{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is common practice in High Energy Physics (and other fields) to display error bars for each\n",
    " histogram bin, corresponding to either the associated statistical or systematic errors.  While the\n",
    " interpretation and meaning of the aforementioned error bars is a point of constant contention\n",
    " ([Those Deceiving Error Bars](http://www.science20.com/quantum_diaries_survivor/those_deceiving_error_bars-85735)), the entries in a histogram bin are typically seen as drawn     from a Poisson distribution, and thus the standard deviation for a bin with $N$ entries is $\\sqrt{N}$.\n",
    "\n",
    "### Error Bar Calculation\n",
    "\n",
    "\n",
    " Let us produce a simple histogram with error bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skhep.visual import MplPlotter as skh_plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8,6)\n",
    "\n",
    "np.random.seed(1001)\n",
    "data = np.random.normal(size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f085d68>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SI7rsiyRJs03Xx9SfC+wLnA5cD/wi8FbgyiRPr6ob23rnAnsCbwduA14KnJ3k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1073122e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = skh_plt.hist(data, bins=20, errorbars=True)\n",
    "plt.title('Simple Error Bar Example')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, when no weights are given, the error bars are calculated as $\\pm\\sqrt{N}$ above and below the bin height.  This is equivalent to using the argument `err_type='gaussian'` when calling the `hist` function.  If weights are included with the input data, then the default error bars are calculated as the root of the sum the square of the weights.  This is equivalent to declaring `err_type='sumW2'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f24af98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9t6td3PSB3YH/knQD6fOxlDR97TjS4K4/ZfvdCTwHfDmrsb8MLI6I/yHVAA8Epkm6lHTO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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1a3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JUuUM1pIkVc5gLUlS5QzWkiRVzmAtSVLlDNaSJFXOYC1JUuUM1pIkVc5gLUlS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\nLUlS5QzWkiRV7v8DRMrv1ZimxLEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1a3c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = np.random.uniform(0.1, 0.2, size=1000)\n",
    "_ = skh_plt.hist(data, bins=20, weights=w, errorbars=True)\n",
    "plt.title('Error Bars for Weighted Histogram, SumW2')\n",
    "plt.figure()\n",
    "_ = skh_plt.hist(data, bins=20, weights=w, errorbars=True, err_type='gaussian')\n",
    "plt.title('Error Bars for Weighted Histogram, sqrt(N)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error Bar Display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are currently two styles for displaying error bars, `err_style='band'` and `err_style='line'`.  By default, the `band` style is used for filled histograms, while the `line` style is used for step and marker histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f43fe10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wUtLCgnpdwGeBS4F3AxcDZyd/ZjZKcyYfMtA78L5XX8icyYfUOiQzGyOlnlO/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\nrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f301ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = np.random.normal(1,1, size=1000)\n",
    "data3 = np.random.normal(-1,1, size=1000)\n",
    "\n",
    "_ = skh_plt.hist(data, bins=20, errorbars=True)\n",
    "_ = skh_plt.hist(data2, bins=20, errorbars=True, histtype='step')\n",
    "_ = skh_plt.hist(data3, bins=20, errorbars=True, histtype='marker')\n",
    "plt.title('Various Histogram and Error Bar Draw Styles')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error bar color is handled automatically by default (`err_color='auto'`).  For a non-stacked histogram, the error bar color is selected to be a slightly darker version of the main histogram color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10f314d68>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwOnAhGgbnXPbwgu71/YjsB0Iv9XQeYABsyIOMQtoa2bH+MxHREREyuCr5+6c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f314f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = skh_plt.hist(data3, bins=20, errorbars=True, color='C6', label='auto color')\n",
    "_ = skh_plt.hist(data2, bins=20, errorbars=True, color='C8', err_color='C9', label='specified color')\n",
    "plt.xlim(-4,8)\n",
    "plt.title('Color Options')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For stacked histograms, the error bars are determined by the sum of all contributions for an individual bin, and placed around the total bin height.  If the color mode is automatic, the error bar color is chosen to be the next in the current color cycle, instead of being a modified version of the currently displayed colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f7424e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n9VLRUM/MV3bZ9PiS7yv1w1OrVrLXDlsWqzdlYEqxWuPZLnvus+FGQ7Dk9u8U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\n4cAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2ce240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LmXkbQESc2Ou+NOzX6uVA/T/YIiJmAwcCfwrcA/xVb3s0Pu2Dv8zMfskKQ71F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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f439b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = skh_plt.hist([data3, data2], bins=20, errorbars=True)\n",
    "plt.title('Unstacked')\n",
    "plt.figure()\n",
    "_ = skh_plt.hist([data3, data2], bins=20, errorbars=True, stacked = True)\n",
    "plt.title('Stacked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale and Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If scale and/or normalization is applied to the histogram, the error bars are scaled appropriately as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10fa57748>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NUmcVsH1A+UzZ3oPulOR44HiA/fbbbwfDlCSpe0bWUk9yNPBs4CjgliS7J9kd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\nkiR1hEldkqSO+P/xS+Bv6yoCDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f0d0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = skh_plt.hist(data2, bins=20, errorbars=True, histtype='step', scale=0.5)\n",
    "_ = skh_plt.hist(data2, bins=20, errorbars=True, histtype='step')\n",
    "\n",
    "plt.title('Histogram and scaled histogram')"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
