{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic fit function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from smpl import plot\n",
    "from smpl import stat\n",
    "from smpl import io\n",
    "from smpl import functions as f\n",
    "import uncertainties.unumpy as unp\n",
    "import smpl\n",
    "smpl.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in ['test_linear_data.txt', 'test_quad_data.txt']:\n",
    "    data = np.loadtxt(io.find_file(n,3))\n",
    "    xdata = data[:,0]\n",
    "    xerr = data[:,2]\n",
    "    ydata = data[:,1]\n",
    "    yerr = data[:,3]\n",
    "    x = unp.uarray(xdata,xerr)\n",
    "    y = unp.uarray(ydata,yerr)\n",
    "\n",
    "\n",
    "    function,fitparams,lfunc = plot.auto(xdata, ydata, fmt='.', label='data', xaxis=\"x in a.u.\",yaxis=\"y in a.u.\",sigmas=1,epsfcn=0.00001,maxfev=1000000,init=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.linspace(-5,5,100)\n",
    "y = stat.noisy(np.exp(2*x))\n",
    "ff = plot.auto(x, y, fmt='.', label='data', xaxis=\"x in a.u.\",yaxis=\"y in a.u.\",sigmas=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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