{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JNB Lab Solutions\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1a**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Read in Homicide data and drop rows that have missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date of Incident</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>Primary Cause</th>\n",
       "      <th>Residence City</th>\n",
       "      <th>Incident Zip Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-02-26 10:48:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>MULTIPLE GUNSHOT WOUNDS</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60623.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date of Incident   Age Gender   Race            Primary Cause  \\\n",
       "0 2017-02-26 10:48:00  23.0   Male  Black  MULTIPLE GUNSHOT WOUNDS   \n",
       "\n",
       "  Residence City  Incident Zip Code  \n",
       "0        Chicago            60623.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hom=pd.read_excel('homicides.xlsx') #read data file\n",
    "Hom=Hom[Hom[\"Manner of Death\"]=='HOMICIDE']\n",
    "Hom=Hom[[\"Date of Incident\",\"Age\",\"Gender\",\"Race\",\"Primary Cause\",\"Residence City\",\"Incident Zip Code\"]]\n",
    "Hom=Hom.dropna()\n",
    "Hom.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Extract the month and year of homicide incidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date of Incident</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>Primary Cause</th>\n",
       "      <th>Residence City</th>\n",
       "      <th>Incident Zip Code</th>\n",
       "      <th>new_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-02-26 10:48:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>MULTIPLE GUNSHOT WOUNDS</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date of Incident   Age Gender   Race            Primary Cause  \\\n",
       "0 2017-02-26 10:48:00  23.0   Male  Black  MULTIPLE GUNSHOT WOUNDS   \n",
       "\n",
       "  Residence City  Incident Zip Code    new_date  year  month  day  \n",
       "0        Chicago            60623.0  2017-02-26  2017      2   26  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "Hom['new_date'] = [d.date() for d in Hom['Date of Incident']] \n",
    "dates=Hom['new_date'].values\n",
    "year=[my_str.year for my_str in dates]\n",
    "month=[my_str.month for my_str in dates]\n",
    "day=[my_str.day for my_str in dates]\n",
    "Hom['year'] = year\n",
    "Hom['month'] = month\n",
    "Hom['day'] = day\n",
    "Hom.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Get Chicago data, select the columns [\"Age\",\"Gender\",\"Race\",\"Primary Cause\",\"Incident Zip Code\",\"year\",\"month\",\"day\"]  and use integer format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>Primary Cause</th>\n",
       "      <th>Incident Zip Code</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>MULTIPLE GUNSHOT WOUNDS</td>\n",
       "      <td>60623</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Gender   Race            Primary Cause  Incident Zip Code  year  month  \\\n",
       "0   23   Male  Black  MULTIPLE GUNSHOT WOUNDS              60623  2017      2   \n",
       "\n",
       "   day  \n",
       "0   26  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChiHom=Hom[Hom[\"Residence City\"]=='Chicago'] #get data for just Chicago\n",
    "df=ChiHom[[\"Age\",\"Gender\",\"Race\",\"Primary Cause\",\"Incident Zip Code\",\"year\",\"month\",\"day\"]]\n",
    "df=df.dropna()\n",
    "df['year']=df['year'].astype(int)\n",
    "df['month']=df['month'].astype(int)\n",
    "df['day']=df['day'].astype(int)\n",
    "df['Age']=df['Age'].astype(int)\n",
    "df['Incident Zip Code']=df['Incident Zip Code'].astype(int)\n",
    "df=df[df[\"year\"]>2014]\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Get the number of Chicago homicides in July 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chicago Homicides In July 2020:  89\n"
     ]
    }
   ],
   "source": [
    "july=df[df[\"month\"]==7]\n",
    "july20=july[july[\"year\"]==2020]\n",
    "print(\"Chicago Homicides In July 2020: \",len(july20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Get the number of homicides which occured on each day of July between 2015 and 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    1\n",
      "2    3\n",
      "3    1\n",
      "4    4\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "julydf=july\n",
    "julydf=julydf.reset_index(drop=True)\n",
    "julycount=pd.Series(np.arange(0,155,1))\n",
    "julycount[:]=0\n",
    "j=0\n",
    "for year in np.arange(2015,2020,1):\n",
    "    for day in np.arange(1,32,1):\n",
    "        for i in julydf.index:\n",
    "            if julydf.loc[i,\"day\"]==day and julydf.loc[i,\"year\"]==year:\n",
    "                julycount[j]=julycount[j]+1\n",
    "        j=j+1\n",
    "print(julycount[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Get frequency distributions for the number of homicides in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.200000\n",
      "1    0.309677\n",
      "2    0.258065\n",
      "3    0.141935\n",
      "4    0.051613\n",
      "5    0.012903\n",
      "6    0.012903\n",
      "7    0.012903\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jm=julycount.max()\n",
    "julyfreq=pd.Series(np.arange(0,jm+1,1))  #create a series with index the number of homicides and value equal to the frequency\n",
    "julyfreq[:]=0\n",
    "dj=julycount\n",
    "for i in julycount.index:\n",
    "    for j in np.arange(0,jm+1,1):\n",
    "        if dj[i]==j:\n",
    "            julyfreq[j]=julyfreq[j]+1\n",
    "s=julyfreq.sum()\n",
    "julyfreq=julyfreq/s\n",
    "print(julyfreq)\n",
    "julyfreq[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) Do a Monte Carlo simulation for monthly homicide counts  in July based on 31 random draws from the respective empirical distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "n = random.random()\n",
    "nummonths=10000\n",
    "numdays=31*nummonths\n",
    "julydaytotal=pd.Series(np.arange(0,numdays,1))\n",
    "julydaytotal[:]=0\n",
    "\n",
    "for i in np.arange(0,numdays,1):\n",
    "    x=random.random()\n",
    "    exit=0\n",
    "    cum=0\n",
    "    for j in julyfreq.index:\n",
    "        if x< cum+julyfreq[j] and exit==0:\n",
    "            julydaytotal[i]=j\n",
    "            exit=1\n",
    "        else:\n",
    "            cum=cum+julyfreq[j]\n",
    "\n",
    "\n",
    "julymonths=pd.Series(np.arange(0,nummonths,1))\n",
    "julymonths[:]=0\n",
    "i=0\n",
    "for month in np.arange(0,nummonths,1):\n",
    "    sum=0\n",
    "    for j in np.arange(0,31,1):\n",
    "        sum=sum+julydaytotal[i]\n",
    "        i=i+1\n",
    "    julymonths[month]=sum\n",
    "    month=month+1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8) Make a histogram for July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '10,000 Simulated July Homicide Counts based on 2015-2019 Data')"
      ]
     },
     "execution_count": 9,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "#Make Histogram\n",
    "fig, ax = plt.subplots()\n",
    "n,bins,patches = ax.hist(julymonths, bins=10,density=1,alpha=.5)\n",
    "plt.xlabel(\"July Homicide Count\")\n",
    "plt.ylabel(\"Proportion\")\n",
    "plt.title(\"10,000 Simulated July Homicide Counts based on 2015-2019 Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1b**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Find the empirical p-value of getting at least 108 homicides (the number in July 2020.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probaility that a random draw of 31 days from July 2015-2020 distibution results in 85 or more homicides: 0.0\n"
     ]
    }
   ],
   "source": [
    "julycount=julymonths[julymonths>=108]\n",
    "pjuly=len(julycount)/10000\n",
    "print(\"Probaility that a random draw of 31 days from July 2015-2020 distibution results in 85 or more homicides:\",pjuly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a) Make a histogram of the difference July homicide count - May homicide count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "maydf=df[df[\"month\"]==5]\n",
    "maydf=maydf.reset_index(drop=True)\n",
    "maycount=pd.Series(np.arange(0,155,1))\n",
    "maycount[:]=0\n",
    "j=0\n",
    "for year in np.arange(2015,2020,1):\n",
    "    for day in np.arange(1,32,1):\n",
    "        for i in maydf.index:\n",
    "            if maydf.loc[i,\"day\"]==day and maydf.loc[i,\"year\"]==year:\n",
    "                maycount[j]=maycount[j]+1\n",
    "        j=j+1\n",
    "mm=maycount.max()\n",
    "mayfreq=pd.Series(np.arange(0,mm+1,1))  #create a series with index the number of homicides and value equal to the frequency\n",
    "mayfreq[:]=0\n",
    "dm=maycount\n",
    "for i in maycount.index:\n",
    "    for j in np.arange(0,mm+1,1):\n",
    "        if dm[i]==j:\n",
    "            mayfreq[j]=mayfreq[j]+1\n",
    "s=mayfreq.sum()\n",
    "mayfreq=mayfreq/s\n",
    "n = random.random()\n",
    "nummonths=10000\n",
    "numdays=31*nummonths\n",
    "maydaytotal=pd.Series(np.arange(0,numdays,1))\n",
    "maydaytotal[:]=0\n",
    "\n",
    "for i in np.arange(0,numdays,1):\n",
    "    x=random.random()\n",
    "    exit=0\n",
    "    cum=0\n",
    "    for j in mayfreq.index:\n",
    "        if x< cum+mayfreq[j] and exit==0:\n",
    "            maydaytotal[i]=j\n",
    "            exit=1\n",
    "        else:\n",
    "            cum=cum+mayfreq[j]\n",
    "\n",
    "\n",
    "maymonths=pd.Series(np.arange(0,nummonths,1))\n",
    "maymonths[:]=0\n",
    "i=0\n",
    "for month in np.arange(0,nummonths,1):\n",
    "    sum=0\n",
    "    for j in np.arange(0,31,1):\n",
    "        sum=sum+maydaytotal[i]\n",
    "        i=i+1\n",
    "    maymonths[month]=sum\n",
    "    month=month+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff=julymonths-maymonths\n",
    "import matplotlib.pyplot as plt\n",
    "#Make Histogram\n",
    "fig, ax = plt.subplots()\n",
    "n,bins,patches = ax.hist(diff, bins=10,density=1,alpha=.5)\n",
    "plt.xlabel(\"Difference in Homicide Counts\")\n",
    "plt.ylabel(\"Proportion\")\n",
    "plt.title(\"Simulated Difference between the May and July Homicide Counts\")\n",
    "plt.savefig(\"Diff.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the probability that July will have at least n more homicides than May $(0\\le n \\le 20)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.7409\n",
       "1     0.7091\n",
       "2     0.6766\n",
       "3     0.6436\n",
       "4     0.6077\n",
       "5     0.5694\n",
       "6     0.5298\n",
       "7     0.4885\n",
       "8     0.4529\n",
       "9     0.4168\n",
       "10    0.3796\n",
       "11    0.3436\n",
       "12    0.3082\n",
       "13    0.2773\n",
       "14    0.2459\n",
       "15    0.2171\n",
       "16    0.1889\n",
       "17    0.1677\n",
       "18    0.1461\n",
       "19    0.1246\n",
       "20    0.1045\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdiff=pd.Series(np.arange(0,21,1))\n",
    "for i in pdiff.index:\n",
    "    pdiff.loc[i]=len(diff[diff>=i])/10000\n",
    "pdiff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Make a plot of these probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "window.mpl = {};\n",
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       "        return MozWebSocket;\n",
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       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
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       "\n",
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       "\n",
       "    this.context = undefined;\n",
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       "\n",
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       "    this._init_toolbar(this);\n",
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       "    var fig = this;\n",
       "\n",
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       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
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       "\n",
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       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
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       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "function getModifiers(event) {\n",
       "    var mods = [];\n",
       "    if (event.ctrlKey) {\n",
       "        mods.push('ctrl');\n",
       "    }\n",
       "    if (event.altKey) {\n",
       "        mods.push('alt');\n",
       "    }\n",
       "    if (event.shiftKey) {\n",
       "        mods.push('shift');\n",
       "    }\n",
       "    if (event.metaKey) {\n",
       "        mods.push('meta');\n",
       "    }\n",
       "    return mods;\n",
       "}\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        modifiers: getModifiers(event),\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"639.8000000000001\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(pdiff, 'o-',color='k')\n",
    "for i in pdiff.index:\n",
    "    plt.text(i,pdiff[i]+.01, str(pdiff[i]),color='r',size=6)\n",
    "    plt.text(i-.2,pdiff[i]-.03, str(i),color='k',size=6)\n",
    "    \n",
    "plt.xlabel(\"Number More Homicides in July than in May\")\n",
    "plt.ylabel(\"Estimated Probability Based on 2015-2019 Data\")\n",
    "xl = np.arange(21)\n",
    "plt.xticks(xl, xl, alpha=0.7,size=5)\n",
    "plt.title(\"Probability that Chicago's July Homicide Count Exceeds May Homicide Count\",size=8)\n",
    "plt.savefig(\"MayvsJuly.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) Based on 2015-2019 data, there is a 10% chance of 20 more homicides in July than May."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit 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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}