{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom fit function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from smpl import plot\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": [
    "data = data_malus = np.loadtxt(io.find_file(\"test_custom_data.csv\",3),skiprows = 1, delimiter=',')\n",
    "ydata = unp.uarray(data[:,0],data[:,1])\n",
    "xdata = unp.uarray(data[:,2],data[:,3])\n",
    "plot.data(xdata,ydata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial guess\n",
    "params allows to set initial guess values for fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Malus(x,I_max,I_min,w,p):\n",
    "    ''' $(I_{max}-I_{min})*\\\\cos(w*x-p)^2+I_{min} $'''\n",
    "    return (I_max-I_min)*unp.cos(w*x-p)**2+I_min\n",
    "\n",
    "plot.fit((xdata),ydata,Malus,xaxis=\"anlge in °\", yaxis=\"intensity in a.u.\", params=[4.6,0.11,0.0175,180+90-137], units=[\"a.u.\",\"a.u.\",\"rad$^{-1}$\",\"rad\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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