{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cbc304b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8a3c151",
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd ../book\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b23d795",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean(filename='', ax=None, yticks=None, ylabel=None):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    \n",
    "    if ylabel is None:\n",
    "        ax.set_ylabel(\"Frequency (Absolute)\")\n",
    "    ax.set_xlabel(\"Minutes of TV Watched\")\n",
    "    #ax.set_title(\"\")\n",
    "    \n",
    "    if yticks is not None:\n",
    "        ax.set_yticks(yticks)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    if filename:\n",
    "        plt.savefig('images/' + filename.rstrip(\".svg\")+'.svg', \n",
    "                   transparent = True,\n",
    "                    format = 'svg',\n",
    "                    bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "style = {\"bins\": 15,\n",
    "        \"ec\": 'black'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80198deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 2000\n",
    "\n",
    "ndata = np.random.normal(size = n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b78449a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist( 20 * (ndata - np.min(ndata)), **style)\n",
    "clean('normal_histogram', yticks = [], ylabel = '')\n",
    "#ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5b56fd1a",
   "metadata": {},
   "source": [
    "edata = np.random.exponential(size = n)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(edata, **style)\n",
    "clean('longright')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3c753429",
   "metadata": {},
   "source": [
    "edata = np.random.exponential(size = n)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(-edata + np.max(edata), **style)\n",
    "clean('longleft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d23ffc85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666.667x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 1.2\n",
    "fig, axs = plt.subplots(1,2, sharey = True, figsize = (8/k,3/k))\n",
    "\n",
    "ax = axs[1]\n",
    "\n",
    "edata = np.random.exponential(size = n, scale = 44)\n",
    "ax.hist(edata, **style, density = True, color = 'C2')\n",
    "y_ticks = ax.get_yticks()\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(['{:.0f}'.format(y*100) for y in y_ticks])\n",
    "ax.set_xlabel(\"Minutes of TV Watched\")\n",
    "ax.set_yticks([])\n",
    "\n",
    "#clean('longright')\n",
    "\n",
    "# Right panel\n",
    "ax = axs[0]\n",
    "ax.hist(-edata + np.max(edata), **style, density = True, color = 'C4')\n",
    "y_ticks = ax.get_yticks()\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(['{:.0f}'.format(y*100) for y in y_ticks])\n",
    "ax.set_xlabel(\"Minutes of TV Watched\")\n",
    "\n",
    "plt.suptitle(\"Skewed Distributions\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"images/skewed_histograms.svg\", format = 'svg', transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39dbd4fd",
   "metadata": {},
   "source": [
    "# Income"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f365a418",
   "metadata": {},
   "source": [
    "ax = axs[1]\n",
    "e = 10**-10\n",
    "b = -e, 10+e, 20 + e, 30+e,  216.6\n",
    "ax.hist(incomes, **sty,\n",
    "       density = True,\n",
    "       bins = b)\n",
    "\n",
    "ax = axs[2]\n",
    "e = 10**-10\n",
    "b = -e, 10+e, 20 + e, 30+e,  215.5, 216.5\n",
    "ax.hist(incomes, **sty,\n",
    "       density = True,\n",
    "       bins = b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fd957044",
   "metadata": {},
   "outputs": [],
   "source": [
    "incomes = 0, 10, 10, 20, 20, 20, 30, 216"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8f1fac9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,1, sharex = False,\n",
    "                       figsize = (6,4.5))\n",
    "\n",
    "sty = {\"ec\": 'black',\n",
    "      \"zorder\": 10}\n",
    "\n",
    "ax = axs[0]\n",
    "b = -0.5, 0.5, 9.5, 10.5, 19.5, 20.5, 29.5, 30.5, 215.5, 216.6\n",
    "ax.hist(incomes, **sty,\n",
    "       density = True,\n",
    "       bins = b)\n",
    "\n",
    "ax = axs[1]\n",
    "b = -e, 10+e, 20 + e, 30+e,  216-5, 216+5\n",
    "b = -5, 5, 15, 25, 35, 216-5, 216+5\n",
    "ax.hist(incomes, **sty,\n",
    "       density = True,\n",
    "       bins = b)\n",
    "\n",
    "ax = axs[2]\n",
    "b = -e, 10+e, 20 + e, 30+e, 216+5\n",
    "b = -5, 5, 15, 25, 35, 216+5\n",
    "ax.hist(incomes, **sty,\n",
    "       density = True,\n",
    "       bins = b)\n",
    "\n",
    "# yticks\n",
    "axs[0].set_ylim(0, .425)\n",
    "axs[0].set_yticks([0, .125, .25, .375])\n",
    "    \n",
    "axs[1].set_ylim(0, .0425)\n",
    "axs[1].set_yticks([0, .0125, .025, .0375])\n",
    " \n",
    "axs[2].set_ylim(0, .0425)\n",
    "axs[2].set_yticks([0, .0125, .025, .0375])\n",
    "axs[2].set_xlabel(\"Income ($)\")\n",
    "#axs[3].set_ylim(0, .0425)\n",
    "#axs[3].set_yticks([0, .0125, .025, .0375])\n",
    " \n",
    "    \n",
    "for ax in axs:\n",
    "    ax.set_xticks( [0,10,20,30,216] )\n",
    "    #ax.set_yticks([0,1,2,3,4,5])\n",
    "    ax.yaxis.grid(True, zorder = -10)\n",
    "    y_ticks = ax.get_yticks()\n",
    "    ax.set_yticks(y_ticks)\n",
    "    ax.set_yticklabels(['{:.2f}'.format(y*100) for y in y_ticks])\n",
    "    \n",
    "\n",
    "\n",
    "axs[0].set_title(\"Income Histograms\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"images/income_histograms.svg\", format = 'svg', transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6e551e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.hist(incomes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b92387",
   "metadata": {},
   "source": [
    "# Heights and Crowding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f271a888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.08333333, 0.        , 0.        , 0.02777778, 0.        ,\n",
       "        0.        , 0.02777778, 0.        , 0.        , 0.02777778]),\n",
       " array([10., 16., 22., 28., 34., 40., 46., 52., 58., 64., 70.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = 10,10,10, 30, 50, 70\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.hist(d,\n",
    "       density = True,\n",
    "       ec = 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f976f41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff6f0ba9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a0c2674b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = 0, 0, 1, 1, 2, 6, 10, 10, 10, 10\n",
    "b = np.linspace(-0.5, 10.5, 12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51ae2ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style2 = style.copy()\n",
    "style2['bins'] = b\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(data,\n",
    "        **style2,\n",
    "       zorder = 10)\n",
    "ax.set_xticks(range(0,11))\n",
    "ax.yaxis.grid(True, zorder = -10)\n",
    "clean(yticks = range(0,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b941b98c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7722cfe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style2 = style.copy()\n",
    "style2['bins'] = [-0.5,  0.5,  1.5,  2.5,  3.5,  4.5, 10.5] \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(data,\n",
    "        **style2,\n",
    "       zorder = 10,\n",
    "       density = True)\n",
    "ax.set_xticks(range(0,11))\n",
    "ax.yaxis.grid(True, zorder = -10)\n",
    "clean(ylabel = '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7db8c8f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style2 = style.copy()\n",
    "style2['bins'] = [-0.5,  0.5,  1.5,  2.5,  3.5,  4.5, 10.5] \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(data,\n",
    "        **style2,\n",
    "       zorder = 10,\n",
    "       density = True)\n",
    "ax.set_xticks(range(0,11))\n",
    "ax.yaxis.grid(True, zorder = -10)\n",
    "clean(yticks = range(0,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d042eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Sample data\n",
    "data = np.random.normal(0, 1, 1000)\n",
    "\n",
    "# Create histogram with density=True\n",
    "plt.hist(data, bins=30, density=True)\n",
    "\n",
    "# Get current y-ticks\n",
    "y_ticks = plt.gca().get_yticks()\n",
    "\n",
    "# Set new y-ticks (convert density to percentage)\n",
    "plt.gca().set_yticks(y_ticks)\n",
    "plt.gca().set_yticklabels(['{:.0f}%'.format(y*100) for y in y_ticks])\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Histogram in Percentage')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0365bf",
   "metadata": {},
   "source": [
    "# Controlling for Variables\n",
    "\n",
    "Recall a confounder is a variable that can distort the comparion of two groups. In an observational study, it's a good idea to compare the distributions for confounders between your treatment and control group.  \n",
    "\n",
    "When a potential confounder is quantitative, this can be done by overlaying the histograms. \n",
    "\n",
    "\n",
    "\n",
    "```{figure} images/histogram_goncalves_mello.png\n",
    ":width: 66%\n",
    ":name: speedingrace\n",
    "\n",
    "Figure 2 from {cite}`goncalves2021few`.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d870927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.01981578, 0.09907891, 0.15852626, 0.27742096, 0.25760518,\n",
       "        0.49539457, 0.31705253, 0.15852626, 0.15852626, 0.03963157]),\n",
       " array([-2.40833931, -1.90369107, -1.39904284, -0.8943946 , -0.38974636,\n",
       "         0.11490188,  0.61955012,  1.12419836,  1.6288466 ,  2.13349484,\n",
       "         2.63814308]),\n",
       " [<matplotlib.patches.Polygon at 0x1221d9750>])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = np.random.normal(size = 100)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.hist(d,\n",
    "        color = 'C0',\n",
    "        histtype = 'step',\n",
    "        ls = 'dotted',\n",
    "        lw = 3,\n",
    "        density = True)\n",
    "\n",
    "ax.hist(d + .3,\n",
    "        color = 'C1',\n",
    "        histtype = 'step',\n",
    "        ls = 'dashed',\n",
    "        lw = 3,\n",
    "        density = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2db652d",
   "metadata": {},
   "source": [
    "# Exercies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6dab5490",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7558/239119586.py:16: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(range(0,26, 5))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = 1, 2, 3, 4\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.hist(d,\n",
    "        ec = 'black',\n",
    "        fc = 'C0',\n",
    "        density = True,\n",
    "       bins = (1,5),\n",
    "       zorder = 10)\n",
    "\n",
    "ax.set_xlim(0,6)\n",
    "#ax.set_yticks([])\n",
    "ax.yaxis.grid(True, zorder = -10)\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticklabels(range(0,26, 5))\n",
    "ax.set_xlabel(\"# Houses Owned\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"images/oneblockhist.svg\", format = 'svg', transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bde16cf",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}