{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import smpl\n",
    "from smpl import plot\n",
    "from smpl import io\n",
    "from smpl import functions as f\n",
    "import numpy as np\n",
    "np.random.seed(1337)\n",
    "print(smpl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uncertainties.unumpy as unp\n",
    "data = np.loadtxt(io.find_file('test_linear_data.txt',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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.data(x,y,label=\"data\",fmt=None)\n",
    "plot.data(x,y,label=\"data\",fmt=\"step\",init=True)\n",
    "plot.data(x,y,label=\"data\",fmt=\"hist\",init=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.data(plot.unv(x),plot.unv(y),label=\"data\",fmt=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.random.randn(1000)\n",
    "print(isinstance(x, (list, tuple, np.ndarray)))\n",
    "plot.data(x,bins=20,label=\"data\",fmt=None)\n",
    "plot.data(x,bins=20,label=\"data\",fmt=\"step\",init=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually increased uncertainties by the scaling given by data_sigmas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.data(x,bins=20,data_sigmas=5,label=\"data\",fmt=None)\n",
    "plot.data(x,bins=20,data_sigmas=5,label=\"data\",fmt=\"step\",init=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}