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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 3, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "import numpy as np\n", |
| 10 | + "import sympy as sp\n", |
| 11 | + "\n", |
| 12 | + "from sumpy.expansion.diff_op import (\n", |
| 13 | + " make_identity_diff_op,\n", |
| 14 | + ")\n", |
| 15 | + "from collections import namedtuple\n", |
| 16 | + "DerivativeIdentifier = namedtuple(\"DerivativeIdentifier\", [\"mi\", \"vec_idx\"])\n", |
| 17 | + "\n", |
| 18 | + "from sumpy.recurrence import _make_sympy_vec, get_reindexed_and_center_origin_on_axis_recurrence\n", |
| 19 | + "\n", |
| 20 | + "from immutabledict import immutabledict\n", |
| 21 | + "from sumpy.expansion.diff_op import LinearPDESystemOperator" |
| 22 | + ] |
| 23 | + }, |
| 24 | + { |
| 25 | + "cell_type": "code", |
| 26 | + "execution_count": 5, |
| 27 | + "metadata": {}, |
| 28 | + "outputs": [], |
| 29 | + "source": [ |
| 30 | + "var = _make_sympy_vec(\"x\", 2)\n", |
| 31 | + "var_t = _make_sympy_vec(\"t\", 2)\n", |
| 32 | + "abs_dist = sp.sqrt((var[0]-var_t[0])**2 + (var[1]-var_t[1])**2)\n", |
| 33 | + "w = make_identity_diff_op(2)\n", |
| 34 | + "\n", |
| 35 | + "partial_1x = DerivativeIdentifier((4,0), 0)\n", |
| 36 | + "partial_1y = DerivativeIdentifier((0,4), 0)\n", |
| 37 | + "biharmonic_op = {partial_1x: 1, partial_1y: 1}\n", |
| 38 | + "list_pde = immutabledict(biharmonic_op)\n", |
| 39 | + "\n", |
| 40 | + "biharmonic_pde = LinearPDESystemOperator(2, (list_pde,))\n", |
| 41 | + "g_x_y = abs_dist**2 * (sp.log(abs_dist)-1)\n", |
| 42 | + "\n", |
| 43 | + "n_init, _, r = get_reindexed_and_center_origin_on_axis_recurrence(biharmonic_pde)\n", |
| 44 | + "\n", |
| 45 | + "derivs = [sp.diff(g_x_y,\n", |
| 46 | + " var_t[0], i).subs(var_t[0], 0).subs(var_t[1], 0)\n", |
| 47 | + " for i in range(8)]\n", |
| 48 | + "\n", |
| 49 | + "x_coord = np.random.rand() # noqa: NPY002\n", |
| 50 | + "y_coord = np.random.rand() # noqa: NPY002\n", |
| 51 | + "coord_dict = {var[0]: var[0], var[1]: var[1]}\n", |
| 52 | + "derivs = [d.subs(coord_dict) for d in derivs]\n", |
| 53 | + "\n", |
| 54 | + "n = sp.symbols(\"n\")\n", |
| 55 | + "s = sp.Function(\"s\")\n", |
| 56 | + "\n", |
| 57 | + "# pylint: disable-next=not-callable\n", |
| 58 | + "subs_dict = {s(0): derivs[0], s(1): derivs[1], s(2): derivs[1], s(3): derivs[1]}\n", |
| 59 | + "check = []\n", |
| 60 | + "\n", |
| 61 | + "assert n_init == 4\n", |
| 62 | + "max_order_check = 8\n", |
| 63 | + "for i in range(n_init, max_order_check):\n", |
| 64 | + " check.append(r.subs(n, i).subs(subs_dict) - derivs[i])\n", |
| 65 | + " # pylint: disable-next=not-callable\n", |
| 66 | + " subs_dict[s(i)] = derivs[i]" |
| 67 | + ] |
| 68 | + }, |
| 69 | + { |
| 70 | + "cell_type": "code", |
| 71 | + "execution_count": 7, |
| 72 | + "metadata": {}, |
| 73 | + "outputs": [], |
| 74 | + "source": [ |
| 75 | + "max_order_check = 8\n", |
| 76 | + "for i in range(n_init, max_order_check):\n", |
| 77 | + " check.append(r.subs(n, i).subs(subs_dict) - derivs[i])\n", |
| 78 | + " # pylint: disable-next=not-callable\n", |
| 79 | + " subs_dict[s(i)] = derivs[i]" |
| 80 | + ] |
| 81 | + }, |
| 82 | + { |
| 83 | + "cell_type": "code", |
| 84 | + "execution_count": 14, |
| 85 | + "metadata": {}, |
| 86 | + "outputs": [ |
| 87 | + { |
| 88 | + "data": { |
| 89 | + "text/latex": [ |
| 90 | + "$\\displaystyle 16.9964566798618$" |
| 91 | + ], |
| 92 | + "text/plain": [ |
| 93 | + "16.9964566798618" |
| 94 | + ] |
| 95 | + }, |
| 96 | + "execution_count": 14, |
| 97 | + "metadata": {}, |
| 98 | + "output_type": "execute_result" |
| 99 | + } |
| 100 | + ], |
| 101 | + "source": [ |
| 102 | + "r.subs(n, 4).subs(subs_dict).subs({var[0]: 1.2, var[1]: 2.3})" |
| 103 | + ] |
| 104 | + }, |
| 105 | + { |
| 106 | + "cell_type": "code", |
| 107 | + "execution_count": 6, |
| 108 | + "metadata": {}, |
| 109 | + "outputs": [ |
| 110 | + { |
| 111 | + "data": { |
| 112 | + "text/plain": [ |
| 113 | + "{s(0): (x0**2 + x1**2)*(log(sqrt(x0**2 + x1**2)) - 1),\n", |
| 114 | + " s(1): -2*x0*(log(sqrt(x0**2 + x1**2)) - 1) - x0,\n", |
| 115 | + " s(2): -2*x0*(log(sqrt(x0**2 + x1**2)) - 1) - x0,\n", |
| 116 | + " s(3): -2*x0*(log(sqrt(x0**2 + x1**2)) - 1) - x0,\n", |
| 117 | + " s(4): 2*(-24*x0**4/(x0**2 + x1**2)**2 + 8*x0**2*(4*x0**2/(x0**2 + x1**2) - 3)/(x0**2 + x1**2) + 12*x0**2/(x0**2 + x1**2) + 3)/(x0**2 + x1**2),\n", |
| 118 | + " s(5): -4*x0*(-24*x0**4/(x0**2 + x1**2)**2 + 40*x0**2/(x0**2 + x1**2) - 15)/(x0**2 + x1**2)**2,\n", |
| 119 | + " s(6): 12*(-320*x0**6/(x0**2 + x1**2)**3 + 360*x0**4/(x0**2 + x1**2)**2 + 24*x0**2*(16*x0**4/(x0**2 + x1**2)**2 - 20*x0**2/(x0**2 + x1**2) + 5)/(x0**2 + x1**2) - 60*x0**2/(x0**2 + x1**2) - 5)/(x0**2 + x1**2)**2,\n", |
| 120 | + " s(7): -48*x0*(-160*x0**6/(x0**2 + x1**2)**3 + 336*x0**4/(x0**2 + x1**2)**2 - 210*x0**2/(x0**2 + x1**2) + 35)/(x0**2 + x1**2)**3}" |
| 121 | + ] |
| 122 | + }, |
| 123 | + "execution_count": 6, |
| 124 | + "metadata": {}, |
| 125 | + "output_type": "execute_result" |
| 126 | + } |
| 127 | + ], |
| 128 | + "source": [ |
| 129 | + "subs_dict" |
| 130 | + ] |
| 131 | + }, |
| 132 | + { |
| 133 | + "cell_type": "code", |
| 134 | + "execution_count": null, |
| 135 | + "metadata": {}, |
| 136 | + "outputs": [], |
| 137 | + "source": [] |
| 138 | + } |
| 139 | + ], |
| 140 | + "metadata": { |
| 141 | + "kernelspec": { |
| 142 | + "display_name": "inteq", |
| 143 | + "language": "python", |
| 144 | + "name": "python3" |
| 145 | + }, |
| 146 | + "language_info": { |
| 147 | + "codemirror_mode": { |
| 148 | + "name": "ipython", |
| 149 | + "version": 3 |
| 150 | + }, |
| 151 | + "file_extension": ".py", |
| 152 | + "mimetype": "text/x-python", |
| 153 | + "name": "python", |
| 154 | + "nbconvert_exporter": "python", |
| 155 | + "pygments_lexer": "ipython3", |
| 156 | + "version": "3.11.9" |
| 157 | + } |
| 158 | + }, |
| 159 | + "nbformat": 4, |
| 160 | + "nbformat_minor": 2 |
| 161 | +} |
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