FinalProject_group124.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project\n",
    "By: Nick Lin [XXXXXXXXX], Daniel Vasquez [XXXXXXXXX], Gabriela Cibrian [XXXXXXXXX], Richard Tang[XXXXXXXXX], Peter Pae [XXXXXXXXX], and Joshua Yuan[XXXXXXXXX]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Background\n",
    "### Research Question\n",
    "Do midterms and assignment deadlines in quarter system increase level of stress? Are suicide rates among students significantly higher during mid-quarter when students have to attend to midterms and homework?\n",
    "\n",
    "Being university students, we feel like the pressure of midterms can be overwhelming especially quarter system seem so fast-paced. So, we questioned if the stress from student obligations affects the suicide rates during \"midterm season\".\n",
    "\n",
    "Since all the UCSD police department incident reports are publicly accessable and stored as PDF files (www.police.ucsd.edu/docs/reports/CallsandArrests/Calls_and_Arrests.asp), we can clean and process the data to incidents we would like to analyze. To test our hypothesises' validity, we will use Chi-square test to test whether stress affects the suicide rates during mid-quater.\n",
    "\n",
    "### Background and Prior Work\n",
    "Nick obtained the dataset approximately a year ago, however, was unable to process the information due to the lack of knowledge required at the time. The 1000+ obtained files contained no clear tables, missing and inconsistent rows, spelling mistakes, and many variants of similar wordings. It wasn't until the Spring Break of 2019 that he obtained the knowledge required to clean and process all the data in the PDF files.\n",
    "Only a small portion of the dataset will be used, but it should be enough to build a time series regression model with an accuracy ideally above guessing.\n",
    "\n",
    "We will cross-compare our results with the information against the 2016-17 annual UCSD CAPS reports from the other UC's, in order to see if UCSD is lacking in mental health aid for students and faculty. In total, suicide total incidents include the following from the CAPS information: Suicide Completed, Suicide Attempts (serious, requiring emergency care) and suicide threats. CAPS also allows us to dive into general population that is actually receiving the most amount of clinical care, however, it stops at that not providing information on statistics for the UC general body suicide attempts, etc. If CAPS were to use the clinical stats to prevent potential suicide attempts they can possibly deter thoughts or actions on themselves through outreach programs.\n",
    "\n",
    "Prior studies based on suicide explore more into the victims mental health and pysche in an attempt to anticpate and hopefully prevent a suicide attempt. In \"Clues to Suicide\" by Schneidman and Farberow, raw data of victims' psychiatric case histories, psychological test results, and suicide notes were consolidated so that a statistical comparison might be made. The results of the comparison revealed 3 things:\n",
    "\n",
    "It is pratically impossible to distinguish a possible suicide attempter from details of his case study alone despite how traumatic. For example, many people in one group were children of broken homes, the study showed that the only way to distinguish the members of this group was by getting a record of mental history of the memebers.\n",
    "\n",
    "The second study revealed that nearly 75% of the victims who have commited suicide had a history of attempting or threathening to attempt suicide. Showing that such acts and threats should be taken seriously. (Although a suicide threat or gesture does not neccesarily indicate a possible attempter.)\n",
    "\n",
    "Almost half of the inidividiuals who commited suicide after leaving the hospital did so within 90 days of being discharged. The third indication is more of a time based observation and shows that suicide attempters that have been released from the hospital's care should still be carefully monitored\n",
    "\n",
    "## Hypothesis\n",
    "By utilizing police and Counseling and Psychological Services (CAPS) reports as foundational pieces in our data recording in predicting student suicide attempts. We believe that suicide attempts will trend with the rising levels of stress as students progress through the academic quarter. If such is true, we can then accurately predict the rate of suicide attempts through out the 11 week period of each quarter, with increasing amount of suicide attempts each week of the quarter. Taking the factors listed into consideration we make the assumption that the suicide attempts are correlated to stress from school, and we are mainly finding the rate of attempts throughput the quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import date, timedelta, datetime\n",
    "import re\n",
    "from fbprophet import Prophet\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset: Cleaning and Setup\n",
    "The crime logs are a set of unstructured data in a pdf file format. The following steps are required and the along with the corresponding ipynb file:  \n",
    "\n",
    "1. Manually deleted old versions and keep the updated as to cut down on extraneous crime logs.  \n",
    "2. Merge the PDF files into their corresponding years for easier handling.  \n",
    "* PDF_Merger.ipynb  \n",
    "3. Convert to the PDF's into CSV files using the Tabula application  \n",
    "* pdf_to_raw_CSV.sh  \n",
    "4. Convert the raw CSV files prone of syntatical errors into a cleaner CSV files with the correct table setup. Manually input the crime logs the Tabula application missed.  \n",
    "* CSV_Cleaner.ipynb  \n",
    "5. Combine the various correct table CSV files into one large CSV file.  \n",
    "* Clean_CSV_Merging.ipynb  \n",
    "6. Standardize text, correct errors and mispellings made by the original crime log makers.  \n",
    "* Clean_CSV_Manual_Proccessing.ipynb  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregate.csv\n",
    "- Dataset Name: aggregate.csv\n",
    "- Link to the partial dataset: http://www.police.ucsd.edu/docs/reports/CallsandArrests/Calls_and_Arrests.asp\n",
    "- Number of observations: 53236\n",
    "\n",
    "This dataset is all incidents being reported to UCSD police department from September 2011 to February of 2018. The data include brief summaries of incidents, incident number, date and time incidents occured, location and deposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"DataCleaningProcessing/aggregate.csv\")\n",
    "\n",
    "# Boolean Filters\n",
    "suicideSearcher = lambda x: bool(re.search(r\"suicid\", x)) if type(x) == str else False\n",
    "harmKillSelfSearcher = lambda x: bool(re.search(r\"(kill|harm|hurt).*self\", x)) if type(x) == str else False\n",
    "depressSearcher = lambda x: bool(re.search(r\"depress\", x)) if type(x) == str else False\n",
    "\n",
    "df = df[(df[\"Summary:\"].map(suicideSearcher)) | (df[\"Event\"].map(suicideSearcher)) | (df[\"Summary:\"].map(harmKillSelfSearcher)) | (df[\"Summary:\"].map(depressSearcher))]\n",
    "df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datetime conversion\n",
    "df[\"Date Occurred\"].replace({\"Unknown\": np.nan, \"10/19/20106\": \"10/19/2016\"}, inplace=True) # bad data\n",
    "\n",
    "# gets the last date if range given in date occured column\n",
    "latestDateF = lambda s: re.search(r\"\\d{0,2}/\\d{0,2}/\\d{4}$\", s).group(0) if pd.notnull(s) else s\n",
    "df[\"Date Occurred\"] = df[\"Date Occurred\"].apply(latestDateF)\n",
    "\n",
    "# conversion to datetime\n",
    "todatetime = lambda s: datetime.strptime(s,\"%m/%d/%Y\") if pd.notnull(s) else s\n",
    "df[\"Date Occurred\"] = df[\"Date Occurred\"].apply(todatetime)\n",
    "\n",
    "# get the number of attempts by day\n",
    "attempts = df[\"Date Occurred\"].value_counts()\n",
    "dates = attempts.sort_index().index\n",
    "date_set = set(dates[0] + timedelta(x) for x in range((dates[-1] - dates[0]).days))\n",
    "missing = sorted(date_set - set(dates))\n",
    "for day in missing:\n",
    "    attempts[day] = 0\n",
    "attempts = attempts.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want full years, since 2011 and 2018 are not full, we'll get rid of all values in that year.\n",
    "attempts = attempts[attempts.index.year != 2018]\n",
    "attempts = attempts[attempts.index.year != 2011]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uniform data for comparisons sake\n",
    "dates = attempts.index\n",
    "uniform_data = pd.Series([0 for _ in range(len(dates))], index = dates)\n",
    "for i in np.random.randint(len(dates), size=attempts.sum()):\n",
    "    uniform_data.iloc[i] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Graph to help visualize the number of attempts per day through a 5 year period\n",
    "plt.figure(figsize=(30,5))\n",
    "plt.bar(attempts.index, attempts)\n",
    "plt.title(\"Suicide Attempts on Campus by Day (Sample)\")\n",
    "plt.xlabel(\"Day\")\n",
    "plt.yticks(np.arange(0,5))\n",
    "plt.ylabel(\"Number of Recorded Attempts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see some clustering (the darker areas). However, it is still possible this is a result of chance by uniform distribution. We know that if the suicide attempts are in fact a uniform distribution, it indicates that we cannot predict the when attempts occur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,5))\n",
    "plt.bar(uniform_data.index, uniform_data)\n",
    "plt.title(\"Suicide Attempts on Campus by Day (Simulated-Uniform)\")\n",
    "plt.xlabel(\"Day\")\n",
    "plt.yticks(np.arange(0,5))\n",
    "plt.ylabel(\"Number of Recorded Attempts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both graphs look visually similar, this means here is a high likelyhood that the suicide attempts are in fact uniformly randomly distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to look at the data with it \"more clumped up.\" We will see if a pattern/non-uniform distribution exists by:\n",
    "1. Year\n",
    "2. Month\n",
    "3. Quarter (11 week period per quarter including finals)  \n",
    "However, because of the increasing population of UCSD throughout the 5 year period, we'll need to be using the rate of attempts by dividing the attempts by the number of total students attending."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to percentage of student population\n",
    "# Source:\n",
    "# https://ir.ucsd.edu/_files/stats-data/profile/profile-2012.pdf\n",
    "# https://ir.ucsd.edu/_files/stats-data/profile/profile-2013.pdf\n",
    "# https://ir.ucsd.edu/_files/stats-data/profile/profile-2014.pdf\n",
    "# https://ir.ucsd.edu/_files/stats-data/profile/profile-2015.pdf\n",
    "# https://ir.ucsd.edu/_files/stats-data/profile/profile-2016-2017.pdf\n",
    "# https://ir.ucsd.edu/_files/stats-data/profile/profile-2017-2018.pdf\n",
    "totCampusPop = {2012: 29052, 2013: 30310, 2014: 31502, 2015: 33735, 2016: 35816, 2017: 36624}\n",
    "totCampusPopL = [29052, 30310, 31502, 33735, 35816, 36624]\n",
    "attemptRates = attempts.astype(np.float128)\n",
    "for i in attemptRates.index:\n",
    "    if i.year == 2012:\n",
    "        attemptRates[i] = attemptRates[i] / totCampusPop[2012]\n",
    "    if i.year == 2013:\n",
    "        attemptRates[i] = attemptRates[i] / totCampusPop[2013]\n",
    "    if i.year == 2014:\n",
    "        attemptRates[i] = attemptRates[i] / totCampusPop[2014]\n",
    "    if i.year == 2015:\n",
    "        attemptRates[i] = attemptRates[i] / totCampusPop[2015]\n",
    "    if i.year == 2016:\n",
    "        attemptRates[i] = attemptRates[i] / totCampusPop[2016]\n",
    "    if i.year == 2017:\n",
    "        attemptRates[i] = attemptRates[i] / totCampusPop[2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xl8FPX9+PHXOwkkHOEKNwHCfSNiAEFRFA/wQNvaCli1amutorb99ZC2KqW12tavrUetUm9rxVup2loVERUVwn1DuAOEIxAIV8jx/v3xmU02S45Ndje7Sd7Px2Mf2Z35zMx7drPz3s8xM6KqGGOMMTUVF+0AjDHG1G2WSIwxxoTEEokxxpiQWCIxxhgTEkskxhhjQmKJxBhjTEgskZgyxHlWRA6KyMIgl3lORH4f6diiSUTSRERFJCHasVSXiPQTkaUikicid0Q7nmgTkUQRWSMiHaMdiz8RGSciWUGWvUNEHoh0TMGyRBICERkgInNF5JCIZIrIN/zm+Q48R/wed/vN/7mI7BeRVSIy2G/6WSLydhDb7isir3nrOCQiK0TkpyISH+JB72zgQiBVVUeWs93vicjnNVhvtXjJqVBEOkd6W+EgIltF5Lj3OWd78TcPctlIJ6lfAPNUNVlVH6kkjnLfcxGZISL/DJg2T0S+H6F4K1WdA24Fbgbmq2q2t75UEXnD77u0UkS+F5ZgI2cW8F0RaR/tQMASSY15X/p3gHeBNrh/zn+KSN+Aoq1Utbn3+J23bCfgJqAn8ATwgN86/w/4cRXb7gV8DewAhqhqS+DbQDqQHOKudQe2qurRENdTYyLSDPgWcAi4Jlpx1MDlqtocGAacDkyPcjw+3YHVlRWow+95TfwQeNHv9Yu471J3IAW4DtgThbiCpqongP/gYo0+VbVHDR7AYOAIIH7T/gf8znueBiiQUM6yo4CXvef9gTXe858Bvwpi2/8E3qtkfoXb9uZ3BuYAB4BM4Afe9JuAE0CRt2+/DVhuQMD8XG/6c8DfgPeAPFyS6+W3XH/gQ29764HvVLF/1+G+2HcCqwLmzQBeBV7wtrUaSPebv9V7H1fgDoqvAEnevO8BnwesT4He3vNLgaXAYW/7M6rxnm4FLvB7/Sf/z6iKdW/31n3Ee4z2pt8IrAUOAh8A3St5zyZ570UuMA8Y4E2f631eJ7x1963Oew5MAE4CBd7yy4H7Atb5WFWfs/c/8jju4HcE+ALoCPzV2791wOkB7+d0YI03/1kgCWgGHAeK/d6vzsBIIMN7f/cAD1Wwn9285RP8ph0BhlXy3r4GZHv/T/OBQeHeL2/eOCAr4Hv6BrAP2ALcERDXNcAn4TqmhfKIegB19QEM4dRE8iHwlvc8DXdw2Alkef8wbb15KcAqoBUwzftH7ep9ERoHse1s4IZK5vu2XdFB71Pvnz8J9+t5HzDem/c9Ag62AcueMt/7Mh3wvswJwEvAbG9eM9wB6gZv3nBgv/+XsZxtfIw7EHcACoHhfvNm4A5glwDxwP3AV37ztwILvS9hG9yB+JZKYvdPJOO8zzUOGIo7IF0Z5Hu6FS+RAKnASuBhv/nVWjdwJS7JD/Det98ACyrYdl/gKK5JshGuKSvT97+ESyzfr+J/qqr3/J8B5cuss6rP2fsf2Q+cgfu/m4s7OF7nfY6/x++g6L2fq3Dfiza4A/Tv/d7LrIB4vgSu9Z43B86sYD8vBVYHTPvIW/9koFs5y9yIq+kn4hLEsoD//bDvl/d/shi4B2iMa73YDFzst67hwIFwHM9CfUQ9gLr68L6wm70vbSPgItwvtw+8+c1xTU0J3pfzdd88b/4UYAnul0x34E1gPHA17kD/Dq6forxtFwATKoktjYprQ11xvyaT/abdDzznPf8eNUskT/m9vgRY5z2/GvgsoPyTwL0VrL8b7tfmMO/1B5Q9IM8APvJ7PRA47vd6K/Bdv9d/Ap6oJPaSRFJOLH8F/lLVe+q33SO4WpLiDsytKnkfK123939xk9/rOOAY5dRKgLuBVwPK7gTGea/nUUkiCfI9ryqRVPo5e/8j//Cbdzuw1u/1ELwart/7eUvA/9Qm7/k4Tk0k84Hf4v1Yq2Rfr8Hvh4c3rTWueXk17ruxDBhRwfKtvM+qZST3C9dqsT1g29OBZ/1e9wGKKtvf2npYH0kNqWoB7lfjpbgawv/DNblkefOPqGqGqhaq6h5czeMiEWnhzX9ZVYer6kRcM1k+runjQeByXC3lwQo2nwN0qmHonXG/YvL8pm0DutRwfT7Zfs+P4RIpuCQ5SkRyfQ/cl7miETPX4r6Iy7zXLwFTRaRRJdtKCuioriiWSonIKBH5RET2icgh4BagbTDLeq5U1WTcAaG//7I1WHd34GG/9+wAIJT/OXXGfYYAqGoxrnYQ7GcazHtelWA+Z/9+h+PlvA78nHb4Pd+G28+K3ISrma0TkUUiclkF5Q4S0I+oqgdV9S5VHYT70bcMeNsbwRgvIg+IyCYROYxLBFD2s4vEfnUHOge8n7/y4vNJxjW3RV2dG8oYS1R1BXCu77WILACer6i4r5j/RBFpAvwBmIj7hbFDVQ+LyCLcP055PsJ1jD5bg7B3AW1EJNkvmXTD/YINhlZdpIwdwKeqemGQ5a8DuomILxkk4JoCJ+L6dUJxFGjqe1HO8M9/AY8BE1X1hIj8leolEgBU9VMReQ73Q+DKINZd3nu6A7hPVV8KYpO7cL98ATeEG1fzDPYzreo9Ly++wGnV/ZyD0dXveTfcfpa3bVR1IzBFROKAbwKvi0iKnjpoZAXQU0QSVLWwnPXsF5EHgetxTU+XAFcAF+CSSEtcMpLAZcOwX/52AFtUtU8l6xmA67OKOquRhEBEhopIkog0FZGf4WoJz3nzRokbvx8nIinAI7ghmIG/IH6Da1bahet07SciHYDzcE1n5bkXGCMif/YdDEWkt4j8U0RaVRazqu4AFgD3e7EPxf2aC+aABe7XVqqINA6y/LtAXxG5VkQaeY8RIjIgsKCIjAZ64fpahnmPwbiD8PVBbq8yy4FBIjJMRJJwTTb+knG1tRMiMhKYGsK2/gpcKCLDglj3PlzTUk+/aU8A00VkEICItBSRb1ewrVeBS0VkvFeL+H+4Gu6CqoIM8j3fA6R5B2n8pvnHG/TnXA23eUNz2+B+VL3it+0UEWnptx/fFZF2Xm0s15tcFLhCVc0CNnr761v2jyIyWEQSRCQZ+BGQqao5uM8tH9cK0BT3oy9UFe2Xv4XAYRH5pYg08WpGg0VkhF+Zc3FNoFFniSQ01wK7gb24/o0LVTXfm9cT+C+uzXwV7p9xiv/CItIP17fyKICq7qa0rfYOKhg+qqqbgNG4tvXVXlPJG7jO+rzylgkwxVt2F/AWrh37wyCWA9eZuBrIFpH9VRX2aj0X4Toyd+Ganf6I67gMdD3wjqquVNVs3wN4GLjM++LVmKpuAGbianQbgcDzYW4FZopIHq6T89UQtrUPN7LMd+5QhetW1WO4kVBfeM0YZ6rqW7j3abbXpLIKV0Mob1vrge/i/o/245pGL1fVk0GEGsx7/ppXNkdElnjPHwauEnfi6iPV/JyD9S/cSMjN3uP33v6uA14GNnvvV2fc6LLVInLEi22yuiGy5XkS9931aYr7HuR62+mOGwUH7jPchqvdrQG+CmF/Kt0vf6pahPsch+E67/cDT+FqRHg/hC6h4haQWiVep40xxsQMEdmK68z/KALrTsT1R473frzVmnDtl4jcDnRV1V+EJbAQWR+JMaZB8VoNBkY7jlCo6qPRjsGfNW0ZY4wJiTVtGWOMCYnVSIwxxoSkQfSRtG3bVtPS0qIdhjHG1BmLFy/er6rtginbIBJJWloaGRkZ0Q7DGGPqDBHZVnUpx5q2jDHGhMQSiTHGmJBYIjHGGBMSSyTGGGNCYonEGGNMSCyRGGOMCYklEmOMMSGxRGKMqffWZ+fx4lfbOFFwyi1KTBg0iBMSjTEN04mCIh6bm8kTn26isFh58tNNzLh8EBcM7FD1wiZoViMxxtRLCzbtZ+LDn/HYJ5lMGtaZf1yXTpNG8Xz/hQxufG4R23IC78JraiqiiUREJojIehHJFJG7ypmfKCKvePO/FpE0v3nTvenrReRiv+k/EZHVIrJKRF727hRmjDEAHDx6kp+/tpyp//iaomLlnzeN4qHvDOPCgR14/86x/ObSAXy9OYcL/zKfhz7cYM1dYRCxy8iLSDywAbgQyAIWAVNUdY1fmVuBoap6i4hMBr6hqleLyEDcrTRHAp1xt0btC3TE3R51oKoeF5FXgfdV9bnKYklPT1e71pYx9ZuqMmf5Lmb+ew25xwv4wdie3Dm+D00ax59Sds/hE/zh/bW8s2wXqa2bcO/lg7hgQHtEJAqRxyYRWayq6cGUjWSNZCSQqaqbvXtHzwauCChzBaX3HH4dGC/uk7wCmK2q+aq6Bcj01geuX6eJiCTg7rW8K4L7YIypA3YcOMb3nl3EnbOXkdq6Cf+edjZ3TexfbhIB6NAiiYcnn87LPziTJo3i+cELGdz0fIY1d9VQJBNJF2CH3+ssb1q5ZVS1EDgEpFS0rKruBB4EtgO7gUOq+r/yNi4iN4tIhohk7Nu3Lwy7Y4yJNYVFxTz12WYu+st8Fm09wD2XDeTNW89iYOcWQS0/uleKNXeFQSQTSXl1xMB2tIrKlDtdRFrjais9cE1ezUTku+VtXFVnqWq6qqa3axfUJfWNMXXIqp2HuPLxL/j9e2sZ3SuFD396Ljee3YP4uOo1TzWKj+P7Y3sy92fjmDi4I498vJELHvqUD9fswe4gG5xIJpIsoKvf61RObYYqKeM1VbUEDlSy7AXAFlXdp6oFwJvAmIhEb4yJScdOFvKH99dyxd++IPtQPn+bOpynr0+nS6smIa23vOYuG90VnEgmkkVAHxHpISKNgcnAnIAyc4DrvedXAXPV/QSYA0z2RnX1APoAC3FNWmeKSFOvL2U8sDaC+2CMiSGfbtjHRX+Zz6z5m/lOeiof//RcLh3aKayd5P7NXQu3HLDmriBE7IREVS0UkWnAB0A88IyqrhaRmUCGqs4BngZeFJFMXE1ksrfsam9E1hqgELhNVYuAr0XkdWCJN30pMCtS+2CMiQ37j+Tzu3fX8M6yXfRs14xXbj6TUT1TIrY9X3PX5ad15g/vr+WRjzfy5pIsG91VgYgN/40lNvzXmLpJVXl9cRb3vb+Wo/mF/Ghcb24d14ukRuWPxoqULzflcM87q9i49wjn9WvHjEmD6J7SrFZjqG3VGf5ricQYE5O27j/Kr95ayYJNOaR3b8393xxCnw7JUYunoKiY5xds5S8fbqCgWLnlnJ78aFzvCocY13WWSAJYIjGm7igoKmbW/M088vFGGsfH8cuJ/Zk6shtx1RyNFSkN5WRGSyQBLJEYUzcs3X6Q6W+uZF12HhMHd2TGpEF0aBGbV0Gq781dlkgCWCIxJrYdyS/kwQ/W8/yXW+mQnMTMKwZx0aCO0Q6rSvW5ucsSSQBLJMbErg/X7OGed1aRffgE153ZnZ9d3I/kpEbRDqta6mNzlyWSAJZIjIk9ew+f4N45q/nPqmz6dUjm/m8NYXi31tEOKyT1qbnLEkmAmiYSVa3TvyiMiUXFxcrLi7bzwH/WkV9YzJ3j+/CDsT1pnFA/bo9UX5q7LJEEqEkiUVW+8+SXnNG9Dd8f24O2zRMjFJ0xDUfm3jymv7mSRVsPMrpnCn/45hB6tK2bv9irEtjcdc9lA7lwYIc68+PUEkmAmiSSvBMF/OqtVby7YheJCXFMHtGNH57bk04tQ7uejzENUX5hEY9/sonH52XStHECv750AN8+I7XOHFRDUVebuyyRBAilj2TTviP8fd4m3lq6kziBq87oyo/O7UW3lKZhjtKY+mnhlgNMf3MFm/YdZdJpnbnn8oENroZfF5u7LJEECEdn+44Dx3hy/iZeXZRFkSpXnNaZW8/rRe/20TvT1phYduh4AQ/8Zx0vL9xOl1ZN+P03BnNev/bRDiuq6lJzlyWSAOEctbXn8Almzd/Mv77ezonCIi4Z3Ilbz+vFoM4tw7J+Y+o6VeX9ldnM+Pdqco7kc9PZPfjJhX1p2jhi14itcwKbu+69fBBpMdZXZIkkQCSG/+YcyeeZL7bwwoJt5OUXMr5/e247v3edH75oTCh25R7nnndW8dHavQzq3IIHvjmUIan2I6s8ZZq7ipRbzo2t5i5LJAEieR7JoeMFvLBgK09/sYXcYwWc3bst087vzagebWKyumpMJBQVKy98uZUHP1hPscJPL+zLDWelkRBfP4b0RlKsNndZIglQGyckHs0v5KWvtzFr/hb2H8knvXtrpp3fm3P7tov6P4QxkbR292HuenMly3fkck7fdtx35WC6trHBKNX15aYc7p2zig17YqO5yxJJgNo8s/1EQRGvZuzgiXmb2HXoBEO6tOS283pz0cAOMXP1UmPC4URBEQ9/vJF/zN9MyyaNuOfygUw6rbP9cApBLDV3WSIJEI1LpJwsLOatpVk8Pm8T23KO0bdDc247rzeXDulk1X1T532+cT+/fnsl23KOcdUZqfz6kgG0btY42mHVG/7NXV1aNeHey2u/ucsSSYBoXmursKiY91bu5rG5mWzce4S0lKbcOq43V57epd5cEsI0HAePnuT3763ljSVZpKU05Q/fGMKY3m2jHVa9Fc3mLkskAWLhoo3Fxcr/1mTz2CeZrNp5mC6tmnDLuT35dnrXWr9tqDHVpaq8s2wXM99dw+HjBfzw3J7cfn4f+9+tBdFq7rJEEiAWEomPqjJvwz4em5vJ4m0HaZecyM1jezJ1VDeaJdo4exN7tucc49dvr+SzjfsZ1rUV939zCAM6tYh2WA1ObTd3WSIJEEuJxEdV+WrzAR77ZCNfZObQumkjbjyrB9eNSaNlk7p1LwZTPxUWFfP051v4y0cbiBfhFxP6890zuxNvg0aiqraauyyRBIjFROJv8baD/O2TTOau20tyYgLXj0njxrN70MY6L02UrMjK5a43VrJm92EuGNCemVcMpnMru2BprKiN5i5LJAFiPZH4rNp5iMfnZfKfVdkkJcRzzahu3HxOT9rH6D2rTf1zNL+Qhz7cwLNfbCGleSIzJw1iwuCONqQ3RkWyucsSSYC6kkh8Mvfm8fgnm3hn+S7i44Sr07vyw3N7ktraTvIykfPJ+r385q1V7Mw9ztRR3fjlhP7WzFpH+Dd3jevXjhlhaO6yRBKgriUSn205R3ni0028vjgLVfjG6V340bhe9GzXPNqhmXpkX14+M99dw7+X76J3++bc/80hjEhrE+2wTDWFu7nLEkmAuppIfHblHmfW/M28vHA7BUXFXDq0M7ed14v+HW3kTKgKi4rZmXscVUiIFxrFx9EoPs49j3N/E+KkXjbtqCqvZWRx3/trOX6yiNvO680t43qSmGBDeusy/+aubm2a8t8fj63RlZctkQSo64nEZ19ePk99vpl/frmNoyeLuGhgB6ad35uhqa2iHVrMU1X25uWzLjuPdbsPsz47j3XZeWTuPcLJouIql28ULyR4icUlG/e6UbyQEB9HQpzQOMH9TSgzv7RMozgpTVLxpWUb+9bhl7z8t1FRgisvjkYB6/Z/7Z8MN+87wq/eWslXmw8wIq01939ziN1bp575clMOy3bk8qNxvWq0vCWSAPUlkfjkHjvJs19s5dkvtnD4RCHn9G3H7ef3tuYIz9H8QjbscYnCJYzDrMvOI/dYQUmZDi0S6dexBf07JtO7XXPi44TC4mIKipTComIKi5WTRcUUeq8Lir2/RUqBN72g2PvrTS8s89qto8z6CovLlHHz3bK1wSUXl4iOFRTRtHE80ycOYPKIrnYdOHOKiCUSEWkNdFXVFTUNLhrqWyLxyTtRwD+/2s5Tn20m5+hJRvVow7Tze3N277b1sikmUFGxsjXnqEsWu12yWJedx/YDx0rKNG0cT98OyfTv6B6+5BFL14VSVYqKtUxiqSp5+eYXeMnJl8jKlPWSVWFRMSf9ElpBUTFJjeK5YUyajQg0FQprIhGRecAkIAFYBuwDPlXVn4YYZ62pr4nE5/jJIl5euJ1Z8zeTffgEp3Vtxe3n9Wb8gPb1JqHsy8svU7tYn53Hhj155Be6Zqk4gbS2zbyE0YJ+XuLo2rqp/do2pgbCnUiWqurpIvJ9XG3kXhFZoapDwxFsbajvicQnv7CINxbv5PF5mWQdPM6ATi247bxeTBzcqc6cjXz8ZBEb9+axbrfXNLXnMOt255Fz9GRJmbbNE/1qGC5x9OnQ3K77ZEwYVSeRBNOVnyAinYDvAL8OKTITUYkJ8Uwd1Y1vp6cyZ9ku/jYvk2n/WkrPdhu4bVxvJg3rTKMYuYR9cbGy/cAxrznKdX6vz85jS85RfL9tkhrF0bdDMuMHtC9pkurXMZm2zROjG7wxpoxgaiRXAfcAn6vqrSLSE/izqn6rNgIMh4ZSIwlUVKz8d1U2j87dyLrsPFJbN+FH43px1RmptTrE88DRk65JarfX+b0njw3ZeRwvKAJABLq3aVpSu/AljO4pzepMTcqY+ibcTVtnqeoXVU2LZQ01kfioKnPX7eXRuZks25FLhxaJ/PCcXkwZ2S2s1+Y5UVBE5t4jXh9Gaef3vrz8kjJtmjWmX4dk+ncq7fzu26F5jca5G2MiJ9yJZImqDq9qWixr6InER1X5IjOHR+du5OstB0hp1pibxvbg2jO7k5wU/KUwiouVnbnHS87JWLfHa5baf5SiYvf/1Dghjj7tm5epYfTvlEy75on1ZgCAMfVZWPpIRGQ0MAZoJyL+I7RaANarWQeJCGf3acvZfdqyaOsBHpubyZ/+u54n5m3ie2f14Maz0mjVtOyw2EPHCkpGSvlqGuuz8zh6sqikTNc2TejfsQUTB3csaZ5KS2lqtxQ2poGorD2hMdDcK+N/yuth4KpIBmUib0RaG56/cSQrsnJ5bG4mj3y8kac/28zkkd1IiBfv3Iw8sg+fKFmmZZNG9O+YzFVnpLrO707J9O2QTHO7IZcxDVowTVvdVXWbiLQAVFXzaie08LGmraqtz87jb59k8u4Kd8XhXu2aM6BT6fkY/Tu2oEMLa5YypqEIdx9JOvAspbWSQ8CNqro4pChrkSWS4B06XkDTxvExM0zYGBMd1UkkwRwtngFuVdU0VU0DbsMllmACmSAi60UkU0TuKmd+ooi84s3/WkTS/OZN96avF5GL/aa3EpHXRWSdiKz1+nJMmLRs0siSiDGmWoI5YuSp6me+F6r6OVBl85aIxAN/AyYCA4EpIjIwoNhNwEFV7Q38Bfijt+xAYDIwCJgAPO6tD+Bh4L+q2h84DVgbxD4YY4yJkGASyUIReVJExonIuSLyODBPRIaLSGVDgEcCmaq6WVVPArOBKwLKXAE87z1/HRgvrhH+CmC2quar6hYgExjp9dOcAzwNoKonVTU32J01xhgTfsEMtxnm/b03YPoYQIHzK1iuC7DD73UWMKqiMqpaKCKHgBRv+lcBy3YBjuMuGvmsiJwGLAbuVNWjgRsXkZuBmwG6detWye4ZY4wJRZWJRFXPq+G6yxveE9izX1GZiqYnAMOB21X1axF5GLgLuPuUwqqzgFngOturEbcxxphqqDKRiEgr4Dogzb+8qt5RxaJZQFe/16nArgrKZIlIAtASOFDJsllAlqp+7U1/HZdIjDHGREkwfSTv45LISlxTku9RlUVAHxHpISKNcZ3ncwLKzAGu955fBcxVNx55DjDZG9XVA+gDLFTVbGCHiPTzlhkPrAkiFmOMMRESTB9JUk1uYuX1eUwDPsBdUuUZVV0tIjOBDFWdg+s0f1FEMnE1kcnesqtF5FVckigEblNV3zU5bgde8pLTZuCG6sZmjDEmfII5IfEnwBHgXaDkMq6qeiCyoYWPnZBojDHVE+4bW50E/oy7qZUv6yjQs2bhGWOMqU+CSSQ/BXqr6v5IB2OMMabuCaazfTVwLNKBGGOMqZuCqZEUActE5BPK9pFUNfzXGGNMAxBMInnbexhjjDGnCObM9udFpAnQTVXX10JMxhhj6pAq+0hE5HJgGfBf7/UwEQk8sdAYY0wDFUxn+wzclXxzAVR1GdAjgjEZY4ypQ4JJJIWqeihgml0E0RhjDBBcZ/sqEZkKxItIH+AOYEFkwzLGGFNXBFMjuR13p8J84F+4e7bfGcmgjDHG1B3B1EguVdVf4y6RAoCIfBt4LWJRGWOMqTOCqZFMD3KaMcaYBqjCGomITAQuAbqIyCN+s1rgLu1ujDHGVNq0tQvIACZR9kZWecBPIhmUMcaYuqPCRKKqy4HlItJBVZ/3nycidwIPRzo4Y4wxsS+YzvbJwJ8Cpn0PSyTGmHqkoKCArKwsTpw4Ee1QalVSUhKpqak0atSoxuuorI9kCjAV6BFwSZQWQE6Nt2iMMTEoKyuL5ORk0tLSEJFoh1MrVJWcnByysrLo0aPmFyyprEayANgNtAX+z296HrC8xls0xpgYdOLEiQaVRABEhJSUFPbt2xfSeirrI9kGbANGB2z4LOAR4LaQtmyMMTGmISURn3DsczB9JIjIMFwz13eALcCbIW/ZGGNMvVDhCYki0ldE7hGRtcBjwA5AVPU8VX201iI0xpgGonnz5mVeP/fcc0ybNg2AJ554ghdeeKHS5f3L16bKaiTrgM+Ay1U1E0BE7PwRY4yJgltuuSXaIVSoskukfAvIBj4RkX+IyHig4TUgGmNMDJgxYwYPPvggAIsWLWLo0KGMHj2an//85wwePLik3K5du5gwYQJ9+vThF7/4Ra3EVlln+1vAWyLSDLgSdzZ7BxH5O/CWqv6vViI0xpha9tt/r2bNrsNhXefAzi249/JBlZY5fvw4w4YNK3l94MABJk2adEq5G264gVmzZjFmzBjuuuuuMvOWLVvG0qVLSUxMpF+/ftx+++107do1PDtRgSov2qiqR1X1JVW9DEjF3Xb3rioWM8YYU01NmjRh2bJlJY+ZM2eeUiY3N5e8vDzGjBkDwNSpU8vMHz9+PC1btiQpKYmBAweybdu2iMcd1KgtH1U9ADzpPYwxpl6qquYQTaqV36A2MTGx5Hl8fDyFhZG/xm4wl5E3xhgTI1oHR1fVAAAbLklEQVS3bk1ycjJfffUVALNnz45yRJUP/02saJ4xxpjoefrpp7n55psZPXo0qkrLli2jGo9UVE0SkSWqOlxEXlTVa2s5rrBKT0/XjIyMaIdhjIlha9euZcCAAdEOIyhHjhwpOefkgQceYPfu3Tz8cM2vo1vevovIYlVND2b5yvpIGovI9cAYEflm4ExVtbPbjTEmCt577z3uv/9+CgsL6d69O88991xU46kskdwCXAO0Ai4PmKfYZVKMMSYqrr76aq6++upoh1GisvNIPgc+F5EMVX26FmMyxhhThwQz/PdFEbkDOMd7/SnwhKoWRC4sY4wxdUUwieRxoJH3F+Ba4O/A9yMVlDHGmLojmEQyQlVP83s9V0TsxlbGGGOA4E5ILBKRXr4XItITKIpcSMYY0zCJCNdeW3q2RWFhIe3ateOyyy6LYlRVC6ZG8nPcFYA3467+2x24IaJRGWNMA9SsWTNWrVrF8ePHadKkCR9++CFdunSJdlhVCuaijR8DfYA7vEc/Vf0k0oEZY0xDNHHiRN577z0AXn75ZaZMmVIy7+jRo9x4442MGDGC008/nXfeeQeArVu3MnbsWIYPH87w4cNZsGABAPPmzWPcuHFcddVV9O/fn2uuuabKa3XVRFAXbVTVfGBFdVcuIhOAh4F44ClVfSBgfiLwAnAGkANcrapbvXnTgZtwzWh3qOoHfsvFAxnATu+qxMYYEz7/uQuyV4Z3nR2HwMQHqiw2efJkZs6cyWWXXcaKFSu48cYb+eyzzwC47777OP/883nmmWfIzc1l5MiRXHDBBbRv354PP/yQpKQkNm7cyJQpU/BdzWPp0qWsXr2azp07c9ZZZ/HFF19w9tlnh3XXqnX13+rwDvZ/Ay4EsoBFIjJHVdf4FbsJOKiqvUVkMvBH4GoRGQhMBgYBnYGPRKSvqvr6Zu4E1gItIhW/McZEw9ChQ9m6dSsvv/wyl1xySZl5//vf/5gzZ07JDa5OnDjB9u3b6dy5M9OmTWPZsmXEx8ezYcOGkmVGjhxJamoqAMOGDWPr1q11J5EAI4FMVd0MICKzgSsA/0RyBTDDe/468JiIiDd9tlcT2iIimd76vhSRVOBS4D7gpxGM3xjTUAVRc4ikSZMm8bOf/Yx58+aRk5NTMl1VeeONN+jXr1+Z8jNmzKBDhw4sX76c4uJikpKSSubVxmXlq+wjEee7InKP97qbiIwMYt1dgB1+r7O8aeWWUdVC4BCQUsWyfwV+ARRXEffNIpIhIhn79u0LIlxjjIkNN954I/fccw9DhgwpM/3iiy/m0UcfLennWLp0KQCHDh2iU6dOxMXF8eKLL1JUVLsDa4MZ/vs4MBrw9fjk4ZqsqlLe/d0De3kqKlPudBG5DNirqour2riqzlLVdFVNb9euXdXRGmNMjEhNTeXOO+88Zfrdd99NQUEBQ4cOZfDgwdx9990A3HrrrTz//POceeaZbNiwgWbNmtVqvBVeRr6kQOnl5Jeq6unetOUBJymWt9xoYIaqXuy9ng6gqvf7lfnAK/OliCQA2UA7vFv5+sr6ygGTcGfWFwJJuD6SN1X1u5XFYpeRN8ZUpS5dRj7cQr2MfDA1kgKv41y9lbejimYlzyKgj4j0EJHGuM7zOQFl5gDXe8+vAuaqy2xzgMkikigiPXDDjxeq6nRVTVXVNG99c6tKIsYYYyIrmM72R4C3gPYich/ugH93VQupaqGITAM+wA3/fUZVV4vITCBDVecAT+MuCpkJHMAlB7xyr+I65guB2/xGbBljjIkhVSYSVX1JRBYD43F9F1eq6tpgVq6q7wPvB0y7x+/5CeDbFSx7H25kVkXrngfMCyYOY4wxkVNlIvG71e66cqYZY4xp4ILpIxnk/8LrLzkjMuEYY4ypaypMJCIyXUTygKEiclhE8rzXe4F3ai1CY4wxMa3CRKKq96tqMvBnVW2hqsneI0VVp9dijMYY0yA0b9482iHUSDCd7dNFpDVuCG6S3/T5kQzMGGNM3RDMJVK+D8zHDeP9rfd3RmTDMsYYA7Bt2zbGjx/P0KFDGT9+PNu3b6eoqIiePXuiquTm5hIXF8f8+e63/dixY8nMzKzVGIM5j+ROYATwlaqeJyL9cQnFGGPqpT8u/CPrDqyrumA19G/Tn1+O/GW1l5s2bRrXXXcd119/Pc888wx33HEHb7/9Nn379mXNmjVs2bKFM844g88++4xRo0aRlZVF7969wxp7VYIZtXXCO98DEUlU1XVAvyqWMcYYEwZffvklU6dOBeDaa6/l888/B1zNY/78+cyfP5/p06fz+eefs2jRIkaMGFHrMQZTI8kSkVbA28CHInIQ2BXZsIwxJnpqUnOoLe5OGy6RPPHEE+zatYuZM2fy5z//mXnz5nHOOefUekzB3Gr3G6qaq6ozcJdGeRp3vxBjjDERNmbMGGbPng3ASy+9VHJTqlGjRrFgwQLi4uJISkpi2LBhPPnkk4wdO7bWYwymaauEqn6K63j/RWTCMcaYhuvYsWOkpqaWPB566CEeeeQRnn32WYYOHcqLL77Iww8/DLgbVnXt2pUzzzwTcDWUvLy8U+5hUhsqbNoSka64GkhnXLPWv4Df4S7j/nKtRGeMMQ1IcXH5F1afO3duudN993IHmDp1aklfSm2rrI/kBeBT4A1gAvAVsBoYqqrZtRCbMcaYOqCyRNLG6xcB+EBE9gAjvPuoG2OMMUAVo7a8M9p9t73NBpqKSDMAVT0Q4diMMaZWqWrJqKiGoqq75AajskTSElhM2funL/FtG+gZ8taNMSZGJCUlkZOTQ0pKSoNJJqpKTk4OSUlJVReuRIWJxLudrTHGNAipqalkZWWxb9++aIdSq5KSkkhNTQ1pHcGckGiMMfVeo0aN6NGjR7TDqJOqdR6JMcYYE8hqJMaY+q2oEHYvg4NbodNp0KYXxNlv6HCq7ITENpUtaKO2jDExSRVyMmHzPPfY8hnkHyqdn9QKupwBqSPco8twaFrp4c5UobIayWLc6Kzyhi/YqC1jTOzI2wNbPi1NHod3uumtusGgK6HnOEjpBbtXQNYiyMqA+X8C9c4kT+ntJRUvwXQYBPGNorMvdZCEYwxxrEtPT9eMjIxoh2GMCZf8I7Dti9LEsXeNm96kNfQ41yWOnuOgTSWd5/l5sGupl1gWu79H97p5CU2g8zBITfcSTDq07BLJPYo5IrJYVdODKhtMIqnrt9q1RGJMHVdUADuXlCaOrIVQXAjxidB9dGni6DgU4uJrtg1VyN0OOzNcjSVrEexeDkUn3fzkzpDq1yTWaRg0bhqOvYtJ1UkkVXa2e7favRNIBZYBZwJfAueHEqQxxlRIFfatL00cWz+Hk3mAuJrCmNtd4ug6Cho1Cc82RaB1d/cY/C03rTAfsld5yWWRe6z9t1c+3jWBpY4orbk00I78KmskIrKS0lvtDvPdaldVr66NAMPBaiTG1AGHd7t+jk2fuORxxLs2bOseLmn0Og/Sxka/Y/zo/tIay84M1yx2Ms/Nq0cd+WGtkeDdaldESm61KyJ2q11jTGhOHC7bz7HPu0d60xS/fo5zoXVa1EIsV7O20G+CewAUF8H+DaXJpQF25Nutdo0xtaPwpPsFX9LPkQFa5Dq2u4+BYde45NFhcN1qHoqLh/YD3GP4tW5aYEd+5sew3LuNk39HfhevSayOd+RXa9SWiJyLu5jjf1X1ZMSiCjNr2jImClRh79qy/RwFR0HioPPw0g7yriMhITGakUaeKhzaUVpjycpwJ0mW15HfJd0lmsbNohpyuDvbu/m93OL97Qhsr0Fsxpj67NDO0sSxeV7pcNqU3jBsikscaWe7YboNiYg7p6VVt0o68jPK6chPL+1vieGO/GA7230nJiYBPYD1qjoo8uGFh9VIjImQ47mupuFLHDkb3fRm7UprHD3OhVZdoxVh3eLryPcll51LIP+wm5fUsrQpLDXd9blEsCM/rDUSVS1zJ3kRGQ78sIaxGWPqssJ8d4DbPM+Nrtq1xHUqN2oK3c+C9Btc8mg/0P0KN9VzSkd+sdeR7w093rn41I78LumlNZcodeTX6Mx2EVmiqsMjEE9EWI3EmBoqLoa9q0trHNsWQMEx1/TS5YzSWkfqCEhoHM1IG478I34d+RkBZ+QnQefTyw5BrmFHfrj7SH7q9zIOGA40rDu/GNOQ5G736+f4FI7td9Pb9oPTr/X6Oc5yTS2m9iU2hx5j3QMCOvK9S70s/Ad8+RgktoRfbo1430oww3+T/Z4XAu8Bb0QmnBiz6g1o2dVVF6M8gsKYiDl+0F0hd/M82PwJHNjspjfvAL3Hl9Y6WnSOWoimEuV25J+EPSvh8K5a6aAPpo/ktxGPIhYVFcBbt7jheRIHKX2g01B3LZ9Op0HHIXX2jFXTABQXuf6MopPuf7nI73lhvqtl+JLHrqWAQuPmbkTVyJtd4mjX3/o56qqExq55q8sZtbO5qgqISF/gZ0Caf3lVrd/X2opLgNuXQPYKd+np3ctd+/DK10rLtOxWNrl0GgrJnezLV9+pegfnk2UfhSdPnVbh9ILKD/Ql6/d7XmZ6eev3W4+vM7YycQmuDX3cXS5xdDmj3p1xbWpHME1brwFPAE8BRZENJ4aIuCGLrbpC/0tLpx/d7yWX5S7BZK+Ade/hRkgDTduWJhVfgmndI2bHfzc4qpC32934KCcTcjbBgS2uAznYA7rvJLJwkniIb+x+ScYHPPynJSRCYnLA9EbuKri+5wmJAdMblS7re964uRvpk5hcdWzGVCGYRFKoqn+PeCR1RbO20Ot89/DJz3MnFvnXXhY86i5zDdA42TWF+SeXdv3s118kHc91SaIkYWS6cxxyNruzq30SmrhrOSU2Lz3AVnhA9p9e1cE7sGzjig/q8Y1rfulzY2JAMInk3yJyK/AWkO+bGMytdkVkAvAwEA88paoPBMxPBF4AzgBygKtVdas3bzpwE64WdIeqfiAiXb3yHYFiYJaqPhzEPkRWYrK7J0L30aXTCvPd5SH8ay9LXnC/fMEdVNoP8Esuw7xO/fp7f4OwKzgBB7eUJor9fknDN9II3K/91t3dmPu0se5OeSm93SO5s9UWjQlRMGe2bylnsqpqpbfaFZF4YANwIZAFLAKmqOoavzK3AkNV9RYRmQx8Q1WvFpGBwMvASKAz8BHQF2gPdFLVJSKSjLsd8JX+6yxPzJxHUlzkfiXvXg7Zfk1jxw+6+f6d+p1O8xLM0IZ3OQl/xUVwKCugZuE9cndQ0qQI0LyjlyD8EkVKb1fjsHMcjKmWcJ/ZXsm9Kis1EshU1c1eULOBKwD/g/4VwAzv+evAYyIi3vTZqpoPbBGRTGCkqn4J7PbiyhORtUCXgHXGrrh4aNfXPYZ+203zjQH3JZXdK2DrF+V36pckl9MguWP96dRXhWM5sH9jQLLY5IaiFuWXlm2cDG17uxsaDbumNHG06QVJLaK3D8Y0YME0bSEiYzh11NYLVSzWBdjh9zoLGFVRGVUtFJFDQIo3/auAZcucnikiacDpwNfB7EPM8h8DPuCy0ulH93tNYstLE8y6d0vnN2tXWmOpK536+UfgwKbSJOGfNE4cKi0X1wja9HRJos+FZWsXzdvXnwRqTD0RzPDfF4FeuNvs+kZtKa6votJFy5kW2I5WUZlKlxWR5riTIn+sqofL3bjIzcDNAN26dSuvSGxr1tadDNZ7fOk0X6e+f3KpqFPfV3up7U79ogI4uK2cpqhNkBdwG5uWXV1tYsi3yyaLll0hPqjfOMaYGBDMtzUdGKjVvyhXFuB/yc9UTr0hlq9Mlogk4O51cqCyZUWkES6JvKSqb1a0cVWdBcwC10dSzdhjU4Wd+mv8msaWw+LnofC4m1/Sqe8bknxa6J365Q2hzcl0TVMHt7qbFfk0aeOSQ89xpX0Xbfu42pMNLDCmXggmkazCjZLaXc11LwL6iEgPYCcwGZgaUGYOcD3wJXAVMFdVVUTmAP8SkYdwne19gIVe/8nTwFpVfaia8dRPCYnuIm2dTy+dVlzkDuy7V7ib52SvgDXvwJLn3XyJg7Z9A5rGyunUP2UIra8Po5whtCm9oONgGHSlV7Po46bZ2f/G1HvBJJK2wBoRWUjZ4b+TKlvI6/OYBnyAG/77jKquFpGZQIaqzsElhRe9zvQDuGSDV+5VXCd6IXCbqhaJyNnAtcBKEVnmbepXqvp+Nfa5/ouLd01a7fqV06nvN1ps62ew8tXS5Vp1g/aD3CiyU4bQxkErbwht97PL1i5sCK0xDVoww3/PLW+6qn4akYgiIGaG/8aiI/tKhyLvXg771kHTlIAhtH1sCK0xDUy4h/+WSRgichauiarOJBJTiebtoPcF7mGMMTUQ7PDfYbjk8R3cfdsbxmXkjTHGVKnCROJd9XcyMAV3+ZJXcE1h59VSbMYYY+qAymok64DPgMtVNRNARH5SK1EZY4ypMyobavMtIBv4RET+ISLjKf9EQWOMMQ1YhYlEVd9S1auB/sA84CdABxH5u4hcVEvxGWOMiXFVDv5X1aOq+pKqXoY7w3wZcFfEIzPGGFMnVOssMlU9oKpP1vvb7BpjjAmanY5sjDEmJJZIjDHGhMQSiTHGmJBYIjHGGBMSSyTGGGNCYonEGGNMSCyRGGOMCYklEmOMMSGxRGKMMSYklkiMMcaExBKJMcaYkFgiMcYYExJLJMYYY0JiicQYY0xILJEYY4wJiSUSY4wxIbFEYowxJiSWSIwxxoTEEokxxpiQWCIxxhgTEkskxhhjQmKJxBhjTEgskRhjjAmJJRJjjDEhsURijDEmJJZIjDHGhMQSiTHGmJBYIjHGGBMSSyTGGGNCYonEGGNMSCyRGGOMCYklEmOMMSGJaCIRkQkisl5EMkXkrnLmJ4rIK978r0UkzW/edG/6ehG5ONh1GmOMqV0RSyQiEg/8DZgIDASmiMjAgGI3AQdVtTfwF+CP3rIDgcnAIGAC8LiIxAe5TmOMMbUoIYLrHglkqupmABGZDVwBrPErcwUww3v+OvCYiIg3fbaq5gNbRCTTWx9BrDNs/rjwj6w7sC4SqzbGmIjr36Y/vxz5y4hvJ5JNW12AHX6vs7xp5ZZR1ULgEJBSybLBrBMAEblZRDJEJGPfvn0h7IYxxpjKRLJGIuVM0yDLVDS9vMQXuE43UXUWMAsgPT293DJVqY1MbowxdV0kayRZQFe/16nArorKiEgC0BI4UMmywazTGGNMLYpkIlkE9BGRHiLSGNd5PiegzBzgeu/5VcBcVVVv+mRvVFcPoA+wMMh1GmOMqUURa9pS1UIRmQZ8AMQDz6jqahGZCWSo6hzgaeBFrzP9AC4x4JV7FdeJXgjcpqpFAOWtM1L7YIwxpmriKgD1W3p6umZkZEQ7DGOMqTNEZLGqpgdT1s5sN8YYExJLJMYYY0JiicQYY0xILJEYY4wJSYPobBeRfcC2Gi7eFtgfxnDqAtvn+q+h7S/YPldXd1VtF0zBBpFIQiEiGcGOXKgvbJ/rv4a2v2D7HEnWtGWMMSYklkiMMcaExBJJ1WZFO4AosH2u/xra/oLtc8RYH4kxxpiQWI3EGGNMSCyRGGOMCUmDSyQi0lVEPhGRtSKyWkTu9Ka3EZEPRWSj97e1N72/iHwpIvki8rOq1hOLwrjPSSKyUESWe+v5bbT2qSrh2me/9cWLyFIRebe29yUY4dxfEdkqIitFZJmIxOzVTsO8z61E5HURWeetb3Q09qkqYfwu9/M+X9/jsIj8uMZxNbQ+EhHpBHRS1SUikgwsBq4EvgccUNUHROQuoLWq/lJE2gPdvTIHVfXBytajqhG5f3wowrjPAjRT1SMi0gj4HLhTVb+Kwm5VKlz77Le+nwLpQAtVvaw29yUY4dxfEdkKpKtqTJ+8F+Z9fh74TFWfEnevo6aqmlvb+1SVcP9fe+uMB3YCo1S1RiduN7gaiaruVtUl3vM8YC3uvu9XAM97xZ7HvfGo6l5VXQQUBLmemBPGfVZVPeK9bOQ9YvKXSLj2GUBEUoFLgadqIfQaCef+1hXh2mcRaQGcg7s/Eqp6MhaTCETscx4PbKppEoEGmEj8iUgacDrwNdBBVXeD+7CA9jVcT0wLdZ+9Jp5lwF7gQ1Wt9/sM/BX4BVAcoRDDKgz7q8D/RGSxiNwcqTjDKcR97gnsA571mi+fEpFmEQw3LMJ1/MLdUPDlUGJpsIlERJoDbwA/VtXD0V5PbQhHrKpapKrDgFRgpIgMDmeM4RbqPovIZcBeVV0c9uAiIEz/j2ep6nBgInCbiJwTtgAjIAz7nAAMB/6uqqcDR4G7whhi2IXx+NUYmAS8Fko8DTKReO37bwAvqeqb3uQ9Xvujrx1ybw3XE5PCtc8+XtV/HjAhzKGGTZj2+SxgktdvMBs4X0T+GaGQQxKuz1hVd3l/9wJvASMjE3HowrTPWUCWX+36dVxiiUlh/i5PBJao6p5QYmpwicTrMH4aWKuqD/nNmgNc7z2/HninhuuJOWHc53Yi0sp73gS4AFgX/ohDF659VtXpqpqqqmm4JoC5qvrdCIQckjB+xs28Tly85p2LgFXhjzh0YfyMs4EdItLPmzQeiLlBMxC+ffYzhRCbtQBQ1Qb1AM7GtQGvAJZ5j0uAFOBjYKP3t41XviPuF8thINd73qKi9UR7/yK8z0OBpd56VgH3RHvfIr3PAescB7wb7X2L8GfcE1juPVYDv472vtXGZwwMAzK8db2NG/UU9X2M8D43BXKAlqHG1eCG/xpjjAmvBte0ZYwxJrwskRhjjAmJJRJjjDEhsURijDEmJJZIjDHGhMQSiTFhJs7nIjLRb9p3ROS/0YzLmEix4b/GRIB36ZjXcNdCiseN95+gqptCWGeCqhaGKURjwsYSiTERIiJ/wl23qRmQp6q/E5HrgduAxsACYJqqFovILNxlOZoAr6jqTG8dWcCTuEvR/FVVQ7omkjGRkBDtAIypx34LLAFOAuleLeUbwBhVLfSSx2TgX8BdqnpARBKAT0TkdS29t81RVT0rGjtgTDAskRgTIap6VEReAY6oar6IXACMADLcJZNoAuzwik8RkZtw38nOwEBKr/f0Su1Gbkz1WCIxJrKKKb2PiQDPqOrd/gVEpA9wJzBSVXO9qwsn+RU5WiuRGlNDNmrLmNrzEfAdEWkLICIpItINd7HEPOCwdwnwi6MYozHVZjUSY2qJqq4Ukd8CH4lIHO72p7fgrjq7BndF5c3AF9GL0pjqs1FbxhhjQmJNW8YYY0JiicQYY0xILJEYY4wJiSUSY4wxIbFEYowxJiSWSIwxxoTEEokxxpiQ/H/YlPNA3clL9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 95% CI of the Annual Rate of Attempts\n",
    "yearMEAN = np.array(attemptRates.groupby(attemptRates.index.map(lambda x: x.year)).mean())\n",
    "yearSTD = np.array(attemptRates.groupby(attemptRates.index.map(lambda x: x.year)).std())\n",
    "N = len(np.array(attemptRates.groupby(attemptRates.index.map(lambda x: x.year))))\n",
    "Z = 1.96\n",
    "        \n",
    "# low and high boundaries\n",
    "yearL95 = yearMEAN - Z * (yearSTD / np.sqrt(5))\n",
    "yearH95 = yearMEAN + Z * (yearSTD / np.sqrt(5))\n",
    "\n",
    "# approximate Conversion annual rate (ignoring leaps)\n",
    "yearMEAN *= 365\n",
    "yearH95 *= 365\n",
    "yearL95 *= 365\n",
    "\n",
    "# Can't have a negative value\n",
    "for i in range(len(yearL95)):\n",
    "    if yearL95[i] < 0:\n",
    "        yearL95[i] = 0\n",
    "\n",
    "# Plot creation\n",
    "plt.title(\"95% CI of the Annual Rate of Attempts (Sample)\")\n",
    "plt.ylabel(\"Annual Rate of Attempts\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.plot(yearH95, label=\"High\")\n",
    "plt.plot(yearMEAN, label=\"Mean\")\n",
    "plt.plot(yearL95, label=\"Low\")\n",
    "plt.legend()\n",
    "plt.xticks(np.arange(0, 6), np.arange(2012, 2018))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a higher standard deviation in our sample data comparative to our simulated uniform data; however, the mean is still roughly the same between the two samples. This indicates that there has not been much change in the rate of attempts of UCSD students throughout the five years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>Actual</th>\n",
       "      <th>Expected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>67.163985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>52.717255</td>\n",
       "      <td>67.163985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>63.633674</td>\n",
       "      <td>67.163985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>65.449889</td>\n",
       "      <td>67.163985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>85.170315</td>\n",
       "      <td>67.163985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>69.012779</td>\n",
       "      <td>67.163985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual   Expected\n",
       "2012  67.000000  67.163985\n",
       "2013  52.717255  67.163985\n",
       "2014  63.633674  67.163985\n",
       "2015  65.449889  67.163985\n",
       "2016  85.170315  67.163985\n",
       "2017  69.012779  67.163985"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chi-Square against uniform distribution\n",
    "NumOfAttemptsAnnual = attempts.groupby(attempts.index.map(lambda x: x.year)).sum()\n",
    "percentNormalizaion = totCampusPopL[0] / np.array(totCampusPopL)\n",
    "num = pd.DataFrame(NumOfAttemptsAnnual)\n",
    "num[\"Date Occurred\"] = num[\"Date Occurred\"] * percentNormalizaion\n",
    "num[\"Expected\"] = [np.mean(num[\"Date Occurred\"]) for _ in range(len(num))]\n",
    "num.rename(columns={\"Date Occurred\": \"Actual\"}, inplace=True)\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=8.215445710491077, pvalue=0.1447548971812181)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chisquare(f_obs=num[\"Actual\"], f_exp=num[\"Expected\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not reject the null hypothesis: the annual rate of suicide attempts is uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 95% CI of the monthly rate of attemps - sample\n",
    "monthMEAN = attemptRates.groupby(attemptRates.index.map(lambda x: x.month)).mean()\n",
    "monthSTD = attemptRates.groupby(attemptRates.index.map(lambda x: x.month)).std()\n",
    "N = len(np.array(attemptRates.groupby(attemptRates.index.map(lambda x: x.month))))\n",
    "\n",
    "monthL95 = monthMEAN - Z * (monthSTD / np.sqrt(N))\n",
    "monthH95 = monthMEAN + Z * (monthSTD / np.sqrt(N))\n",
    "\n",
    "# No negative numbers\n",
    "for i in range(len(monthL95)):\n",
    "    if monthL95.iloc[i] < 0:\n",
    "        monthL95.iloc[i] = 0\n",
    "\n",
    "# approximate Conversion monthly rate\n",
    "monthH95 *= 365/12\n",
    "monthMEAN *= 365/12\n",
    "monthL95 *= 365/12\n",
    "\n",
    "plt.plot(monthH95, label=\"High\")\n",
    "plt.plot(monthMEAN, label=\"Average Rate of Attempts\")\n",
    "plt.plot(monthL95, label=\"Low\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Month\")\n",
    "plt.xticks(np.arange(1, 13), [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
    "plt.ylabel(\"Monthly Rate of Attempts\")\n",
    "plt.title(\"95% CI of Monthly Rate of Attempts (Sample)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first it appears that the monthly rates between the sample and the simulated data are roughly the same. However, upon closer inspection, we see that the rate of attempts actually coincides with the academic quarters. We see that during the breaks (i.e. Summer, Winter, and Spring) there is a dramatic drop. This is perhaps due to a mixture the students leaving campus and therefor decreasing the population and our hypothesis that stress from the progression of the academic quarter leads to a spike in the attempts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>Actual</th>\n",
       "      <th>Expected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>53</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>43</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Expected\n",
       "1       32     38.25\n",
       "2       50     38.25\n",
       "3       37     38.25\n",
       "4       40     38.25\n",
       "5       53     38.25\n",
       "6       43     38.25\n",
       "7       35     38.25\n",
       "8       30     38.25\n",
       "9       23     38.25\n",
       "10      43     38.25\n",
       "11      44     38.25\n",
       "12      29     38.25"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chi-Square against uniform distribution\n",
    "NumOfAttemptsMonthly = attempts.groupby(attempts.index.map(lambda x: x.month)).sum()\n",
    "num = pd.DataFrame(NumOfAttemptsMonthly)\n",
    "num[\"Date Occurred\"] = num[\"Date Occurred\"]\n",
    "num[\"Expected\"] = [np.mean(num[\"Date Occurred\"]) for _ in range(len(num))]\n",
    "num.rename(columns={\"Date Occurred\": \"Actual\"}, inplace=True)\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=22.856209150326794, pvalue=0.01851870355999591)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chisquare(f_obs=num[\"Actual\"], f_exp=num[\"Expected\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reject the null hypothesis: the monthly rate of suicide attempts is not uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quarter Total - sample\n",
    "def betweenDates(date, start, end):\n",
    "    return start <= date <= end\n",
    "\n",
    "# Start dates\n",
    "# Source - google docs\n",
    "\n",
    "# Fall\n",
    "startFall = [\"09/27/12\", \"09/26/13\", \"10/02/14\", \"09/24/15\", \"09/22/16\", \"09/28/17\"]\n",
    "endFall = [\"12/15/12\", \"12/14/13\", \"12/20/14\", \"12/12/15\", \"12/10/16\", \"12/16/17\"]\n",
    "# Winter\n",
    "startWinter = [\"01/09/12\", \"01/07/13\", \"01/06/14\", \"01/05/15\", \"01/04/16\", \"01/09/17\"]\n",
    "endWinter = [\"03/24/12\", \"03/23/13\", \"03/22/14\", \"03/21/15\", \"03/19/16\", \"03/25/17\"]\n",
    "# Spring\n",
    "startSpring = [\"04/02/12\", \"04/01/13\", \"03/31/14\", \"03/30/15\", \"03/28/16\", \"04/03/17\"]\n",
    "endSpring = [\"06/15/12\", \"06/14/13\", \"06/13/14\", \"06/12/15\", \"06/10/16\", \"06/16/17\"]\n",
    "\n",
    "# Conversion\n",
    "startFall = [datetime.strptime(d, \"%m/%d/%y\") for d in startFall]\n",
    "endFall = [datetime.strptime(d, \"%m/%d/%y\") for d in endFall]\n",
    "startWinter = [datetime.strptime(d, \"%m/%d/%y\") for d in startWinter]\n",
    "endWinter = [datetime.strptime(d, \"%m/%d/%y\") for d in endWinter]\n",
    "startSpring = [datetime.strptime(d, \"%m/%d/%y\") for d in startSpring]\n",
    "endSpring = [datetime.strptime(d, \"%m/%d/%y\") for d in endSpring]\n",
    "\n",
    "# Attempt Rates\n",
    "fallRates = [];\n",
    "for start, end in zip(startFall, endFall):\n",
    "    fallRates.append(attemptRates[attemptRates.index.map(lambda x: betweenDates(x, start, end))])\n",
    "winterRates = [];\n",
    "for start, end in zip(startWinter, endWinter):\n",
    "    winterRates.append(attemptRates[attemptRates.index.map(lambda x: betweenDates(x, start, end))])\n",
    "springRate = [];\n",
    "for start, end in zip(startSpring, endSpring):\n",
    "    springRate.append(attemptRates[attemptRates.index.map(lambda x: betweenDates(x, start, end))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEICAYAAABBBrPDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHTVJREFUeJzt3X+YXFWd5/H3h4Rk0DBhDa0CiXScBCHxB2qMirOPjuAS/BVnDEsYf4DC4rhk3HF8RhN3Ft2scYnrGAXhcRiJZBicBMPo9GoURxF/oEIaRSTBaE8STDaCHRMCURLo8N0/zmm4KatO3U53p9PJ5/U8/fStc889dU7dqvupe2/VLUUEZmZmrRw10h0wM7NDm4PCzMyKHBRmZlbkoDAzsyIHhZmZFTkozMysyEFhw0rSqyRtHel+DDVJz5H0Y0kPS3rPSPdnpEkaL2m9pGeOdF+qBvL8k/QeSZcPd59GIwfFKCLpQkk/lfQ7SfdLulrSxGG+v+8NV/s17n+zpEck7c7jvU7ShJrLdkoKSWOHqXvvB26NiGMj4opCP66T1CfpxIbyD0v6p4ayWyVdPEz9LRqCQL8E+E5E3J/bmyzpJknbJe3Kz9sLh6Szw+ca4K2Snj7SHTnUOChGCUnvA5YCfwNMBF4GdAJfl3T0MNzfoDewQ7SRfkNETABOB14ILBqCNofCycC6UgVJTwXeDOwC3nIwOjWC3gVcX7l9PbCF9DhNAt4OPDAC/aotIvYAXyX11aoiwn+H+B/wh8Bu4D83lE8Afg1ckG9fB3ykMv9VwNbK7YXAvwMPA+uBP63MuxC4DVgG7ABuAvYA+/J9P5jrjQc+DvyS9ML/DHBM9f6ADwD3kzYWT/SBFHI3NYzhSuCTLca9GTircvtjwFcqt18H/Bh4iLRR+nBl3i+ByH3fDbw8l78TuBfYCdwMnFx43N9ICoMHgVuB03L5Lflx2ZPbPqXF8m/P/fpvwD2V8jnAo8BjefmfAEsa2vx0rnsq8G95nWyoPgfy+r6atHHbndffM4FP5vH9DHhhw+O5KK/7ncDngD8Ango8AjxeebxOBGYD3fnxfQD4RItxPisvP7ZSths4vfDYfiE/R3YB3wFmDvW4WrwGTiQ9t3uBTcB7Gvr1FuBbI/2aP9T+RrwD/quxktKGpa/6QqzMWwHckKevoxwU5+YXylHAecBvgRPyvAvzffwlMBY4Jpd9r+H+Pgl0AU8DjgX+L/C/K/fXR9rzGZ/beKIPwAn5Po/Lt8eSgu7FLca9mRwUwGTgp8CnGsb3vDye5+eN2ZvyvE5SUFQ3Xm8CeoDT8n3/LfD9Fvd9Su7ra4CjSYeaeoBxef6twMVt1ts3SeH2jPy4vKgy78PAPzXU369N0gZ8C/CO3N8XAdvJG9W8vrcDLyZt8G8hbfzeDowBPkJlo5cfz3uAKXn93db/fGl8ruSyHwBvy9MTgJe1GOfrgHUNZd/I7c8HntVkmXfm58/4/Jy6qzJvWMaVnyd3ApcB44BnAxuBsyttvQjYMdKv+UPtz4eeRofjge0R0ddk3q+AjjqNRMQXImJbRDweEauAX5DeNfbbFhFXRkRfRDzSuLwkAf8FeG9E7IiIh4GPkjYG/R4HPhQRexvbiIhfkd49npuL5uRx3Vno9pckPUzaYP4a+FClvVsj4qd5PHcD/wy8stDWu0ihdm9+LD8KnC7p5CZ1zyPtvfxbRDxG2os6Bjij0P4TJD0L+BPg8xHxACk0LqizbMXrgc0R8bm8Tn5Eejc8r1LnixFxZ6TDJl8E9kTEP0bEPmAV6XBd1acjYktE7CDtxZxfuP/HgGmSjo+I3RHxwxb1jiPtpVadC3wX+B/AJkl3SXpJ/8yIWB4RD0fEXlJovqDhfNtwjOslQEdELI6IRyNiI/AP7P/8fZh0aNcqHBSjw3bg+BbH/E8g7Ua3Jent+QX7oKQHgeeSQqjfljZNdABPAe6stPE19g+q3vzibmUF8NY8/Vb2P67dzJsi4ljSO8NTq/2V9FJJ35LUK2kX8BcN42l0MvCpSt93AAJOalL3ROC+/hsR8Tjp8WlWt5m3AfdGxF359g3Anw/wfNLJwEv7+5v7/BbSYZh+1eP+jzS53Xjyv7qO7yONs5WLSHtWP5O0VtLrW9TbSdo7eEJE7IyIhRExk7RHdRcp9CVpjKTLJf27pIdIewSw/7objnGdDJzY8Hh+MPev37Gkw2FW4aAYHX4A7AX+rFqYT5aeA3w7F/2WtCHv98xK3ZNJ754WAJMi4jjS7roq9RsvJdx4ezvpRTozIo7LfxMjnWxutUyjLwHPl/Rc0jvmG9rUT41GfJt0SOLjleLPkw6DTYmIiaTzJf3jadaPLcC7Kn0/LiKOiYjvN6m7jbRhAZ7Ym5oC/L86/SUdJnl2/rTW/cAnSBvCcwr9ayzbAny7ob8TIuLdNfvQzJTK9LNI42zan4j4RUScDzyddDhxdX7ONbqbNNamH16IiO2k9XYi6dDQnwNzgbNI7947c1U1W76mVuOq2gJsang8j42I11bqnEY6Z2QVDopRICJ2Af8TuFLSHElHS+oknRDczpMb27uA10p6Wv48+19VmnkqaWPQCyDpHaQ9ipIHgMmSxuV+PE4Km2X9HyGUdJKkswcwlj3AatJG/o6I+GXdZUnHsl8j6fR8+1jS8eQ9kmaTNkD9ekmHwZ5dKfsMsEjSzNz3iZLOpbkbgddJOjPvBbyPFNbNQmU/kl4O/BHpsN7p+e+5pDH3H356AOiUVH0NPtDQ3y8Dp0h6W17nR0t6iaTT2vWh4NL80dWnkd5Nr6rc96Tq4R9Jb5XUkdf7g7l4X2ODEbGVhsOYkpZKeq6ksZKOBd4N9ETEb0jrbS/wG9Ibm48OYjztxlV1B/CQpA9IOibv2Ty3ekiMdOjyq0PQn8OKg2KUiIiPkV4AHycdR91EepGdFRG/zdWuJ70b2gx8ncqLJSLWA39H2jt5gHQS+LY2d3sL6VM/90vanss+QDqp+8N82OAbwHMGOJwV+f7bHXbaT0T0Av9IOu4N8F+BxfkcxmWkjXt/3d+RjlXflg8zvCwivkh6Z7wy9/0ennyH33hfG0iHxq4khfEbSB/VfbRGVy8A/jWfP7m//w/4FPD6vDH7Qq77G0k/ytOfAuZJ2inpinwO6D+RjqFvI31KqP+DAgfq86Tnxsb895E83p+RzvFszI/XiaRzSOsk7c59m184rPj3pMNt/Z5COrfwYL6fk0mfIoO0Du8j7Z2tB1qd+xj0uKry+Y03kIJ7E2m9fpZ8TkLSHwCvJT0/rUIR/uGi0UjSO0l7Ga8Y4LvyEZdP9P4MeGZEPDTS/TlSSNpM+lTVN4ah7fGkjyqfmT+0cNAM1bgk/SXpMOb7h6Rjh5Hh+taqDbOIWC7pMdKncEZNUORDLX8NrHRIHD7yp5dmjHQ/BiMirhzpPhyqHBSjWEQM6NDNSMsnQh8gHXaYM8LdMbOafOjJzMyKfDLbzMyKDotDT8cff3x0dnaOdDfMzEaVO++8c3tEtL2yw2ERFJ2dnXR3d490N8zMRhVJ97Wv5UNPZmbWhoPCzMyKHBRmZlZUKyjy9YU2SOqRtLDJ/PGSVuX5t+frEPXPW5TLN1SvCdSqzXx1ySWSfi7pXvn3iM3MRlTbk9mSxgBXkX7AZSuwVlJXvnZQv4uAnRExTdJ80vVozpM0g3SdmpmkK0d+Q9IpeZlWbV5IuhLkqRHxuPz7tWZmI6rOHsVs0lUfN+YLoq0kXSK4ai5PXkhrNXBmvizzXNKlGvZGxCbSxeRmt2nz3cDifMVKIuLXBz48MzMbrDpBcRL7/yjIVn7/x1ueqJN/OWwX6QfVWy1bavOPSHsj3ZK+Kml6s05JuiTX6e7trfW7PWZmdgDqBEWzHxNpvO5HqzoDLYd0CeU9ETGL9NsHy5t1KiKuiYhZETGro6PWL4GamdkBqBMUW9n/16Mm8/u/HvVEnfwrVxNJPzPZatlSm1tJvwsM6Xr2z6/RRzMzGyZ1vpm9FpguaSrph0bms/8viUH6OcoLSD+KMw+4JSJCUhfweUmfIJ3Mnk76lSkV2vwS8GrSnsQrgZ8f+PDMbKR1LvzKSHfhsLX58tcdlPtpGxQR0SdpAXAzMAZYHhHrJC0GuiOiC7gWuF5SD2lPYn5edp2kG0m/YtUHXJp/ZYpmbea7vBy4QdJ7gd3AxUM3XDMzG6ha13qKiDXAmoayyyrTe4Cmvz0cEUtIP0nZts1c/iBwcGLSzMza8jezzcysyEFhZmZFDgozMytyUJiZWZGDwszMihwUZmZW5KAwM7MiB4WZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVlRrYsCHs58CeThc7AugWxmw+uIDwobfRzuw8fhbs340JOZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVmRg8LMzIocFGZmVuSgMDOzIgeFmZkVOSjMzKzIQWFmZkUOCjMzK6oVFJLmSNogqUfSwibzx0taleffLqmzMm9RLt8g6ex2bUq6TtImSXflv9MHN0QzMxuMtlePlTQGuAp4DbAVWCupKyLWV6pdBOyMiGmS5gNLgfMkzQDmAzOBE4FvSDolL1Nq828iYvUQjM/MzAapzh7FbKAnIjZGxKPASmBuQ525wIo8vRo4U5Jy+cqI2BsRm4Ce3F6dNs3M7BBQJyhOArZUbm/NZU3rREQfsAuYVFi2XZtLJN0taZmk8c06JekSSd2Sunt7e2sMw8zMDkSdoFCTsqhZZ6DlAIuAU4GXAE8DPtCsUxFxTUTMiohZHR0dzaqYmdkQqBMUW4EplduTgW2t6kgaC0wEdhSWbdlmRPwqkr3A50iHqczMbITUCYq1wHRJUyWNI52c7mqo0wVckKfnAbdEROTy+flTUVOB6cAdpTYlnZD/C3gTcM9gBmhmZoPT9lNPEdEnaQFwMzAGWB4R6yQtBrojogu4FrheUg9pT2J+XnadpBuB9UAfcGlE7ANo1ma+yxskdZAOT90F/MXQDdfMzAaqbVAARMQaYE1D2WWV6T3AuS2WXQIsqdNmLn91nT6ZmdnB4W9mm5lZkYPCzMyKHBRmZlbkoDAzsyIHhZmZFTkozMysyEFhZmZFDgozMytyUJiZWZGDwszMihwUZmZW5KAwM7MiB4WZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVmRg8LMzIocFGZmVuSgMDOzIgeFmZkVOSjMzKzIQWFmZkUOCjMzK3JQmJlZkYPCzMyKagWFpDmSNkjqkbSwyfzxklbl+bdL6qzMW5TLN0g6ewBtXilp94ENy8zMhkrboJA0BrgKOAeYAZwvaUZDtYuAnRExDVgGLM3LzgDmAzOBOcDVksa0a1PSLOC4QY7NzMyGQJ09itlAT0RsjIhHgZXA3IY6c4EVeXo1cKYk5fKVEbE3IjYBPbm9lm3mEPk/wPsHNzQzMxsKdYLiJGBL5fbWXNa0TkT0AbuASYVlS20uALoi4lelTkm6RFK3pO7e3t4awzAzswNRJyjUpCxq1hlQuaQTgXOBK9t1KiKuiYhZETGro6OjXXUzMztAdYJiKzClcnsysK1VHUljgYnAjsKyrcpfCEwDeiRtBp4iqafmWMzMbBjUCYq1wHRJUyWNI52c7mqo0wVckKfnAbdEROTy+flTUVOB6cAdrdqMiK9ExDMjojMiOoHf5RPkZmY2Qsa2qxARfZIWADcDY4DlEbFO0mKgOyK6gGuB6/O7/x2kDT+53o3AeqAPuDQi9gE0a3Poh2dmZoPVNigAImINsKah7LLK9B7SuYVmyy4BltRps0mdCXX6Z2Zmw8ffzDYzsyIHhZmZFTkozMysyEFhZmZFDgozMytyUJiZWZGDwszMihwUZmZW5KAwM7MiB4WZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVmRg8LMzIocFGZmVuSgMDOzIgeFmZkVOSjMzKzIQWFmZkUOCjMzK3JQmJlZkYPCzMyKHBRmZlbkoDAzsyIHhZmZFdUKCklzJG2Q1CNpYZP54yWtyvNvl9RZmbcol2+QdHa7NiVdK+knku6WtFrShMEN0czMBqNtUEgaA1wFnAPMAM6XNKOh2kXAzoiYBiwDluZlZwDzgZnAHOBqSWPatPneiHhBRDwf+CWwYJBjNDOzQaizRzEb6ImIjRHxKLASmNtQZy6wIk+vBs6UpFy+MiL2RsQmoCe317LNiHgIIC9/DBCDGaCZmQ1OnaA4CdhSub01lzWtExF9wC5gUmHZYpuSPgfcD5wKXNmsU5IukdQtqbu3t7fGMMzM7EDUCQo1KWt8l9+qzkDL00TEO4ATgXuB85p1KiKuiYhZETGro6OjWRUzMxsCdYJiKzClcnsysK1VHUljgYnAjsKybduMiH3AKuDNNfpoZmbDpE5QrAWmS5oqaRzp5HRXQ50u4II8PQ+4JSIil8/Pn4qaCkwH7mjVppJp8MQ5ijcAPxvcEM3MbDDGtqsQEX2SFgA3A2OA5RGxTtJioDsiuoBrgesl9ZD2JObnZddJuhFYD/QBl+Y9BVq0eRSwQtIfkg5P/QR499AO2czMBqJtUABExBpgTUPZZZXpPcC5LZZdAiyp2ebjwCvq9MnMzA4OfzPbzMyKHBRmZlbkoDAzsyIHhZmZFTkozMysyEFhZmZFDgozMytyUJiZWZGDwszMihwUZmZW5KAwM7MiB4WZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVmRg8LMzIocFGZmVuSgMDOzIgeFmZkVOSjMzKzIQWFmZkUOCjMzK3JQmJlZkYPCzMyKHBRmZlZUKygkzZG0QVKPpIVN5o+XtCrPv11SZ2Xeoly+QdLZ7dqUdEMuv0fScklHD26IZmY2GG2DQtIY4CrgHGAGcL6kGQ3VLgJ2RsQ0YBmwNC87A5gPzATmAFdLGtOmzRuAU4HnAccAFw9qhGZmNih19ihmAz0RsTEiHgVWAnMb6swFVuTp1cCZkpTLV0bE3ojYBPTk9lq2GRFrIgPuACYPbohmZjYYdYLiJGBL5fbWXNa0TkT0AbuASYVl27aZDzm9Dfhas05JukRSt6Tu3t7eGsMwM7MDUSco1KQsatYZaHnV1cB3IuK7zToVEddExKyImNXR0dGsipmZDYGxNepsBaZUbk8GtrWos1XSWGAisKPNsi3blPQhoAN4V43+mZnZMKqzR7EWmC5pqqRxpJPTXQ11uoAL8vQ84JZ8jqELmJ8/FTUVmE4679CyTUkXA2cD50fE44MbnpmZDVbbPYqI6JO0ALgZGAMsj4h1khYD3RHRBVwLXC+ph7QnMT8vu07SjcB6oA+4NCL2ATRrM9/lZ4D7gB+k8+H8S0QsHrIRm5nZgNQ59ERErAHWNJRdVpneA5zbYtklwJI6bebyWn0yM7ODw9/MNjOzIgeFmZkVOSjMzKzIQWFmZkUOCjMzK3JQmJlZkYPCzMyKHBRmZlbkoDAzsyIHhZmZFTkozMysyEFhZmZFDgozMytyUJiZWZGDwszMihwUZmZW5KAwM7MiB4WZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVmRg8LMzIocFGZmVuSgMDOzIgeFmZkV1QoKSXMkbZDUI2lhk/njJa3K82+X1FmZtyiXb5B0drs2JS3IZSHp+MENz8zMBqttUEgaA1wFnAPMAM6XNKOh2kXAzoiYBiwDluZlZwDzgZnAHOBqSWPatHkbcBZw3yDHZmZmQ6DOHsVsoCciNkbEo8BKYG5DnbnAijy9GjhTknL5yojYGxGbgJ7cXss2I+LHEbF5kOMyM7MhUicoTgK2VG5vzWVN60REH7ALmFRYtk6bRZIukdQtqbu3t3cgi5qZ2QDUCQo1KYuadQZaXltEXBMRsyJiVkdHx0AWNTOzAagTFFuBKZXbk4FtrepIGgtMBHYUlq3TppmZHQLqBMVaYLqkqZLGkU5OdzXU6QIuyNPzgFsiInL5/PypqKnAdOCOmm2amdkhoG1Q5HMOC4CbgXuBGyNinaTFkt6Yq10LTJLUA/w1sDAvuw64EVgPfA24NCL2tWoTQNJ7JG0l7WXcLemzQzdcMzMbqLF1KkXEGmBNQ9lllek9wLktll0CLKnTZi6/AriiTr/MzGz4+ZvZZmZW5KAwM7MiB4WZmRU5KMzMrMhBYWZmRQ4KMzMrclCYmVmRg8LMzIocFGZmVuSgMDOzIgeFmZkVOSjMzKzIQWFmZkUOCjMzK3JQmJlZkYPCzMyKHBRmZlbkoDAzsyIHhZmZFTkozMysyEFhZmZFDgozMytyUJiZWZGDwszMihwUZmZW5KAwM7OiWkEhaY6kDZJ6JC1sMn+8pFV5/u2SOivzFuXyDZLObtempKm5jV/kNscNbohmZjYYbYNC0hjgKuAcYAZwvqQZDdUuAnZGxDRgGbA0LzsDmA/MBOYAV0sa06bNpcCyiJgO7Mxtm5nZCKmzRzEb6ImIjRHxKLASmNtQZy6wIk+vBs6UpFy+MiL2RsQmoCe317TNvMyrcxvkNt904MMzM7PBGlujzknAlsrtrcBLW9WJiD5Ju4BJufyHDcuelKebtTkJeDAi+prU34+kS4BL8s3dkjbUGMvh4Hhg+0h3og4tHekeHBJGzfoCr7Ns1KyzIVhfJ9epVCco1KQsatZpVd5sT6ZU//cLI64Brmk273AmqTsiZo10P6wer6/Rx+vs99U59LQVmFK5PRnY1qqOpLHARGBHYdlW5duB43Ibre7LzMwOojpBsRaYnj+NNI50crqroU4XcEGengfcEhGRy+fnT0VNBaYDd7RqMy/zrdwGuc1/PfDhmZnZYLU99JTPOSwAbgbGAMsjYp2kxUB3RHQB1wLXS+oh7UnMz8uuk3QjsB7oAy6NiH0AzdrMd/kBYKWkjwA/zm3bk464w22jnNfX6ON11kDpTbyZmVlz/ma2mZkVOSjMzKzIQXGIkbRP0l2Vv85C3U5J9+TpV0n68sHq55FC0jJJf1W5fbOkz1Zu/52kD0pa3byF/dr64HD10/Yn6b9LWifp7vw6avzuV2nZNza7VNGRzEFx6HkkIk6v/G0e6Q4d4b4PnAEg6SjSl7FmVuafAXwzIuY1WbbRgIMiX+7GBkDSy4HXAy+KiOcDZ7H/F3xLy46NiK6IuHw4+zjaOChGgbzn8F1JP8p/Z4x0n44gt5GDghQQ9wAPS/oPksYDpwE7K3t2F0r6F0lfyxe2/Fguvxw4Jr+7vSGXvVXSHbns7/tDQdJuSYsl3Q68/OAO97BwArA9IvYCRMT2iNgmabOkpfkxv0PSNABJ10n6hKRvAUvzOvx0Zd4Vkr4vaaOkebn8KElX572WL0ta0z/vcOSgOPT0b0zukvTFXPZr4DUR8SLgPOCKkevekSUitgF9kp5FCowfAP0b8FnA3cCjDYudTlpPzwPOkzQlIhby5N7iWySdluu8IiJOB/YBb8nLPxW4JyJeGhHfG+YhHo6+DkyR9PO8MX9lZd5DETEb+DTwyUr5KcBZEfG+Ju2dAPwxaS+lf0/jz4BO0jq+mMM80OtcwsMOrkfyhqPqaODTkvo3KKcc/G4d0fr3Ks4APkG6/tgZwC7SoalG34yIXQCS1pOup9N46ONM4MXA2nQtTI4hvSGAtI5vGtohHDkiYrekFwP/EfgTYFXlnMM/V/4vqyz2hf7veDXxpYh4HFgv6Rm57I/zMo8D9+e9kcOWg2J0eC/wAPAC0l7gnpHtzhGn/zzF80iHnrYA7wMeApY3qb+3Mr2P5q8zASsiYlGTeXsKGy2rIT9+twK3SvopT145ovrFser0bwvNVdenGv4fEXzoaXSYCPwqv3t5G+nb7Hbw3EY67LAjIvZFxA7gONLhhh8MoJ3HJB2dp78JzJP0dABJT5NU60qeVibpOZKmV4pOB+7L0+dV/g9k3TX6HvDmfK7iGcCrBtHWIc97FKPD1cBNks4lXQur9O7Hht5PSZ92+nxD2YSI2C5pQs12rgHulvSjfJ7ib4Gv509TPQZcypMbNDtwE4ArJR1HunRQD+knCV4PjM8fEjgKOH8Q93ET6fDhPcDPSeetdg2m04cyX8LDzI4IkjYDsyJiSH5rQtKEfD5kEulip6+IiPuHou1DjfcozMwOzJfzXss44H8driEB3qMwM7M2fDLbzMyKHBRmZlbkoDAzsyIHhZmZFTkozMys6P8Duq3AbM/DqJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = np.mean(fallRates) * np.array(fallRates).shape[1]\n",
    "w = np.mean(winterRates) * np.array(winterRates).shape[1]\n",
    "s = np.mean(springRate) * np.array(springRate).shape[1]\n",
    "plt.bar([\"Fall\", \"Winter\", \"Spring\"], [f, w, s])\n",
    "plt.title(\"Quarterly Rate of Attempts (Sample)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside from the slight increase in the rates during Spring quarter, we see that that the rates between the three academic quarters are rather uniform even without the use of chi-square. This indicates, that using the quarters to try and predict the occurances of suicides attempt is not possible. However, if we look at the rates of attempts per week during the quarters, we might be able to find a better prediction pattern that we can use to forecast future attempts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the weekly rates of attempts\n",
    "weekMean = attemptRates.groupby(pd.Grouper(freq='W-MON')).mean() * 7\n",
    "\n",
    "fallRates = []\n",
    "winterRates = []\n",
    "springRates = []\n",
    "\n",
    "for i in range(len(startFall)):\n",
    "    fallRates.append(weekMean[(startFall[i] <= weekMean.index) & (weekMean.index <= endFall[i])])\n",
    "    winterRates.append(weekMean[(startWinter[i] <= weekMean.index) & (weekMean.index <= endWinter[i])])\n",
    "    springRates.append(weekMean[(startSpring[i] <= weekMean.index) & (weekMean.index <= endSpring[i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalWeeks = np.array(fallRates + winterRates + springRates)\n",
    "f = np.array(fallRates)\n",
    "w = np.array(winterRates)\n",
    "s = np.array(springRates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(1, 12), totalWeeks.mean(axis=0))\n",
    "plt.xlabel(\"Week of Quarter\")\n",
    "plt.xticks(np.arange(1, 12))\n",
    "plt.ylabel(\"Rate of Attempts\")\n",
    "plt.title(\"Rate of Attempts per Week During Academic Quarter (Sample)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEWCAYAAAApTuNLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xu4VVW9//H3h83NuJmAJoJCiSaCKZKkmGGWYhla4r2StKyTVpqn1H5lZVrZ0ahz0tLUoymKZJlEmpnXFLygooiImqKQpqKg4vHC5fv7Y4wNk+Vaey9wr73XZn9ez7OfPdeYc44x5lxzru8ac441piICMzOzttaprStgZmYGDkhmZlYnHJDMzKwuOCCZmVldcEAyM7O64IBkZmZ1wQGpjkjaVtL9kl6V9PW2ro+1DEm3SPpilcsukPSxWtepJa3L9q1jvh+WNL+l821vJF0h6YC2rkcpSSFp6yqW20HSjGry7HABKZ/wr0taJunfki6W1LPKdQfnN6Fzjar3beCWiOgVEf/dRD0ulrRC0oCS9B9IuqwkrSYfFtWQNFbSorYouymSOuf3f5dC2hH5vS1Ne6RtatkyJA2RtErSuW1dl3UVEf+IiG3XZ11JwyRNk/Ry/oJ3k6QPtXQdS8qs6gN6HfPcAfgAcE1+3VXS2ZIW5WP4SUmTWrLMlhYRDwJLJX2quWU7XEDKPhURPYEdgZ2AU9q4Po22AuY2tYCkHsCBwMvAEa1Rqfau9AtERKwAZgIfKSTvATxSJu22mlewtj4PLAEOldStrSvTGiS9D7gDmAMMAQYAfwJuKH7haMHy3vEX1Cby+DIwOdaMYHAKMArYBegF7Anc/07LbwWTSdvStIjoUH/AAuBjhdc/A/5SeP1J0hv8CrAQ+EFh3tNAAMvy3645/ShgHunEvx7Yqonyx5OCzlLgFmC7nH4TsBJ4I+e9TYX1P5/r9Q3goUL6OOAtYHle/wHgjJI8f5WXfT9wA/ASMB84uJDPxcC5wHV5nTuA9wC/yNv3CLBTyf48BXg4z/9foDvQA3gdWFXYXwNIJ9KsvH+fA35eYTvHAouA7wCLczlHFOZ3A87K78lzwG+AjUrWPQn4N3Bpmfy/B/y58PphYGKZtM/m6U7AycA/gReBqcAmhWU/BMzI7+sDwNjCvFuAL+bpzYEHgf9s4vh82/7M8x4ifZlqXLZL3jc7NnG8/RP4j7yPJpTM275wHDwHfCen70IK2EuBZ4FfAV0L6308Hwcv53m3Nm5fc+cD6fz5KvAY8CrwI+B9ubxX8n7tWnwfC+sOAv4IvJDfg19V2OZLgWvLpP8auLlc3qWfDVXsgwCOzdvxJOmLSwCvkY71Q/Jy+wGzcz4zgB1KyjspHw9vAp3L1PkJYPfC6+nA8U28343H6Kv5GPp0Yd5E0vk8KdfnCWC3nL4QeB44suSz4Df5GHk1v8+l7+XWzZ2Pef4WpM+Dbk1+Pjc1c0P8KznoBpK+Rf2yMH8sMIL0AbRD3rkH5HmD85vQubD8AcDjwHZAZ+C7wIwKZW+TD9iPkz5Mvp3XbTwBb6FwYlfI40ZSEN0MWAGMLMz7AXBZyfJr5UkKFAuBL+T6jiR9qG1fOAgXAzuTAstNpBPu80ADcDr5pC7sz4dIHxab5AP+9MK+LD3pZwKfy9M9gQ9V2M6xeft+ng/2j+R9t22e/wtgWi6zF/Bn4Ccl656Z192oTP4fIX0QdwL6AU8B78rvd2PaKmDLvPzxwJ35mOkGnAdcUTjZXgQ+kdf9eH7dv/gekI6fR4Fjmjk+K+3PbwNXFpbdH5jTRF4fJn3QvRv4H2BaYV4v0gftifl97gWMzvN2JgXYzrnO88gfgnm/vAJMIB3DJ+R93RhwmzwfSOfPNKA3KSC+STqm3wv0IX2IHll6/JCOvQdIH6Y9cp13r7Dd/wa+UCZ9z1zX7jQfkCrug8J23JDfo40KaVsXlhlJ+pAfnet/ZC6jW6G82fm9LneM9sh59i+kfZf0of9V0ueUStY5iPTFrxNwCOmc2TzPm5i3/wusOZefBs4hHdN7kwJPz8JnwaukKwXdgF8Ct5fsg8aAVPF8LCz/CoWAXPa9W5cP8w3hLx8Ey/KOjnwybNzE8r8AJuXpwbw9IF0HHF143Qn4P8q0kkjfyqeWLPsv8rdpmglIwJakD8kd8+vrWTuY/oDmA9IhwD9KljkP+H7hIPxtYd7XgHmF1yOApSX78yuF158A/pmnx/L2k/424IdAv2bep7H55OlRSJua96Hyifa+wrxdgScL675FbllUyL87qeX4AeDTpMsikIJOY9qTheXnAXsVXm9Oao12Jn3LvbQk/+tZ88F6CymwLgAOq+L4rLQ/B5CO29759VXAt5vI6wLgT4X9sxzYNL8+DLi/ynPmeODqPP154M7CPJFao40BqcnzgXT+jCnMvxc4qfD6bOAXpcdPrv8LlGlFlKnvCmBcmfT35/IHVDg2F1C4elJpHxS246Mly5QGpF8DPypZZj7wkUJ5RzWxHVvkPLsX0hpILbM7SMH8GQqtmjJ5zAb2z9MTgccK80bk/DcrpL3Ims+Xi4EphXk9SVdcBhW3l2bOx0Lav4A9mnrvOuo9pAMiohfpoHw/6VsfAJJGS7pZ0guSXga+UpxfxlbALyUtlbSU9K1bpIOp1ADSN3EAImIVqbVSbtlyPkcKDrPz68nA4ZK6VLl+Y31HN9Y31/kI0mW5Rs8Vpl8v87q0E8jCwvRTpO2s5GhSS/ERSfdI2q+JZZdExGtl8u5Pas3cW9iGv+b0Ri9ExBuVMs7z7iZ9+9sD+EeedXshrXj/aCvg6kJ580gn52Z53kEl+3R3UtBqdATphLyqie1tVHZ/RsQzpA+iAyVtDOxLOgbeRtJGpG/Lk/O6M0nfhg/PiwwiXdopt+42kqbnTj+vAD9mzTkwoFi/SJ80xfpWcz6s6/HVWN+nIt3/a85i1t73jTYnfYgubi6DZvZBo4VlVi3aCjix5LgYxNrnR1N5LM3/ezUmRMTKiDgnIsYAG5Muy18kabtc789Lml0ob3hJvUv3NRHR1P4vvtfLSO9n6fldzfnYuB1LaUJHDUgARMStpG8BZxWSLyc1PQdFRB/StVA1rlImm4XAlyNi48LfRhFRrpvjM6SDFABJIh2g/6qyyp8H3ptPkn+TvnX3I30wVapfadpC4NaS+vaMiP+osg7lDCpMb0nazrL1iYjHIuIwYFPSJbWrckeNct5dMq8x78WkE2f7wjb0idRRZXVRVdT7NlLg+TBrAtI/CmnFgLQQ2Ldkv3WPiH/leZeWzOsRET8trP+DXO/LJTU0U69K+xPgEuCzpGAzM5dfzqdJl8XOLRwvW5COocbteV+FdX9Nukc0NCJ6k+7jNZ4DzxbrVziGG63L+bAuFgJbVtmB4O+k/VPqYFLr7i3SN/p3Nc7I70nxA7SpfdCouWNsIXBGyb54V0RcUU0e+cvYP0lf4MrNfz0iziHdqxsmaSvgt8BxQN+I2Jh0+be03uui+F73JF2Se6ZkmWbPx9wjuCuphVhRhw5I2S+Aj0vaMb/uBbwUEW/kHjmHF5Z9gXTJ7L2FtN8Ap0jaHkBSH0nlTgZIl5w+KWmv3Ko5kdTsbvZklbQr6QNkF1LvwB1J334uJ12bhvTtZ7Ck4vv6XEl9pwPbSPqcpC7574ON37DW07GSBkrahHTiXlkou6+kPoXt+Kyk/rl12PhtaWUTef8wd3X9MOkG8e/zur8FJknaNOe7haR91rHet5HuKwwi3buA1EIaS9q/xYD0G+CMfNIjqb+k/fO8y4BPSdpHUoOk7rnL+8DC+stJH5I9gEtL3qNSlfYnpN5iI0mdWn7XRB5HAheRLss0Hi9jgB0ljSAdB++RdLykbpJ6SRqd1+1Fut6/TNL7SZ0iGv0F2F7SZ3Jw+Dprt67X5XxYF3eTguFPJfXI+3hMhWV/COwm6QxJm+Rt+xrp3sn38zKPAt0lfTKfi98l3Sdp1NQ+qKT0XPst8JV81UW53p+U1KvC+uVcS6HnZ36/xkraSOnnC0fmut7PmntOL+Rlv0D6jHgnPiFpd0ldSR1Q7oqItVp1VZ6PY4GbIuLNJktr6nrehvhHmevEpG9Df8jTE0iXSV4lnbS/onBfBjiN9IYvJd+QJ11Km8OannkXNVH+p0kffi+Teq1sX5h3CxXuIZFO9D+USd+FFNQ2AfqSPlCXAPfFmmu5j+a0/85p25I+WBp7K93E2teNTy/k/0XSb6MaX28NrCjZn429wpaSvsG/qzD/olzGUlJT/zLSjd5lpN6GB1TY3rGkexP/j/QN7GlyZ4g8vzvpMsoTeb/PA75eXLeKY6EnKVBMK0l/GHimJK0T8E3SN7xXSd9cf1yYPzq/ny/l/foX1nSIWP2+5nr/Pe/nThWOz4r7My9zAekbfs8K27UF6T7KiDLzrgXOytPDSfdQl5A6Apyc0xu7wC8jtRhPY+2b2ePyMVWpl13F84G332e5HZhYeH06cEG595HUWvxTPp4Wk4/nCvtgOOn8fYX0hWcpqYVbXGYiKcg9D/wna3dqaG4frLUdOe0rOb+l5J6reV/dw5reer8HelX6LKqwHXPJnRdIXafvzft+KSlQ71dY/gzSMbiYdAVl9XuTt7e4DVuTr7oW0haRO4uwdi+7ZaQvaEPK7QOaOB/z/L8A45s7Jxs30my9SFpAOuD/3sL5jiV9ERjY3LIdjaRTST8L+Gxb16U9yC3VO0kddy5s6/qsK0mXkzpD/amVy72Y9IXgu+8wnxHA+RGxa3PL1mrEATOrgXwZ72hSK8SqEBGLJO0L7C+pZ6Sb8+1GRBze/FL1KyLmkK7UNMv3kMzaCUlfIl0Cuy4i2vsIEq0qIuZExOntLRh1NL5kZ2ZmdcEtJDMzqwu+h1RGv379YvDgwW1dDTOzduXee+9dHBGlP4itmgNSGYMHD2bWrFltXQ0zs3ZF0lPNL1WZL9mZmVldcEAyM7O64IBkZmZ1wfeQqrR8+XIWLVrEG29UHEC6Q+nevTsDBw6kS5d1GWjczKwyB6QqLVq0iF69ejF48GDSAMcdV0Tw4osvsmjRIoYMGdLW1TGzDYQv2VXpjTfeoG/fvh0+GAFIom/fvm4tmlmLckBaBw5Ga3hfmFlLc0AyM7O64HtI6+uvo1o2v3HN/xC3oaGBESNGrH79pz/9iUojSixYsID99tuPhx56iFtuuYWzzjqL6dOnt1RtzcxanANSO7LRRhsxe/bstq6GmbWwAWcPKJv+zImlTwvfsPmSXTu3YMECPvzhDzNy5EhGjhzJjBnNPg3dzKwuuYXUjrz++uvsuOOOAAwZMoSrr76aTTfdlBtuuIHu3bvz2GOPcdhhh3kcPjNrlxyQ2pFyl+yWL1/Occcdx+zZs2loaODRRx9to9p1HFOnTi2bfvDBB7dyTcw2LA5I7dykSZPYbLPNeOCBB1i1ahXdu3dv6yqZma0X30Nq515++WU233xzOnXqxKWXXsrKlSvbukpmZuvFLaT1VUU37dbw1a9+lQMPPJDf//737LnnnvTo0aOtq2Rmtl4ckNqRZcuWvS1t6NChPPjgg6tf/+QnPwHSQwYfeughAMaOHcvYsWNbpY5mZuvLl+zMzKwuOCCZmVld8CU7s0oqDg/17VathllHUdMWkqRxkuZLelzSyWXmd5N0ZZ5/l6TBhXmn5PT5kvZpLk9JQ3Iej+U8u+b0LSXdLOl+SQ9K+kQtt9nMzNZPzQKSpAbgHGBfYBhwmKRhJYsdDSyJiK2BScCZed1hwKHA9sA44FxJDc3keSYwKSKGAkty3gDfBaZGxE45z3Nrsb1mZvbO1LKFtAvweEQ8ERFvAVOA/UuW2R+4JE9fBeyl9KCd/YEpEfFmRDwJPJ7zK5tnXuejOQ9yngfk6QB65+k+QMcardDMrJ2o5T2kLYCFhdeLgNGVlomIFZJeBvrm9DtL1t0iT5fLsy+wNCJWlFn+B8DfJH0N6AF8rFxlJR0DHAOw5ZZbNrtxlYaPWV/NDTtzwgknsNVWW3H88ccDsM8++zBo0CAuuOACAE488UT69u3Lfffdx1VXXdVUVvz4xz/mO9/5TstU3MyshdSyhVTukaJR5TItlQ5wGHBxRAwEPgFcKult2x0R50fEqIgY1b9//zLZta3ddttt9Ujeq1atYvHixcydO3f1/BkzZrDXXns1G4wgBaR15REgzKzWahmQFgGDCq8H8vbLZauXkdSZdEntpSbWrZS+GNg451Fa1tHAVICImAl0B/q9g+1qE2PGjFkdkObOncvw4cPp1asXS5Ys4c0332TevHm8+93vZvjw4QBcfPHFfOYzn2HcuHEMHTqUb3879Qw7+eSTV48afsQRRwBw2WWXscsuu7Djjjvy5S9/eXXw6dmzJ6eeeiqjR49m5syZbbDVZtaR1DIg3QMMzb3fupI6FEwrWWYacGSengDcFBGR0w/NvfCGAEOBuyvlmde5OedBzvOaPP00sBeApO1IAemFFt/aGhswYACdO3fm6aefZsaMGey6666rA8WsWbPYYYcd6Nq161rrzJ49myuvvJI5c+Zw5ZVXsnDhQn7605+uHjV88uTJzJs3jyuvvJI77rhj9YjhkydPBuC1115j+PDh3HXXXey+++5tsdlm1oHU7B5Svid0HHA90ABcFBFzJZ0GzIqIacCFpEtoj5NaRofmdedKmgo8DKwAjo2IlQDl8sxFngRMkXQ6cH/OG+BE4LeSTiBdxpuYA1i709hKmjFjBt/85jf517/+xYwZM+jTpw+77bbb25bfa6+96NOnDwDDhg3jqaeeYtCgQWstc+ONN3LvvffywQ9+EEjPXNp0002B9Mj0Aw88sMZbZWaW1PSHsRFxLXBtSdqphek3gIMqrHsGcEY1eeb0J0i98ErTHwbGrGvd61HjfaQ5c+YwfPhwBg0axNlnn03v3r056qij3rZ8t27dVk83NDSwYsWKty0TERx55JGrx8Ar6t69Ow0NDS27EWZmFXjooHZkzJgxTJ8+nU022YSGhgY22WQTli5dysyZM9l1112rzqdLly4sX74cYHVHiOeffx6Al156iaeeeqom9Tcza4qHDlpPbfF00BEjRrB48WIOP/zwtdKWLVtGv379yo4GXs4xxxzDDjvswMiRI5k8eTKnn346e++9N6tWraJLly6cc845bLXVVrXaDDOzstROb6fU1KhRo2LWrLWfdzRv3jy22267NqpRfdrg90mFseymvlJ+LDs/wtzW14CzB5RNf+bE9vU7fkn3RkSlQSCb5Ut2ZmZWFxyQzMysLjggmZlZXXBAMjOzuuBedmbtQLnBfN2JwjY0biGZmVldcAtpPVXqprm+qu3eecYZZ3D55ZfT0NBAp06dOO+88xg9uvSpHuVNmzaNhx9+mJNPftvDe83M2pwDUjsyc+ZMpk+fzn333Ue3bt1YvHgxb731VlXrrlixgvHjxzN+/Pga19LMbP04ILUjzz77LP369Vs9Rl2/fukpGoMHD+aQQw7h5ptvBuDyyy9n6623ZuLEiWyyySbcf//9jBw5khEjRjBr1ix+9atfMXHiRHr37s2sWbP497//zc9+9jMmTJjAqlWrOO6447j11lsZMmQIq1at4qijjmLChAkV62Vm1hIckNqRvffem9NOO41tttmGj33sYxxyyCF85CMfAaB3797cfffd/O53v+P4449n+vTpADz66KP8/e9/p6GhgYsvvnit/J599lluv/12HnnkEcaPH8+ECRP44x//yIIFC5gzZw7PP/882223XdmBW802BO4sUl/cqaEd6dmzJ/feey/nn38+/fv355BDDlkdZA477LDV/4sP0zvooIMqjth9wAEH0KlTJ4YNG8Zzzz0HwO23385BBx1Ep06deM973sOee+5Z240yM8vcQmpnGhoaGDt2LGPHjmXEiBFccsklAEhrnuJenO7Ro0fFvIqPp2gc09BjG66/ch1d2ttYZGZtyS2kdmT+/Pk89thjq1/Pnj179ajcV1555er/6/IoilK77747f/jDH1i1ahXPPfcct9xyyzuqs5lZtdxCWk9t8c132bJlfO1rX2Pp0qV07tyZrbfemvPPP5/p06fz5ptvMnr0aFatWsUVV1yx3mUceOCB3HjjjQwfPpxtttmG0aNHr37qrJlZLTkgtSM777wzM2bMKDvv2GOP5fvf//5aaaWdGCZOnMjEiRPLzmt8llKnTp0466yz6NmzJy+++CK77LILI0aMaJH6m5k1xQHJ3ma//fZj6dKlvPXWW3zve9/jPe95T1tXyTYkFZ4zxbhZ5dOtw3BA2gAsWLCgRfPzfSOrJxvKw+usee7UsA7cA20N7wsza2kOSFXq3r07L774oj+IScHoxRdfpHv37m1dFTPbgPiSXZUGDhzIokWLeOGFF9q6KnWhe/fuDBw4sK2rYWbrqR4vhTogValLly4MGTKkrathZrbB8iU7MzOrCw5IZmZWFxyQzMysLjggmZlZXXBAMjOzuuCAZGZmdcEByczM6oIDkpmZ1QUHJDMzqwsOSGZmVhcckMzMrC44IJmZWV1wQDIzs7qwTgFJUidJvWtVGTMz67iaDUiSLpfUW1IP4GFgvqRv1b5qZmbWkVTTQhoWEa8ABwDXAlsCn6smc0njJM2X9Likk8vM7ybpyjz/LkmDC/NOyenzJe3TXJ6ShuQ8Hst5di3MO1jSw5LmSrq8mrqbmVnrqiYgdZHUhRSQromI5dVkLKkBOAfYFxgGHCZpWMliRwNLImJrYBJwZl53GHAosD0wDjhXUkMzeZ4JTIqIocCSnDeShgKnAGMiYnvg+Grqb2ZmrauagHQesADoAdwmaSvg5SrW2wV4PCKeiIi3gCnA/iXL7A9ckqevAvaSpJw+JSLejIgngcdzfmXzzOt8NOdBzvOAPP0l4JyIWAIQEc9XUXczM2tl1TzC/M8R8d+NLyQ9DRxVxXpbAAsLrxcBoystExErJL0M9M3pd5asu0WeLpdnX2BpRKwos/w2ud53AA3ADyLir1XU3+rJX0eVTx83q3XrUUcGnD2gbPozJz7TyjUxaxnVtJD+UHwREUFqmTRHZdKiymVaKh1S0B0KjAUOAy6QtPHbKisdI2mWpFkvvPBCmezMzKyWKraQJL2fdA+nj6TPFGb1BrpXkfciYFDh9UCg9Ktb4zKLJHUG+gAvNbNuufTFwMaSOudWUnH5RcCd+d7Xk5LmkwLUPcWKRMT5wPkAo0aNKg2cZmZWY021kLYF9gM2Bj5V+BtJui/TnHuAobn3W1dSJ4VpJctMA47M0xOAm3ILbBpwaO6FN4QUQO6ulGde5+acBznPa/L0n4A9AST1I13Ce6KK+puZWSuq2EKKiGuAayTtGhEz1zXjfE/oOOB60r2biyJirqTTgFkRMQ24ELhU0uOkltGhed25kqaSfve0Ajg2IlYClMszF3kSMEXS6cD9OW/ysntLehhYCXwrIl5c1+0xM7PaqqZTw3OS/gx8iHRfZiZwQkQ028qIiGtJv10qpp1amH4DOKjCumcAZ1STZ05/gtQLrzQ9gG/mPzMzq1PVdGq4HJgKbA4MAH4PXFHLSpmZWcdTTUBSRFwaESvy32W8vbecmZnZO1LNJbub8xA9U0iB6BDgL5I2AYiIl2pYPzMz6yCqCUiH5P9fLkk/ihSg3tuiNTIzsw6p2YAUEUNaoyJmZtaxNRuQ8oCmnwQGF5ePiJ/XrlpmZtbRVDWWHfAGMAdYVdvqmJlZR1VNQBoYETvUvCZmZtahVdPt+zpJe9e8JmZm1qFV00K6E7haUidgOWlk7YiI3jWtmZmZdSjVBKSzgV2BOXkYHjMzsxZXzSW7x4CHHIzMzKyWqmkhPQvcIuk64M3GRHf7bp/8lFEzq1fVBKQn81/X/GdmZtbiqhmp4YcAknpExGu1r5KZmXVEzd5DkrRrfrjdvPz6A5LOrXnNzMysQ6nmkt0vgH3Ijx+PiAck7VHTWplZm5o6dWrZ9IMPPriVa2IdSTW97IiIhSVJK2tQFzMz68CqaSEtlLQbEJK6Al8nX74zM2splVpl1nFUE5C+AvwS2AJYBPwN+GotK9Wu/XVU+fRxs1q3HmZm7Uw1AWnbiDiimCBpDHBHbapkZmYdUTX3kP6nyjQzM7P1VrGFJGlXYDegv6RvFmb1BhpqXTEzM+tYmrpk1xXomZfpVUh/BZhQy0qZmVnHUzEgRcStwK2SXo+InxXnSTqINOiqmZlZi6jmHtKhZdJOaemKmJlZx9bUPaR9gU8AW0j678Ks3sCKWlfMzMw6lqbuIT0DzALGA/cW0l8FTqhlpczMrONp6h7SA8ADki6PiOWN6ZIGAV8E/qsV6mdmZh1Es/eQImK5pH6S/kPSbcAtwGY1r5mZmXUoTd1D6gV8Gjgc2Aa4GnhvRAxspbqZmVkH0tQ9pOeBu4HvArdHREj6dOtUy1qCB6s0s/akqUt23wG6A78GTpH0vtapkpmZdURNdWqYBEyS9F7gMOBPwABJJwFXR8SjrVRHM6sTA84eUDb9mROfaeWa2Iaomk4NT0TEGRExAvgg0Ae4ruY1MzOzDqWqJ8Y2iog5EfGdiPDlOzMza1HrFJDMzMxqxQHJzMzqQsWAJOnG/P/M1quOmZl1VE21kDaX9BFgvKSdJI0s/lWTuaRxkuZLelzSyWXmd5N0ZZ5/l6TBhXmn5PT5kvZpLk9JQ3Iej+U8u5aUNUFSSBpVTd3NbAPy11Hl/6yuNPXD2FOBk4GBwM9L5gXw0aYyltQAnAN8HFgE3CNpWkQ8XFjsaGBJRGwt6VDgTOAQScNIj73YHhgA/F3SNnmdSnmeCUyKiCmSfpPz/nWuSy/g68BdTdW53ap4Yn27VathZvZONPU7pKuAqyR9LyJ+tB557wI8HhFPAEiaAuwPFAPS/sAP8vRVwK8kKadPiYg3gSclPZ7zo1yekuaRAuTheZlLcr6/zq9/BPwM+M/12A4rp1wQHDer7KL+7YqZVaOpFhIAEfEjSeOBPXLSLRExvYq8twAWFl4vAkZXWiYiVkh6Geib0+8sWXeLPF0uz77A0ohYUbq8pJ2AQRExXZIDkpk1yV+g2k6zAUnST0itk8k56RuSxkREc0+NVZm0qHJR3yyrAAAQyklEQVSZSunl7nlVXF5SJ2ASMLFyNXNFpGOAYwC23HLL5hY3M6svlS7dV7hyUY+q6fb9SeDjEXFRRFwEjMtpzVkEDCq8Hkh66F/ZZSR1Jo0C8VIT61ZKXwxsnPMopvcChgO3SFoAfAiYVq5jQ0ScHxGjImJU//79q9g8MzNrSc22kLKNSYECUtCoxj3AUElDgH+ROikcXrLMNOBIYCYwAbgpjyo+Dbhc0s9JnRqGkkYeV7k88zo35zym5DyviYiXgX6NhUm6BfjPiGg/XxnMaq3sN2t3iLHWV01A+glwf/7AF+leUnOX6xrvCR0HXA80ABdFxFxJpwGzImIacCFwae608BIpwJCXm0rqALECODYiVgKUyzMXeRIwRdLpwP05bzMzayeq6dRwRW5ZfJAUkE6KiH9Xk3lEXAtcW5J2amH6DeCgCuueAZxRTZ45/QnW9MSrVJ+x1dTbzMxaX1WX7CLiWdLlNTMzs5rwWHZmZlYXHJDMzKwuVBWQJO0u6Qt5un/u5WZmZtZiqvlh7PeBUcC2wP8CXYDLgDG1rZpZB+RxCa0Dq6aF9GlgPPAaQEQ0/uDUzMysxVQTkN6KiCAP+yOpR22rZGZmHVE1AWmqpPNIQ/N8Cfg7cEFtq2VmZh1NNT+MPUvSx4FXSPeRTo2IG2peMzMz61Cq6dRwZkScBNxQJs3MzKxFVHPJ7uNl0vZt6YqYmVnHVrGFJOk/gK8C75X0YGFWL+COWlfMzMw6lqYu2V0OXEca7fvkQvqrEfFS+VXMzMzWT8WAlJ8l9DJwGICkTYHuQE9JPSPi6dapopmZdQTN3kOS9ClJjwFPArcCC0gtJzMzsxZTTaeG00mP/n40IoYAe+F7SGZm1sKqCUjLI+JFoJOkThFxM7BjjetlZmYdTDUP6FsqqSdwGzBZ0vOkx4qbmZm1mGoC0v7A68AJwBFAH+C0WlbKzKzdKzdy+7hZrV6NqVOntnqZ66uaoYNey5OrgEskNQCHApNrWTEzM+tYKt5DktRb0imSfiVpbyXHAU8AB7deFc3MrCNoqoV0KbAEmAl8EfgW0BXYPyJmt0LdzMysA2kqIL03IkYASLoAWAxsGRGvtkrNzMysQ2mq2/fyxomIWAk86WBkZma10lQL6QOSXsnTAjbKrwVERPSuee3MzKzDaGosu4bWrIiZmXVs1YzUYGZmVnMOSGZmVheqGanBrG5V+hX6wQf7p3Jm7Y1bSGZmVhfcQrIWU09jZg04e0DZ9GdOfKaVa2Jm1XILyczM6oIDkpmZ1QUHJDMzqwsOSGZmVhcckMzMrC44IJmZWV1wt28zs1ZSTz+NqEduIZmZWV1wQDIzs7pQ00t2ksYBvwQagAsi4qcl87sBvwN2Bl4EDomIBXneKcDRwErg6xFxfVN5ShoCTAE2Ae4DPhcRb0n6JukR7CuAF4CjIuKpWm53OR5zzcysaTVrIUlqAM4B9gWGAYdJGlay2NHAkojYGpgEnJnXHQYcCmwPjAPOldTQTJ5nApMiYiiwJOcNcD8wKiJ2AK4CflaL7TUzs3emlpfsdgEej4gnIuItUutl/5Jl9gcuydNXAXtJUk6fEhFvRsSTwOM5v7J55nU+mvMg53kAQETcHBH/l9PvBAbWYFvNzOwdquUluy2AhYXXi4DRlZaJiBWSXgb65vQ7S9bdIk+Xy7MvsDQiVpRZvuho4LpylZV0DHAMwJZbbtnUdrUoDwJqZpbUsoWkMmlR5TItlb6mIOmzwCjgv8osS0ScHxGjImJU//79yy1iZmY1VMsW0iJgUOH1QKD0a3/jMoskdQb6AC81s2659MXAxpI651bSWmVJ+hjw/4CPRMSb73C7zMysBmrZQroHGCppiKSupE4K00qWmQYcmacnADdFROT0QyV1y73nhgJ3V8ozr3NzzoOc5zUAknYCzgPGR8TzNdpWMzN7h2rWQsr3hI4Drid10b4oIuZKOg2YFRHTgAuBSyU9TmoZHZrXnStpKvAwqbv2sRGxEqBcnrnIk4Apkk4n9ay7MKf/F9AT+H3q+8DTETG+VtttZmbrp6a/Q4qIa4FrS9JOLUy/ARxUYd0zgDOqyTOnP0HqhVea/rF1rriZmbU6j9RgZmZ1wQHJzMzqggOSmZnVBQckMzOrCw5IZmZWFxyQzMysLjggmZlZXXBAMjOzuuCAZGZmdcEByczM6oIDkpmZ1QUHJDMzqwsOSGZmVhcckMzMrC44IJmZWV1wQDIzs7rggGRmZnXBAcnMzOqCA5KZmdUFByQzM6sLDkhmZlYXHJDMzKwuOCCZmVldcEAyM7O64IBkZmZ1wQHJzMzqggOSmZnVBQckMzOrCw5IZmZWFxyQzMysLjggmZlZXXBAMjOzuuCAZGZmdcEByczM6oIDkpmZ1QUHJDMzqwsOSGZmVhcckMzMrC44IJmZWV2oaUCSNE7SfEmPSzq5zPxukq7M8++SNLgw75ScPl/SPs3lKWlIzuOxnGfX5sowM7P6UbOAJKkBOAfYFxgGHCZpWMliRwNLImJrYBJwZl53GHAosD0wDjhXUkMzeZ4JTIqIocCSnHfFMszMrL7UsoW0C/B4RDwREW8BU4D9S5bZH7gkT18F7CVJOX1KRLwZEU8Cj+f8yuaZ1/lozoOc5wHNlGFmZnVEEVGbjKUJwLiI+GJ+/TlgdEQcV1jmobzMovz6n8Bo4AfAnRFxWU6/ELgur/a2PAvLb53TBwHXRcTwSmVExOKS+h4DHJNfbgvMb8HdUaofsLjZpdp3mR1hG9uizI6wjR2lzA1xG7eKiP7ru3LnlqxJiXKtkNLoV2mZSunlWnRNLV9tPYiI84Hzyyzb4iTNiohRrVFWW5XZEbaxLcrsCNvYUcrsCNu4rmp5yW4RMKjweiDwTKVlJHUG+gAvNbFupfTFwMY5j9KyKpVhZmZ1pJYB6R5gaO791pXUSWFayTLTgCPz9ATgpkjXEKcBh+YeckOAocDdlfLM69yc8yDneU0zZZiZWR2p2SW7iFgh6TjgeqABuCgi5ko6DZgVEdOAC4FLJT1OarUcmtedK2kq8DCwAjg2IlYClMszF3kSMEXS6cD9OW8qldHGWuXSYBuX2RG2sS3K7Ajb2FHK7AjbuE5q1qnBzMxsXXikBjMzqwsOSGZmVhcckFqRpIskPZ9/G9Ua5Q2SdLOkeZLmSvpGK5TZXdLdkh7IZf6w1mXmchsk3S9peiuVt0DSHEmzJc1qpTI3lnSVpEfye7prjcvbNm9f498rko6vcZkn5OPmIUlXSOpey/Jymd/I5c2t1faVO/clbSLphjzc2Q2S3t0KZR6Ut3OVpLrr/u2A1LouJg2F1FpWACdGxHbAh4Bjywzf1NLeBD4aER8AdgTGSfpQjcsE+AYwrxXKKdozInZsxd91/BL4a0S8H/gANd7eiJift29HYGfg/4Cra1WepC2ArwOjImI4qeNSTTshSRoOfIk0CswHgP0kDa1BURfz9nP/ZODGPNzZjfl1rct8CPgMcFsLl9UiHJBaUUTcRiv+Bioino2I+/L0q6QPsC1qXGZExLL8skv+q2nPGUkDgU8CF9SynLYkqTewB7n3aES8FRFLW7EKewH/jIinalxOZ2Cj/JvBd/H23y62tO1Io7z8X0SsAG4FPt3ShVQ494vDmhWHO6tZmRExLyJqOQrNO+KA1EHkUc53Au5qhbIaJM0GngduiIhal/kL4NvAqhqXUxTA3yTdm4edqrX3Ai8A/5svTV4gqUcrlNvoUOCKWhYQEf8CzgKeBp4FXo6Iv9WyTFKLYQ9JfSW9C/gEa//4vpY2i4hnIX15BDZtpXLrlgNSByCpJ/AH4PiIeKXW5UXEynyZZyCwS74sUhOS9gOej4h7a1VGBWMiYiRp5PljJe1R4/I6AyOBX0fETsBrtPwlnrLyj9DHA7+vcTnvJrUahgADgB6SPlvLMiNiHukJADcAfwUeIF3qtjbggLSBk9SFFIwmR8QfW7PsfEnpFmp732wMMF7SAtLo7x+VdFkNywMgIp7J/58n3VfZpcZFLgIWFVqbV5ECVGvYF7gvIp6rcTkfA56MiBciYjnwR2C3GpdJRFwYESMjYg/SJa7Hal1m9pykzQHy/+dbqdy65YC0AcuP2bgQmBcRP2+lMvtL2jhPb0T6kHmkVuVFxCkRMTAiBpMuK90UETX9Vi2ph6RejdPA3qRLPzUTEf8GFkraNiftRRrJpDUcRo0v12VPAx+S9K587O5FK3RUkbRp/r8l6YZ/a2wrrD2sWXG4sw6rlqN9WwlJVwBjgX6SFgHfj4gLm17rHRkDfA6Yk+/pAHwnIq6tYZmbA5coPUyxEzA1IlqlK3Yr2gy4On1m0hm4PCL+2grlfg2YnC+hPQF8odYF5vsqHwe+XOuyIuIuSVcB95Eum91P6wx18wdJfYHlpGHKlrR0AeXOfeCnwFRJR5OC8UGtUOZLwP8A/YG/SJodEftUzqV1eeggMzOrC75kZ2ZmdcEByczM6oIDkpmZ1QUHJDMzqwsOSGZmVhcckMwySZOKoz1Lul7SBYXXZ0v65nrmvaz5pSqu+/U8uvfkMvN2z6OrPyJpvqRj17ecCmV/pyXzM2uKA5LZGjPIIwNI6gT0A7YvzN8NuKMN6vVV4BMRcUQxUdJ7gMuBr+QRwMcAR0l6x4ODKukErHNAyr9BM1tnDkhma9zBmqFqtieNvvCqpHdL6kYaGfp+AEnfknSPpAdVeOaTpM/mFstsSeeVfjhL6idppqRPlhYu6Zv5uTwPNbbUJP2GNLDqNEknlKxyLHBxYUT3xaRBZr+V171Y0oRC/svy/56SbpR0n9IznfbP6YNzS+xc0o9TLySNvD27sXVWafskLZN0mqS7gJo+p8k2XA5IZlken25FHkJmN2AmaXT0XYFRwIMR8ZakvYGhpPHrdgR2lrSHpO2AQ0gDr+4IrARWt2okbQb8BTg1Iv5SLFvSzqSRF0aTnl31JUk7RcRXSI9g2DMiJpVUeXugdFDZWUBzz7x6A/h0Hhx2T+DsPFQPwLbA7yJip4j4AvB6fibSEc1sXw/goYgYHRG3N1O+WVkeOshsbY2tpN2An5OeH7Ub8DLpkh6ksev2JreWgJ6kALUD6UF29+TP941YM2BmF9JD2I6NiFvLlLs7cHVEvAYg6Y/AhwtllCPW71lTAn6cRyhfRdrGzfK8pyLizgrr7UXl7VtJGsTXbL05IJmtrfE+0gjSJbuFwInAK8BFeRkBP4mI84orSvoacElEnFIm3xWk1sw+pIfAlVKZtObMJbXcphXSdia1khrL7JTrJqBrTj+CNJbZzhGxPI+U3vio8NeaKE9U3r43ImLlemyD2Wq+ZGe2tjuA/YCX8nOdXgI2Jl22m5mXuZ7UeaAnpEdv5xGjbwQmFEaP3kTSVnmdAI4C3i+p3HOMbgMOyCNd9yA9tfQfzdT1HGCipB1zeX2BM4Af5fkLSAEK0nOGuuTpPqRnSC2XtCewFZUtV3qECc1sn9k75haS2drmkHrXXV6S1jN3GiAi/pbvp8zMl66WAZ+NiIclfZf0JNlO5NGjgafyeislHQr8WdIrEXFuYwERcZ+ki4G7c9IFEdHU5Toi4lmlB9idL6kPMBiYWLgk+FvgGkl3k4JJY+tncq7DLGA2TT8e5HzgQUn35ftIFbfP7J3yaN9mG4j8G6SvAHvU4hEKZrXmgGRmZnXB95DMzKwuOCCZmVldcEAyM7O64IBkZmZ1wQHJzMzqggOSmZnVhf8PRp1Z8JjCFmEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(1, 12) - .2, f.mean(axis=0), width=.2, label=\"Fall\", color=\"orange\", alpha=.8)\n",
    "plt.bar(np.arange(1, 12), w.mean(axis=0), width=.2, label=\"Winter\", color=\"darkgrey\")\n",
    "plt.bar(np.arange(1, 12) + .2, s.mean(axis=0), width=.2, label=\"Spring\", color=\"green\", alpha=.9)\n",
    "\n",
    "plt.xlabel(\"Week of Quarter\")\n",
    "plt.xticks(np.arange(1, 12))\n",
    "\n",
    "plt.ylabel(\"Rate of Attempts\")\n",
    "plt.title(\"Rate of Attempts per Week by Academic Quarter (Sample)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spring quarter has the a high attempt number after midterms. Fall is more uniform through out the quarter in number of attempts. Winter Quarter peaks during the midterm seasons and falls during the last two weeks of the quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>Week</th>\n",
       "      <th>Rate Observed</th>\n",
       "      <th>Rate Expected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.991503</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.516567</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.166402</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.226923</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.612783</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1.869863</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1.467571</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.080722</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1.694283</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1.520383</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1.522962</td>\n",
       "      <td>1.673077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Week  Rate Observed  Rate Expected\n",
       "0      1       0.991503       1.673077\n",
       "1      2       1.516567       1.673077\n",
       "2      3       1.166402       1.673077\n",
       "3      4       1.226923       1.673077\n",
       "4      5       2.612783       1.673077\n",
       "5      6       1.869863       1.673077\n",
       "6      7       1.467571       1.673077\n",
       "7      8       2.080722       1.673077\n",
       "8      9       1.694283       1.673077\n",
       "9     10       1.520383       1.673077\n",
       "10    11       1.522962       1.673077"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Num of Attempts / 6 years / 52 weeks / population mean over 6 years\n",
    "expectedPerWeek = len(df) / 6 / 52\n",
    "# Chi square tests are for values > 5\n",
    "weekComparison = pd.DataFrame({'Week' :[i for i in range(1,12)] ,'Rate Observed' : totalWeeks.mean(axis=0) * np.mean(totCampusPopL),'Rate Expected': [expectedPerWeek] * 11})\n",
    "weekComparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=1840349.2756461096, pvalue=0.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exact test of goodness of fit\n",
    "stats.chisquare(weekComparison[\"Rate Observed\"], weekComparison[\"Rate Expected\"] * 100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will not be able to use chisquare in these cases since observed rates are under 5. Chi-square fails, but visually looking at the graph, we see a trend of that is discussed in our hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday = attemptRates.copy()\n",
    "weekday.index = list(map(lambda x: x.weekday(), weekday.index))\n",
    "weekdayRates = weekday.groupby(weekday.index).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEICAYAAAB8lNKlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3XuYXVV9//H3x4QEBEw0xLYkQGIzKhOkVIdQfLxgsBKqNlCDv1DkBxVFWmh/rVoJWhHyJJa01thWqFKJxVAdYqwyahS1gBfEkMFwC5gyJsHEgJ2QBAiXwMTv74+1BnZOzmXPZE9mkM/reebJ2Wuv9V1r77Nzvvt29lFEYGZmVpUXDPcAzMzsN4sTi5mZVcqJxczMKuXEYmZmlXJiMTOzSjmxmJlZpZxYhpmkb0k6q0S9NZJOaDDvBEmbKh+c7VOSQtK04R4HgKQNkt483OMYDpLeKmn5cI+jlqRrJF1Sop4kdUt65T4YVl1OLBWQ9DpJP5b0sKStkm6WdGyZthFxckRcXaLe9Ii4aa8H24CkS/IH24ya8rMl/aim7D8kLRiqsTQjaUoe5+gmdS6R9LSkHZK25/fm+AH0cZOk91Qz4mdifjiPZ4ekJyXtKkyvqbKvkaze9lQzf6Gk/64pe7mkRyS9auhHCMBC4LJC/6dKuiOPYYuk70k6fB+NZcAifTnxk8ClwzUGJ5a9JOlFwDeAfwVeAkwivaE7h3NcAyFJwJnAVqDl0dNzxLURcRBwCHAj8OXhHExEfDwiDspjOg+4pX86IqYPNF6zxPocNx/4bUnvhWe2zX8HPhkRd1XZUb11mHdAxkZEd55+BfB54P8B44CpwGeAX1c5liHwNeAtkl46LL1HhP/24g/oALY3mX8JcE1hegoQwOg8fRPwnsL89wL3Ao8C9wCvzuUbgDfn1wcA/wFsy3X+FthUiHEo8BWgF1gP/FWLZXgD8ATwLuAhYEwuPxJ4EtgF7AC2A+cCTwNP5bKvt+ozr4MvA9fk5boLeDlwEfC/wEbgLYX6NwF/D9wKPAxcB7wkz/tFXn878t/xJdZ5e24zMU+/mLQz0JvX4TeAyXnewry8T+b4n87lrwS+S0q+a4F3FuL/UX4fHgV+CXywxfo+G/hRnfIgJZ378rguB1RoczOwOI9hAWnH8O+A+/N6/AIwLtc/obhNNNiGrs793At8qGYb2gB8ELgzvwfXAvvXbKc9eSxdwKH1tu/iNk6d7anB+jmOtB1OAt6Xx7BfYf57gJ/lsX8LOKww79PAJuARYBXw2sK8BXk5vpTfq7Pr9D0f+Exhei7Q3eS9PB74Cen/xgPAv/SPFRid18WfAz/PfX4MaMttHslj6a//5rzeL87Lvx6YW+jrGuCSwvQfA3fkvn8EHFUzthuBM4blc3E4Ov1N+gNelDeCq4GTgRfXzL+EkokFOI30wXQsIGAacESet4FnPxQuA35IOkI6DLib/KFA+rC5LW+cY4CXAeuAk5osw1XAMmC/vCx/Uph3NjUfgqSktqAw3bTPvA6eBE7K/9m+kP/TfCT3+V5gfSHeTXk9HAUcSEpY19Rbfw2W55JC/TF5fW0prPMJwDuAFwIHk5Le12r6Lyb7A0nJ78/y+F+d403P8x8AXp9fv5i8M9BkfHus01wepCQ3HjiclPhmFdr0AX+Zx3AA8G7Sh/vLgIOA/wKW5von0DyxXAZ8P493MunDuzax3EraYXgJKfmcl+fNzMv/amAs6Wj9B43eH3bfxusue5118U/Af+d+Ogrlc0iJ/RV5PVwC/LAw/8w83tHAhaTtaGyet4C0Q/R20jZ7QJ1+vwr8TWG6jXT24Z+ANwEH1tQ/lpQIR+f34X+AC/K8/sTyX6Tt7Ojc/3fzenoxKUGekeu/Ob/H/5jX60zgcWBanv9MYsn9/ir/OypvCz8n7xTmOlcA/zAcn4s+FbaXIuIR4HWkDejfgV5JXZJ+axDh3kPaEFZF0hMR99ep905gYURsjYiNpL2kfseS9sznR8RTEbEuj2tuvQ4lvZCU0L4YEU8Dyxn46bAyff4wIq6PiD7SB/lE4LLcZycwRdL4Qv2lEXF3RDwGfBR4p6RRAxjTOyVtJx2JvReYk/smIh6KiK9ExOMR8SjpKOWNTWK9DdgQEZ+PiL6I+Ckp2c3J858G2iW9KCK25fmDdVlEbI+IX5D2OI8pzNscEf+ax/AEcAbpFNG6iNhBOgKcW/I02TuBj+fxbmL3bajfv0TE5ojYCny9MJYzgCUR8dOI2Jn7PV7SlEEsbyN/R9qxWhr5tFT2vjzutfn9XADMkDQJICKW5v8XfcA/kHb8ijdE/Cgivh4Rv87rsNZ40pEFOd59pIRyOGm7fUjSkvz/hvx/dWV+T9YBV7LntrQoIh6NiDtJCfrbEbEhIrYB1wO/X6j7a+BjEbEzIm4Avk36/1nrXOCK3P+uiFiSy4vXdh/Ny7PPObFUICLujYizI2IyaS/7UOBTgwh1GGmvo5VDSXvQ/YrJ5wjg0HzRenv+cP0w0CjRnUraS1qRp/8TOFnSxAGMu0yfvyq8fgLYEhG7CtOQ9rr71S7ffqTrJWUti4jxeQx3A6/pnyHphZI+K+l+SY8APwDGN0lcRwDH1SzfGcBv5/nvIJ0Ou1/S9wdyo0AdDxZeP07jdQJpOyi+9/eT9pLL7NTUbkO1sZuNZbd+c1LrP3VVifyhvx6ovbHhCODywvuwhfRhPBlA0ock/UzSw6RTZQey+3ZTbzmLtpGOLopj+XFEnBYRh5BOG88kJVMkvVLSNyU9mLel+ey5ndZu+7XTxff4oYh4vDB9P2l91zoCuLBmm/wddn8PDiadJtvnnFgqFhE/I50qOioXPUY65dLvt2vbFGwEfrdENw+QklC/4h0qG0mnlcYX/g6OiD9qEOss0ob9C0kPkvbK9gNO71+kOm1qywbaZxm1y/c06UNkQI/jjogtpL3cSyT9Ti7+AOlUynER8SLShwWk04/U6WMj8P2a5TsoIv4897EqImYDLyVdNF02kDEOZHFqpjeTPmD6HU7aSfgVNdtdTprFnYUHyB/GWXF9t7Jbv5IOJJ1e/GXuFxpv8wN6/+rYCJxT814cEBErJb0JeD8p0Y8nnWrawbPva5n+7yRd/6srIm4lvcf9/78/S9pxmZa3pYtr+huoCZIOKEwfTlrftTYCl9ashxdGRHHbO5J0DWafc2LZS3mP5QOS+veYDiN9KP8kV7kdeIOkwyWNI+/pNPA54IOSXpPvRZ8m6Yg69ZYBF0l6ce73LwvzbgUekXShpAMkjZJ0VL3bn/PpgxNJp3qOyX+/Byzi2dNhvwImSxpTaPor0vnkAfc5AO+S1J5POcwHlucjnF7SHurLmrYuyMn+etIFakh7ck8A2yW9hHRBtah2+b4BvFzSmZL2y3/HSjpS0hhJZ0gal0/rPUK6OL0vfAn4G0lTJR0EfJx0N1wf6Vz//krfydiPdGppbKFtcRuaBFwwgH6/CPyZpGMkjc39rsynd3pJCeZdeTt4N7vvLNXbngbiM8BHJB0JIGm8pP5TkgeTEusW0s7RJaQjloFYQeFUlqQ3SnpP/91Vud+38+z/74NJNzc8lue9bzALVfAC0k7QGKXvrZ1MOj1d60rg/LwdStJBkt6ekzw5OR0DfG8vxzMoTix771HSxbuVkh4jbXB3k/aKiYjvku5EuZN0gfsbjQJFxJdJ5/u/mON+jXQhstalpEPk9cB3gKWFGLtIG/4xef4WUsIaVyfOmcDtEfGdiHiw/490vv1oSUcBN5BORzwoaUtudxXpmsJ2SV8bYJ9lLSUd+T0I7A/8VV6+x0nr6Obc/x+UjPePwLn5A+JTpIvfW0jv17dr6v4zMEfSNkn/kq/DvIV0zWhzHtMinv2gPhPYkE+FnEe6u25fWEJaTz8grfcnyTsZEfEw8Bek96H/SKL4Jdr5eXo96cNnOSVvkY+I/yZd9/oK6cjnd9n9etp7SXcqPgRMB35cmFdveyot/x/5JPDlvL7vJN0UAikpfI90V90GUpJ/YIDxbwV2Suo/dbqNdLr4bkk7ch/LSBfzIf0/P4v0//WzpP/re2MT6b16gHRD0HvydZ7aca4k3W32b3mM/8Pu290pwHcj4le1bfeF/lsZzUYMSTeR7ur63HCP5flC0p+Tbm1tdhPD84KkPwLeHRFzWlautt83A5+LiCl7GUekW63PjIh7qxjbQP2mfsnKzJrI15teBtxCuqX2A6TvgDzvRcQKnr2Z5Tkn0tFCx3COwYnF7PlpDOnUzVTSnUOdpO89mO01nwozM7NK+eK9mZlV6nl5KuyQQw6JKVOmDPcwzMyeU2677bYtEdHyy9PPy8QyZcoUuru7W1c0M7NnSKr3iKk9+FSYmZlVyonFzMwqVSqxSJolaa2kHknz6swfK+naPH+lCk85lXRRLl8r6aRWMfPjKVZKui/HHNOsj/x4jasl3SXpXknNHpliZmZDrGViUXp43eWkZ9a0A6dLaq+pdg6wLSKmkX6IaFFu20561MN0YBZwRX5+ULOYi4DFEdFGelTBOc36ID1SemxEvIr0BNv3qdrHd5uZ2QCUOWKZAfTk33x4ivRFqtk1dWaTnmsD6ZlDJ+bHCswGOvNvC6wn/SjRjEYxc5uZPPvQtatJz7xp1kcAByr9BsUBpB/SeaT0GjAzs0qVSSyT2P03DDax5+8uPFMnP1n1YdJjtBu1bVQ+gfRzpX11+mrUx3KefWjbL4BP5B8m2o2kcyV1S+ru7e0tsdhmZjYYZRJLvd8WqP26fqM6VZU362MG6THlh5IeT/EBSXs8Uj0iroyIjojomDhxIL9hZWZmA1EmsWxi9x8BmsyePzzzTJ18SmocsLVJ20blW0i/5De6prxZH39K+qnPpyPif4GbGeYHsJmZPZ+VSSyrgLZ8t9YY0sX4rpo6XTz7w1BzgBvyEza7SL/BPVbSVNJTVG9tFDO3uZFnf0v8LOC6Fn38ApiZf+zmQOAPgJ+VXwVmZlallt+8j4g+SReQfoFvFLAkItZImg90R0QX6YeflkrqIR1FzM1t10haBtxD+mW38/t/57xezNzlhUCnpAXA6hybRn2Q7i77POnHtQR8PiLuHPQasRFhyrxvDvcQdrPhsrcO9xDMnjOel0837ujoCD/SZWRzYjEbeSTdFhEtLzX4m/dmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpVyYjEzs0o5sZiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVcqJxczMKuXEYmZmlXJiMTOzSjmxmJlZpZxYzMysUk4sZmZWqVKJRdIsSWsl9UiaV2f+WEnX5vkrJU0pzLsol6+VdFKrmJKm5hj35ZhjmvUh6QxJtxf+fi3pmMGuEDMz2zstE4ukUaTflT8ZaAdOl9ReU+0cYFtETAMWA4ty23bSb9NPB2YBV0ga1SLmImBxRLQB23Lshn1ExH9GxDERcQxwJrAhIm4f+KowM7MqlDlimQH0RMS6iHgK6ARm19SZDVydXy8HTpSkXN4ZETsjYj3Qk+PVjZnbzMwxyDFPadFH0enAl0osk5mZDZEyiWUSsLEwvSmX1a0TEX3Aw8CEJm0blU8AtucYtX016qPo/9AgsUg6V1K3pO7e3t4mi2tmZnujTGKpPSoAiJJ1qipvOQ5JxwGPR8TddeoREVdGREdEdEycOLFeFTMzq0CZxLIJOKwwPRnY3KiOpNHAOGBrk7aNyrcA43OM2r4a9dFvLj4NZmY27MokllVAW75bawzpA7yrpk4XcFZ+PQe4ISIil8/Nd3RNBdqAWxvFzG1uzDHIMa9r0QeSXgCcRrpWY2Zmw2h0qwoR0SfpAuB6YBSwJCLWSJoPdEdEF3AVsFRSD+koYm5uu0bSMuAeoA84PyJ2AdSLmbu8EOiUtABYnWPTqI/sDcCmiFg32BVhZmbVUN7pf17p6OiI7u7u4R6GNTFl3jeHewi72XDZW4d7CGbDTtJtEdHRqp6/eW9mZpVyYjEzs0o5sZiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVarlFyRtTyPpOxb+foXtDW/LNhScWJ4HRtKHB/gDxOw3nU+FmZlZpZxYzMysUk4sZmZWKScWMzOrlBOLmZlVyonFzMwq5cRiZmaVcmIxM7NK+QuSZhXxF1HNklJHLJJmSVorqUfSvDrzx0q6Ns9fKWlKYd5FuXytpJNaxZQ0Nce4L8ccU6KPoyXdImmNpLsk7T+YlWFmZnuvZWKRNAq4HDgZaAdOl9ReU+0cYFtETAMWA4ty23ZgLjAdmAVcIWlUi5iLgMUR0QZsy7Gb9TEauAY4LyKmAycATw9wPZiZWUXKHLHMAHoiYl1EPAV0ArNr6swGrs6vlwMnSlIu74yInRGxHujJ8erGzG1m5hjkmKe06OMtwJ0RcQdARDwUEbvKrwIzM6tSmcQyCdhYmN6Uy+rWiYg+4GFgQpO2jconANtzjNq+GvXxciAkXS/pp5I+VG8hJJ0rqVtSd29vb4nFNjOzwSiTWFSnLErWqaq8WR+jgdcBZ+R/T5V04h4VI66MiI6I6Jg4cWKdUGZmVoUyiWUTcFhhejKwuVGdfM1jHLC1SdtG5VuA8TlGbV/N+vh+RGyJiMeBFcCrSyyXmZkNgTK3G68C2iRNBX5Juhj/pzV1uoCzgFuAOcANERGSuoAvSvokcCjQBtxKOvrYI2Zuc2OO0ZljXteij+uBD0l6IfAU8EbSxX0z+w3jW7qfG1omlojok3QBcD0wClgSEWskzQe6I6ILuApYKqmHdBQxN7ddI2kZcA/QB5zff2G9Xszc5YVAp6QFwOocmyZ9bMuJaxXp1NiKiBhZW5+Z2fNIqS9IRsQK0immYtnFhddPAqc1aLsQWFgmZi5fR7prrLa8WR/XkG45NjOzYeZHupiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVcqJxczMKuXEYmZmlXJiMTOzSjmxmJlZpZxYzMysUk4sZmZWKScWMzOrlBOLmZlVyonFzMwq5cRiZmaVcmIxM7NKObGYmVmlSiUWSbMkrZXUI2lenfljJV2b56+UNKUw76JcvlbSSa1iSpqaY9yXY45p1oekKZKekHR7/vvMYFeGmZntvZaJRdIo4HLgZKAdOF1Se021c4BtETENWAwsym3bSb9NPx2YBVwhaVSLmIuAxRHRBmzLsRv2kf08Io7Jf+cNaA2YmVmlyhyxzAB6ImJdRDwFdAKza+rMBq7Or5cDJ0pSLu+MiJ0RsR7oyfHqxsxtZuYY5JintOjDzMxGkDKJZRKwsTC9KZfVrRMRfcDDwIQmbRuVTwC25xi1fTXqA2CqpNWSvi/p9fUWQtK5kroldff29pZYbDMzG4wyiaXeUUGUrFNVebM+HgAOj4jfB94PfFHSi/aoGHFlRHRERMfEiRPrhDIzsyqUSSybgMMK05OBzY3qSBoNjAO2NmnbqHwLMD7HqO2rbh/5NNtDABFxG/Bz4OUllsvMzIbA6NZVWAW0SZoK/JJ0Mf5Pa+p0AWcBtwBzgBsiIiR1kY4gPgkcCrQBt5KOPvaImdvcmGN05pjXtehjIinB7JL0stzHukGsCzOzyk2Z983hHsJuNlz21iHvo2ViiYg+SRcA1wOjgCURsUbSfKA7IrqAq4ClknpIRypzc9s1kpYB9wB9wPkRsQugXszc5YVAp6QFwOocm0Z9AG8A5kvqA3YB50XE1sGvEjMz2xtljliIiBXAipqyiwuvnwROa9B2IbCwTMxcvo5011hted0+IuIrwFdaLoSZme0T/ua9mZlVyonFzMwq5cRiZmaVcmIxM7NKObGYmVmlnFjMzKxSTixmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpVyYjEzs0o5sZiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVapUYpE0S9JaST2S5tWZP1bStXn+SklTCvMuyuVrJZ3UKqakqTnGfTnmmFZ95PmHS9oh6YMDXQlmZladlolF0ijgcuBkoB04XVJ7TbVzgG0RMQ1YDCzKbdtJv00/HZgFXCFpVIuYi4DFEdEGbMuxG/ZRsBj4VtkFNzOzoVHmiGUG0BMR6yLiKaATmF1TZzZwdX69HDhRknJ5Z0TsjIj1QE+OVzdmbjMzxyDHPKVFH0g6BVgHrCm/6GZmNhTKJJZJwMbC9KZcVrdORPQBDwMTmrRtVD4B2J5j1PZVtw9JBwIXApc2WwhJ50rqltTd29vbYpHNzGywyiQW1SmLknWqKm/Wx6WkU2c76sx/tmLElRHREREdEydObFbVzMz2wugSdTYBhxWmJwObG9TZJGk0MA7Y2qJtvfItwHhJo/NRSbF+oz6OA+ZI+gdgPPBrSU9GxKdLLJuZmVWszBHLKqAt3601hnQxvqumThdwVn49B7ghIiKXz813dE0F2oBbG8XMbW7MMcgxr2vWR0S8PiKmRMQU4FPAx51UzMyGT8sjlojok3QBcD0wClgSEWskzQe6I6ILuApYKqmHdBQxN7ddI2kZcA/QB5wfEbsA6sXMXV4IdEpaAKzOsWnUh5mZjSxlToURESuAFTVlFxdePwmc1qDtQmBhmZi5fB3prrHa8oZ9FOpc0my+mZkNPX/z3szMKuXEYmZmlXJiMTOzSjmxmJlZpZxYzMysUk4sZmZWKScWMzOrlBOLmZlVyonFzMwq5cRiZmaVcmIxM7NKObGYmVmlnFjMzKxSTixmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpUqlVgkzZK0VlKPpHl15o+VdG2ev1LSlMK8i3L5WkkntYopaWqOcV+OOaZZH5JmSLo9/90h6dTBrgwzM9t7LROLpFHA5cDJQDtwuqT2mmrnANsiYhqwGFiU27YDc4HpwCzgCkmjWsRcBCyOiDZgW47dsA/gbqAjIo7JfXxW0uiBrQYzM6tKmSOWGUBPRKyLiKeATmB2TZ3ZwNX59XLgREnK5Z0RsTMi1gM9OV7dmLnNzByDHPOUZn1ExOMR0ZfL9wei7MKbmVn1yiSWScDGwvSmXFa3Tv6QfxiY0KRto/IJwPZCoij21agPJB0naQ1wF3Beof0zJJ0rqVtSd29vb4nFNjOzwSiTWFSnrPaooFGdqsqbjiMiVkbEdOBY4CJJ++9RMeLKiOiIiI6JEyfWCWVmZlUok1g2AYcVpicDmxvVydc3xgFbm7RtVL4FGF+4RlLsq1Efz4iIe4HHgKNKLJeZmQ2BMollFdCW79YaQ7oY31VTpws4K7+eA9wQEZHL5+Y7uqYCbcCtjWLmNjfmGOSY1zXrI8cYDSDpCOAVwIbSa8DMzCrV8u6piOiTdAFwPTAKWBIRayTNB7ojogu4ClgqqYd0FDE3t10jaRlwD9AHnB8RuwDqxcxdXgh0SloArM6xadQH8DpgnqSngV8DfxERWwa/SszMbG+Uui03IlYAK2rKLi68fhI4rUHbhcDCMjFz+TrSXWO15XX7iIilwNKWC2FmZvuEv3lvZmaVcmIxM7NKObGYmVmlnFjMzKxSTixmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpVyYjEzs0o5sZiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVcqJxczMKuXEYmZmlSqVWCTNkrRWUo+keXXmj5V0bZ6/UtKUwryLcvlaSSe1ipl/w36lpPtyzDHN+pD0h5Juk3RX/nfmYFeGmZntvZaJRdIo4HLgZKAdOF1Se021c4BtETENWAwsym3bSb9NPx2YBVwhaVSLmIuAxRHRBmzLsRv2AWwB3h4RrwLOwj9TbGY2rMocscwAeiJiXUQ8BXQCs2vqzAauzq+XAydKUi7vjIidEbEe6Mnx6sbMbWbmGOSYpzTrIyJWR8TmXL4G2F/S2LIrwMzMqlUmsUwCNhamN+WyunUiog94GJjQpG2j8gnA9hyjtq9GfRS9A1gdETtrF0LSuZK6JXX39va2WGQzMxusMolFdcqiZJ2qyluOQ9J00umx99WpR0RcGREdEdExceLEelXMzKwCZRLLJuCwwvRkYHOjOpJGA+OArU3aNirfAozPMWr7atQHkiYDXwX+b0T8vMQymZnZECmTWFYBbflurTGki/FdNXW6SBfOAeYAN0RE5PK5+Y6uqUAbcGujmLnNjTkGOeZ1zfqQNB74JnBRRNw8kIU3M7PqtUws+XrGBcD1wL3AsohYI2m+pD/O1a4CJkjqAd4PzMtt1wDLgHuAbwPnR8SuRjFzrAuB9+dYE3Lshn3kONOAj0q6Pf+9dJDrw8zM9tLo1lUgIlYAK2rKLi68fhI4rUHbhcDCMjFz+TrSXWO15XX7iIgFwIKWC2FmZvuEv3lvZmaVcmIxM7NKObGYmVmlnFjMzKxSTixmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpVyYjEzs0o5sZiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVcqJxczMKuXEYmZmlSqVWCTNkrRWUo+keXXmj5V0bZ6/UtKUwryLcvlaSSe1iilpao5xX445plkfkiZIulHSDkmfHuyKMDOzarRMLJJGAZcDJwPtwOmS2muqnQNsi4hpwGJgUW7bDswFpgOzgCskjWoRcxGwOCLagG05dsM+gCeBjwIfHOCym5nZEChzxDID6ImIdRHxFNAJzK6pMxu4Or9eDpwoSbm8MyJ2RsR6oCfHqxszt5mZY5BjntKsj4h4LCJ+REowZmY2zMoklknAxsL0plxWt05E9AEPAxOatG1UPgHYnmPU9tWoj1IknSupW1J3b29v2WZmZjZAZRKL6pRFyTpVlZcdR0MRcWVEdEREx8SJE8s2MzOzASqTWDYBhxWmJwObG9WRNBoYB2xt0rZR+RZgfI5R21ejPszMbAQpk1hWAW35bq0xpIvxXTV1uoCz8us5wA0REbl8br6jayrQBtzaKGZuc2OOQY55XYs+zMxsBBndqkJE9Em6ALgeGAUsiYg1kuYD3RHRBVwFLJXUQzqKmJvbrpG0DLgH6APOj4hdAPVi5i4vBDolLQBW59g06iPH2gC8CBgj6RTgLRFxz2BXipmZDV7LxAIQESuAFTVlFxdePwmc1qDtQmBhmZi5fB3prrHa8mYoXf7wAAAHjElEQVR9TGm6AGZmts/4m/dmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpVyYjEzs0o5sZiZWaWcWMzMrFJOLGZmViknFjMzq5QTi5mZVcqJxczMKuXEYmZmlXJiMTOzSjmxmJlZpZxYzMysUk4sZmZWKScWMzOrVKnEImmWpLWSeiTNqzN/rKRr8/yVkqYU5l2Uy9dKOqlVTElTc4z7cswxg+3DzMz2vZaJRdIo4HLgZKAdOF1Se021c4BtETENWAwsym3bgbnAdGAWcIWkUS1iLgIWR0QbsC3HHnAfA10RZmZWjTJHLDOAnohYFxFPAZ3A7Jo6s4Gr8+vlwImSlMs7I2JnRKwHenK8ujFzm5k5BjnmKYPsw8zMhsHoEnUmARsL05uA4xrViYg+SQ8DE3L5T2raTsqv68WcAGyPiL469QfTxzMknQucmyd3SFrbeJH3iUOALXsbRIsqGEl5HvO+8Vwb83NtvOAxD9YRZSqVSSyqUxYl6zQqr3ek1Kz+YPrYvSDiSuDKOnWHhaTuiOgY7nEMhMe8bzzXxvxcGy94zEOtzKmwTcBhhenJwOZGdSSNBsYBW5u0bVS+BRifY9T2NdA+zMxsGJRJLKuAtny31hjShfKumjpdwFn59RzghoiIXD4339E1FWgDbm0UM7e5Mccgx7xukH2YmdkwaHkqLF/PuAC4HhgFLImINZLmA90R0QVcBSyV1EM6ipib266RtAy4B+gDzo+IXQD1YuYuLwQ6JS0AVufYDKaPEW7EnJYbAI9533iujfm5Nl7wmIeU0k6/mZlZNfzNezMzq5QTi5mZVcqJZQhICklLC9OjJfVK+sZwjquWpAmSbs9/D0r6ZWF6zHCPrxFJiyX9dWH6ekmfK0z/k6T3l4x1iaQPDsU4C300Ws/bJd0zlH1XTdKuwrLcXny0UqHOoZKW79l635P0EUlrJN2Zx1v7Hbxi3bMlHbovx1fTf+mxjnRlvsdiA/cYcJSkAyLiCeAPgV8O85j2EBEPAcdA+oAFdkTEJ4Z1UOX8GDgN+JSkF5C+OPaiwvzXAn9dr+FwaLSe84fyoHc2JI0ufJl4X3kiIo5pNDOPaTPP3tk5bCQdD7wNeHVE7JR0CNBsh+ls4G6G4esKgxjriOYjlqHzLeCt+fXpwJf6Z0h6iaSv5T2Tn0g6OpdfImmJpJskrZP0V8MwbiRNk3R7YXqepL/Lr9vyEcJtkn4g6eW5fK6kuyXdIenGIR7izaTkAekZcXcDj0p6saSxwJHAakl/K2lVXs+XFpbnI0oPLP0e8IohHmsroyT9e95T/Y6kA/IYb5LUkV8fImlDfn22pC9L+jrwneEb9rNqxyRpiqS7h3tcwO8AWyJiJ0BEbImIzZIuztvF3ZKuVDIH6AD+Mx8tHDBCxrohJxkkdUi6Kb8eEZ8VjTixDJ1O0vdr9geOBlYW5l0KrI6Io4EPA18ozHslcBLpeWcfk7TfPhpvWVcCfxERrwEuAj6dyz8GnBgRvwecOpQDyHvEfZIOJyWYW0jr93jSh8OdwAmk7zTNIB0tvEbSGyS9hnSr+u8DfwIcO5RjLaENuDwipgPbgXeUaHM8cFZEzBzSkdV3QOE02FdHyJga+Q5wmKT/kXSFpDfm8k9HxLERcRRwAPC2iFgOdANnRMQx+UzDSBhrMyP2s8KnwoZIRNyZT3WcDqyomf068gdIRNyQz8GPy/O+mfdadkr6X+C3SE8XGHaSxgN/AHxFeuZJOv3b0M3AFyR9GfivfTCc/qOW1wKfJD0f7rXAw6RTZW/Jf6tz/YNIH+IHA1+NiMcBJNV+2XdfWx8R/UeHtwFTSrT5bkRsHbohNdXoVNhwjqmuiNiRdyReD7wJuFbpJzoelfQh4IXAS4A1wNeHb6RNx9rMiP2scGIZWl3AJ0h7zxMK5c2eb7azULaL4XmP+tj9aHb/XCbS4Xq9D5b3kh4k+jbgDklHR8S2IRzjj0mJ5FWkU2EbgQ8AjwBLSOv87yPis8VGShf9R9KXt2rf7/5TMMX3YP+aNo8N9aAGYSSOifxl6ZuAmyTdBbyPdAahIyI25mtetet3WNQZ61k03w5GwmdFXT4VNrSWAPMj4q6a8h8AZwBIOoH0Yf3IPh5bMw8Ch+ZrFvuTrxXlRPGApFMBJL1A0u/lNi+LiJ8AHyX9js4eT5iu2M2kJLY1InblveXxpFMyt5Ce6vBuSQflsU6S9FLSuj9V0gGSDgbePsTjHKwNwGvy62G/EP5cJOkVktoKRccA/U8135K3jeK6fZR0RLvPNRjr/ey+HZQ5TToijJgM95soIjYB/1xn1iXA5yXdCTzOs89AGxEi4klJHyc9020d6XE5/eYC/5b39MYA1wB3AIuVntUm4DsRMdQXb+8i3Q32xZqygyJiC+ki8pHALfm03Q7gXRHxU0nXAreT/uP+cIjHOVifAJZJOhO4YbgH8xx1EPCv+RRuH+m3ms4lXcu6i/ShvapQ/z+Az0h6Ajh+H19naTTWI4GrJH2Y3a/Tjmh+pIuZmVXKp8LMzKxSTixmZlYpJxYzM6uUE4uZmVXKicXMzCrlxGJmZpVyYjEzs0r9f0IpWIoWQsrAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([\"Mon\", \"Tues\", \"Wed\", \"Thur\", \"Fri\", \"Sat\", \"Sun\"], weekdayRates)\n",
    "plt.title(\"Suicide Attempt Rates Throughout Year (Sample)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further back up our hypothesis, we decided to look at the attempts throughout the week. If our hypothesis is true, we should see a decline during the weekends, times when students are less stressed. We see a weak possibility. Perhaps this is due to using the entire year instead during active academic sessions. As such, we must get rid of the data that belong to the inactive academic times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fallRates = []\n",
    "winterRates = []\n",
    "springRates = []\n",
    "for i in range(len(startFall)):\n",
    "    fallRates.append(attemptRates[(startFall[i] <= attemptRates.index) & (attemptRates.index <= endFall[i])])\n",
    "    winterRates.append(attemptRates[(startFall[i] <= attemptRates.index) & (attemptRates.index <= endFall[i])])\n",
    "    springRates.append(attemptRates[(startFall[i] <= attemptRates.index) & (attemptRates.index <= endFall[i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = fallRates + winterRates + springRates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday = pd.Series()\n",
    "for quarter in total:\n",
    "    weekday = weekday.append(quarter)\n",
    "weekday.index = list(map(lambda x: x.weekday(), weekday.index))\n",
    "weekdayRates = weekday.groupby(weekday.index).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([\"Mon\", \"Tues\", \"Wed\", \"Thur\", \"Fri\", \"Sat\", \"Sun\"], weekdayRates)\n",
    "plt.title(\"Suicide Attempt Rates During Academic Quarter (Sample)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a much stronger trend seen here. According to our data, a student is roughly twice as likely to attempt a suicide on a Tuesday then Sunday. Weekends are a bit lower in the attempt rates then weekdays, again presumably from stress."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting Future Attempts by Quarter Weekage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple forecast of the next years attempt as according to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "modDF = pd.DataFrame(attemptRates)\n",
    "modDF[\"ds\"] = modDF.index\n",
    "modDF.rename(columns={\"Date Occurred\": \"y\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/Users/nicklin/anaconda3/lib/python3.6/site-packages/pystan/misc.py:399: FutureWarning:\n",
      "\n",
      "Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = Prophet()\n",
    "m.fit(modDF)\n",
    "future = m.make_future_dataframe(periods=365)\n",
    "forecast = m.predict(future)\n",
    "fig1 = m.plot(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The years follow a pattern as seen in the monthly distributions. The statochato comes from the increasing numb er of students, decreasing the ratage per attempt because of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethics and Privacy\n",
    " \n",
    "Our topic of investigation entails rather sensitive and personal information. Mental health and suicide, while being a prominent issue that requires attention and care, is still confidential information that should only be disclosed should the person affected wish to share such information. In the crime logs, names and information are listed when an arrest is made, however Nick took the liberty of removing all mentions of names and personal information when cleaning and processing the data. It will be difficult to identify any individual information, as any identifiable information is removed. If this information were to accidentally be released to the public it would seriously jeapordize the personal lives of those involved. While the privacy of survivors is obviously of utmost concern, we are also committed to the protection of recently discharged hospital patients. Studies have shown that almost half of the patients who commit suicide do so within 90 days of being discharged from care. Though it is ultimately the patient's decision regarding what care they wish to receive, it could be beneficial if hospital staff were alerted about this sensitive period and recommended patients to continue to be monitored for a brief period after leaving intensive care.\n",
    "\n",
    "Another ethical concern regards whether by conducting this research, should the dataset collected be useful in lowering and helping prevent suicide/self harm attempts in the future, if it thereby becomes our personal responsibility to ensure that the data is used and the appropriate steps be taken to ensure that action is taken. Though we have exempted ourselves from this responsibility we still uphold the belief that should the data be beneficial, we will put our wholehearted effort into making sure it is used for good. The data is unlikely to be biased as it is provided by the UCPD, which primarily serves the UCSD community more so than the greater San Diego Community. We will ensure that all the information gathered from the police logs have a correlation to the UC system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions / Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "College students undergo a considerable amount of stress, balancing different aspects of classes, work, student organization, internships, and personal strains. Students are pressured and strained to completely devote their lives to academics, leaving no room for personal care nor leisure time to destress. Large amounts of students develop problems related to mental health, such as issues with depression, anxiety, and isolation. According to the data collected, we can conclude that the quarter system and exams coincide with suicide attempts by students. However it was not the linear increase which we had predicted. The quarter that had the most attempts was Spring quarter, with peaks during weeks 3, 6, and 10, most likely due to midterms and final exams. According to a UCSD CAPS report from 2017, over 57% of students seen at CAPS are female and 33% are first generation students, an important topic to note because of the lack of diversity of people of color, seeing as less than 20% of all UCSD undergraduates are of African-American and Latino descent.\n",
    "\n",
    " \n",
    "There are very important factors that come into play such as academic competition, place of residence, socio-intellectual factors, and financial pressure among other stressors. This problem is not soley reliant on academic struggles but additional factors that are driving students to attempt and commit suicide when placed under this considerable amount of stress. Minorities might feel isolated and alone especially if they are first generation college students that don't know how to naviigate college life and appreciate it to it's full extent. Our research shows that students attempt suicide more during the time of exams and during the spring quarter, but it fails to show the statistics as to how many of those students are minorities and first generation students. Students who are deeply struggling in a system that they had no prior knowledge to."
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}