{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lambdify in symbolic module\n", "### Importing required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sympy\n", "from sympy.abc import x, y\n", "from sympy import symbols\n", "from einsteinpy.symbolic import BaseRelativityTensor\n", "\n", "sympy.init_printing()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating a Base Relativity Tensor" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "syms = symbols(\"x y\")\n", "x, y = syms\n", "T = BaseRelativityTensor([[x, 1],[0, x+y]], syms, config=\"l\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calling the lambdify function" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEEAAAAVBAMAAADrxp6XAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAInZUiRDNmWbvRN27qzJGkhbKAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABKElEQVQoFX2QP0jDUBCHv+aPMSatAUeX0MVJ6OCuiLgJoYtLKwERHDuIVEF8To4G3ZxEwcUhhY4u1V0ozoIu7ro4ipcHARNtHry7d/f73t29BwRMXlozWpMBrjNtqQIgu27HVQQ74KhK4hTcSgA34raamFLsCdFe3Forv8heCOmCNWZXiJ7x7AxKtSwz4QymY4ZiA+OzrkrEnBPzAuaAS2jgxyUdIjfkXLSPjIDZ8A9BGjW+YSZhO9PSwI7KzD5Gors8QufqFUvqFpkn/LHMOGIDlt8PuMH+KhJHpEq+POQONptmvwX3xSLd1eMAvADvLb9qFglYF2VeGvVyQkb5tZzYvpCwL/shT7fzg/ZeWF+B2kiCjk6IUflBe7N5KN7POteUzvxvTuAH/84xnAuhLykAAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left ( x, \\quad y\\right )$$" ], "text/plain": [ "(x, y)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "args, func = T.tensor_lambdify()\n", "args" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`args` indicates the order in which arguments should be passed to the returned function `func`\n", "\n", "### Executing the returned function for some value" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$$\\left [ \\left [ 2, \\quad 1\\right ], \\quad \\left [ 0, \\quad 3\\right ]\\right ]$$" ], "text/plain": [ "[[2, 1], [0, 3]]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "func(2, 1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }