,1000.)\n", - "\n", - "# Convert Seismic Velocities to Elastic Moduli\n", - "mu = np.multiply(np.square(vs),rho)\n", - "K = np.subtract(np.multiply(np.square(vp),rho),mu*(4./3.))\n", - "lam = np.subtract(K, mu*(2./3.))\n", - "\n", - "visc = 1E30\n", - "eta = np.full(len(mu),visc)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b0978269-d387-4702-a0fb-a3be2c9344f6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:81: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_i[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:82: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_i[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_i[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:84: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_i[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:122: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_c[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:123: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_c[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:124: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_c[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:125: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_c[i-1] = radial_solution.h[1]-radial_solution.k[1]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:86: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_i[i-1] = radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:87: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_i[i-1] = radial_solution.h[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:88: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_i[i-1] = radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:89: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_i[i-1] = radial_solution.h[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:127: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kp_c[i-1] = radial_solution.k[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:128: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hp_c[i-1] = radial_solution.h[0]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:129: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " kl_c[i-1] = radial_solution.k[1]\n", - "/var/folders/3t/v1hqyg7n1mx49w8wk2wn_8w00000gn/T/ipykernel_1241/326768609.py:130: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " hl_c[i-1] = radial_solution.h[1]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "forcing_frequency = 2. * np.pi / (24 * 60 * 60) # Phobos orbital freq\n", - "\n", - "N = 1000\n", - "\n", - "radius_array = np.ones((len(r)-1)*N)\n", - "shear_array = np.ones((len(r)-1)*N)\n", - "viscosity_array = np.ones((len(r)-1)*N)\n", - "bulk_mod_array = np.ones((len(r)-1)*N)\n", - "density_array = np.ones((len(r)-1)*N)\n", - "\n", - "for i in range(0,len(r)-1):\n", - " radius_array[i*N:(i+1)*N] = np.linspace(r[i],r[i+1],N)\n", - " shear_array[i*N:(i+1)*N] = np.linspace(mu[i],mu[i+1],N)\n", - " viscosity_array[i*N:(i+1)*N] = np.linspace(eta[i],eta[i+1],N)\n", - " bulk_mod_array[i*N:(i+1)*N] = np.linspace(K[i],K[i+1],N)\n", - " density_array[i*N:(i+1)*N] = np.linspace(rho[i],rho[i+1],N)\n", - "\n", - "volume_array, mass_array, gravity_array = \\\n", - " calculate_mass_gravity_arrays(radius_array, density_array)\n", - " \n", - "\n", - " \n", - "# Purely Elastic Body\n", - "from TidalPy.rheology import Elastic\n", - "\n", - "elastic_rheology = Elastic()\n", - "# Calculate the \"complex\" shear (really all Im[mu] = 0)\n", - "complex_shear = np.empty(radius_array.shape, dtype=np.complex128)\n", - "shear_array = np.ascontiguousarray(shear_array)\n", - "viscosity_array = np.ascontiguousarray(viscosity_array)\n", - "elastic_rheology.vectorize_modulus_viscosity(forcing_frequency, shear_array, viscosity_array, complex_shear)\n", - "\n", - "\n", - "nmax=18\n", - "\n", - "degs = np.arange(1,nmax+1)\n", - "kp_i = np.zeros(nmax)\n", - "hp_i = np.zeros(nmax)\n", - "kl_i = np.zeros(nmax)\n", - "hl_i = np.zeros(nmax)\n", - "\n", - "kp_c = np.zeros(nmax)\n", - "hp_c = np.zeros(nmax)\n", - "kl_c = np.zeros(nmax)\n", - "hl_c = np.zeros(nmax)\n", - "\n", - "\n", - "\n", - "for i in range(1,nmax+1):\n", - " radial_solution = \\\n", - " radial_solver(\n", - " radius_array,\n", - " density_array,\n", - " gravity_array,\n", - " bulk_mod_array,\n", - " complex_shear,\n", - " forcing_frequency,\n", - " planet_bulk_density,\n", - " layer_types=(\"liquid\",\"solid\",\"solid\"),\n", - " is_static_by_layer=(True,False,False),\n", - " is_incompressible_by_layer=(True,True,True),\n", - " upper_radius_by_layer=(3473E3,4500E3, 6371E3),\n", - " degree_l=i,\n", - " solve_for=('tidal','loading'),\n", - " use_kamata=True,\n", - " integration_method='DOP853',\n", - " integration_rtol = 1.0e-9,\n", - " integration_atol = 1.0e-9,\n", - " scale_rtols_by_layer_type = False,\n", - " max_num_steps = 10_000_000,\n", - " expected_size = 1000,\n", - " max_ram_MB = 1000,\n", - " max_step = 0,\n", - " limit_solution_to_radius = True,\n", - " nondimensionalize = True,\n", - " verbose = False,\n", - " raise_on_fail = True\n", - " )\n", - "\n", - " if i==1:\n", - " kp_i[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - " hp_i[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - " kl_i[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - " hl_i[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - " else:\n", - " kp_i[i-1] = radial_solution.k[0]\n", - " hp_i[i-1] = radial_solution.h[0]\n", - " kl_i[i-1] = radial_solution.k[1]\n", - " hl_i[i-1] = radial_solution.h[1]\n", - " \n", - " radial_solution = \\\n", - " radial_solver(\n", - " radius_array,\n", - " density_array,\n", - " gravity_array,\n", - " bulk_mod_array,\n", - " complex_shear,\n", - " forcing_frequency,\n", - " planet_bulk_density,\n", - " layer_types=(\"liquid\",\"solid\",\"solid\"),\n", - " is_static_by_layer=(True,False,False),\n", - " is_incompressible_by_layer=(False, False, False),\n", - " upper_radius_by_layer=(3473E3,4500E3, 6371E3),\n", - " degree_l=i,\n", - " solve_for=('tidal','loading'),\n", - " use_kamata=True,\n", - " integration_method='DOP853',\n", - " integration_rtol = 1.0e-9,\n", - " integration_atol = 1.0e-9,\n", - " scale_rtols_by_layer_type = False,\n", - " max_num_steps = 10_000_000,\n", - " expected_size = 1000,\n", - " max_ram_MB = 1000,\n", - " max_step = 500,\n", - " limit_solution_to_radius = True,\n", - " nondimensionalize = True,\n", - " verbose = False,\n", - " raise_on_fail = True\n", - " )\n", - "\n", - " if i==1:\n", - " kp_c[i-1] = radial_solution.k[0]-radial_solution.k[0]\n", - " hp_c[i-1] = radial_solution.h[0]-radial_solution.k[0]\n", - " kl_c[i-1] = radial_solution.k[1]-radial_solution.k[1]\n", - " hl_c[i-1] = radial_solution.h[1]-radial_solution.k[1]\n", - " else:\n", - " kp_c[i-1] = radial_solution.k[0]\n", - " hp_c[i-1] = radial_solution.h[0]\n", - " kl_c[i-1] = radial_solution.k[1]\n", - " hl_c[i-1] = radial_solution.h[1]\n", - " \n", - " print(i)\n", - "\n", - "\n", - "alt_n = np.array([1, 2, 3, 4, 5, 6, 8, 10, 18])\n", - "alt_h = -np.array([0.29, 1.001, 1.052, 1.053, 1.088, 1.147, 1.291, 1.433, 1.893])\n", - "alt_kn = -np.array([0, 0.615, 0.585, 0.528, 0.516, 0.535, 0.604, 0.682, 0.952])\n", - "\n", - "plt.figure(figsize=(12,6))\n", - "plt.subplot(1,2,1)\n", - "\n", - "# n1 = np.arange(0,11)\n", - "plt.plot(degs,hl_i,'-.',label=\"incompressible\",ms=8,color='red')\n", - "plt.plot(degs,hl_c,'-.',label=\"compressible\",ms=8,color='blue')\n", - "plt.plot(alt_n,alt_h,'x',label=\"Farrell (1972)\",ms=8,color='black')\n", - "plt.xticks(np.arange(0, nmax,1))\n", - "plt.xlim([1,nmax])\n", - "plt.tick_params(labelsize='large')\n", - "plt.title('Load Love Numbers $(h^\\prime)$',fontsize = 'xx-large')\n", - "plt.xlabel(r'$n$',fontsize='xx-large')\n", - "plt.grid()\n", - "\n", - "\n", - "\n", - "# Plot\n", - "plt.subplot(1,2,2)\n", - "plt.plot(degs,kl_i,'-.',label=\"incompressible\",ms=8,color='red')\n", - "plt.plot(degs,kl_c,'-.',label=\"compressible\",ms=8,color='blue')\n", - "plt.plot(alt_n,alt_kn/alt_n,'x',label=\"Farrell (1972)\",ms=8,color='black')\n", - "plt.xticks(np.arange(0, nmax,1))\n", - "plt.xlim([2,nmax])\n", - "plt.tick_params(labelsize='large')\n", - "plt.legend(loc='best',fontsize='12',ncol=1)\n", - "plt.title('Load Love Numbers $(k^\\prime)$',fontsize = 'xx-large')\n", - "plt.xlabel(r'$n$',fontsize='xx-large')\n", - "plt.grid()\n", - "plt.tight_layout()\n", - "\n", - "plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29a59ed3-514d-454e-ba89-d33591533718", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}