{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Model evaluation\n", "=============================" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from __future__ import print_function, division\n", "from PyAstronomy import funcFit2 as fuf2\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "\n", "# Create a a model object representing a Gaussian\n", "gf = fuf2.GaussFit()\n", "\n", "# Parameters can be accessed using the square bracket\n", "# notation\n", "gf[\"A\"] = 10.0\n", "gf[\"sig\"] = 15.77\n", "gf[\"off\"] = 1.0\n", "gf[\"mu\"] = 7.5\n", "\n", "x = np.linspace(gf[\"mu\"]-5*gf[\"sig\"], gf[\"mu\"]+5*gf[\"sig\"], 150)\n", "y = gf.evaluate(x)\n", "\n", "gf[\"A\"] = 7.5\n", "y2 = gf.evaluate(x)\n", "\n", "plt.plot(x, y, 'b.-', label=\"A=10\")\n", "plt.plot(x, y2, 'r.-', label=\"A=7.5\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }