heart_rate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro\n",
    "Analysis of daily heart rate data over first 2 months of fitbit use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "import matplotlib.patches as mpatch\n",
    "import numpy as np\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data preview: \n",
      "        date  restingHeartRate  normal_max  normal_min  normal_minutes  \\\n",
      "0 2022-04-21                58         107          30            1396   \n",
      "1 2022-04-22                58         107          30            1437   \n",
      "2 2022-04-23                58         107          30            1405   \n",
      "3 2022-04-24                58         106          30            1439   \n",
      "4 2022-04-25                58         107          30            1411   \n",
      "\n",
      "   fatBurn_max  fatBurn_min  fatBurn_minutes  cardio_max  cardio_min  \\\n",
      "0          132          107               42         163         132   \n",
      "1          132          107                3         163         132   \n",
      "2          132          107               35         163         132   \n",
      "3          131          106                1         163         131   \n",
      "4          132          107               28         163         132   \n",
      "\n",
      "   cardio_minutes  weekDay   dayName  \n",
      "0               2        3  Thursday  \n",
      "1               0        4    Friday  \n",
      "2               0        5  Saturday  \n",
      "3               0        6    Sunday  \n",
      "4               1        0    Monday  \n",
      "\n",
      "Check datatypes: \n",
      "date                datetime64[ns]\n",
      "restingHeartRate             int64\n",
      "normal_max                   int64\n",
      "normal_min                   int64\n",
      "normal_minutes               int64\n",
      "fatBurn_max                  int64\n",
      "fatBurn_min                  int64\n",
      "fatBurn_minutes              int64\n",
      "cardio_max                   int64\n",
      "cardio_min                   int64\n",
      "cardio_minutes               int64\n",
      "weekDay                      int64\n",
      "dayName                     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# import data\n",
    "allHeartDF = pd.read_csv('working/allHeart.csv', index_col=0)\n",
    "\n",
    "# convert datatype\n",
    "allHeartDF['date'] = pd.to_datetime(allHeartDF['date'], format='%Y-%m-%d')\n",
    "\n",
    "# add day of week (0 = Monday, 6 = Sunday)\n",
    "allHeartDF['weekDay'] = allHeartDF['date'].dt.dayofweek\n",
    "\n",
    "# add day name\n",
    "allHeartDF['dayName'] = allHeartDF['date'].dt.day_name()\n",
    "\n",
    "# rename columsnas all min and max inverted - will amend api script but don't want to rerun now\n",
    "allHeartDF.rename(columns={'normal_min': 'normal_max', 'normal_max': 'normal_min', 'fatBurn_min': 'fatBurn_max', 'fatBurn_max': 'fatBurn_min', 'cardio_min': 'cardio_max', 'cardio_max': 'cardio_min'}, inplace=True)\n",
    "\n",
    "# preview data and confirm datatypes\n",
    "print('Data preview: \\n' \n",
    "        +str(allHeartDF.head()) +'\\n'\n",
    "        +'\\n'\n",
    "        'Check datatypes: \\n' \n",
    "        +str(allHeartDF.dtypes))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data explanation\n",
    "This is daily data, meaning there is adate and single data entry for each day, always in bpm (beats per minute). Note that date format is yyyy-mm-dd (ie year, month, day). \n",
    "\n",
    "Firstly, there are two heart rate parameters Fitbit calculates that aren't shown here:\n",
    "Maximum heart rate is commonly defined as \"220 - age\", in my case \"220-38 = 182\"\n",
    "Heart rate reserve is the difference between resting heart rate and max heart rate, in my case \"182 - 58 = 124\". \n",
    "\n",
    "Secondly, Fitbit determines resting heart rate (restingHeartRate in the dataframe) by recording heart rate when asleep or awake but inactive (no steps detected) according to a proprietary algorithm. Resting heart rates are generally 60 - 100 bpm, with lower resting heart rates indicative of good heart health.\n",
    "\n",
    "Then there are 3 heart rate zones:  \n",
    "\n",
    "normal = Fitbit calls this \"below zones\" or \"out of range\", by which it means \"not in a fat burn or cardio zone\". I have called it \"normal\" as it is typical heart rate when going about day-to-day activities.  \n",
    "\n",
    "fatBurn = the first training zone, defined as 40-59% of heart rate reserve + resting heart rate, that is:  \n",
    "&emsp; fat burn zone bottom = $(0.4 * heart\\ rate\\ reserve) + resting\\ heart\\ rate$  \n",
    "&emsp; fat burn zone top = $(0.59 * heart\\ rate\\ reserve) + resting\\ heart\\ rate$  \n",
    "\n",
    "cardio = the second training zone, 60-84% of heart rate reserve + resting heart rate, that is:  \n",
    "&emsp; cardio zone bottom = $(0.6 * heart\\ rate\\ reserve) + resting\\ heart\\ rate$  \n",
    "&emsp; cardio zone top = $(0.84 * heart\\ rate\\ reserve) + resting\\ heart\\ rate$ \n",
    "\n",
    "peak = peak training zone, defined as 85% of heart rate reserve + resting heart rate to max heart rate, that is:   \n",
    "&emsp; peak zone bottom = $(0.85 * heart\\ rate\\ reserve) + resting\\ heart\\ rate$  \n",
    "&emsp; peak zone top = max heart rate\n",
    "\n",
    "\n",
    "See [here](https://help.fitbit.com/articles/en_US/Help_article/1565.htm) for more info on Fitbit heart rate zones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    My heart rate zones are: \n",
      "    Maximum Heart Rate: 182 \n",
      "    Heart Rate Reserve: 124 \n",
      "    Resting Heart Rate: 58 \n",
      "    Normal Zone: 30 - 107 \n",
      "    Fat Burn Zone: 108 - 131 \n",
      "    Cardio Zone: 132 - 162 \n",
      "    Peak Zone: 163 - 182 \n"
     ]
    }
   ],
   "source": [
    "# my heart rate zone calculations, according to fitbit\n",
    "\n",
    "age = 38\n",
    "restingHeartRate = allHeartDF['restingHeartRate'].mean()\n",
    "\n",
    "maxHeartRate = 220 - age # commonly defined as \"220 - age\"\n",
    "heartRateReserve = int(maxHeartRate - restingHeartRate)\n",
    " \n",
    "# normal zone boundaries - its not clear how Fitbit calculated this, so I've used their values\n",
    "normalZoneBottom = allHeartDF['normal_min'][0]\n",
    "normalZoneTop = allHeartDF['normal_max'][0]\n",
    "\n",
    "# fat burn zone boundaries\n",
    "fatBurnZoneBottom = (0.4 * heartRateReserve) + restingHeartRate\n",
    "fatBurnZoneTop = (0.59 * heartRateReserve) + restingHeartRate\n",
    "\n",
    "# cardio zone boundaries\n",
    "cardioZoneBottom = (0.6 * heartRateReserve) + restingHeartRate\n",
    "cardioZoneTop = (0.84 * heartRateReserve) + restingHeartRate\n",
    "\n",
    "# peak zone\n",
    "peakZoneBottom = (0.85 * heartRateReserve) + restingHeartRate\n",
    "peakZoneTop = maxHeartRate\n",
    "\n",
    "# print\n",
    "print(f\"\\\n",
    "    My heart rate zones are: \\n\\\n",
    "    Maximum Heart Rate: {maxHeartRate} \\n\\\n",
    "    Heart Rate Reserve: {heartRateReserve} \\n\\\n",
    "    Resting Heart Rate: {restingHeartRate:.0f} \\n\\\n",
    "    Normal Zone: {normalZoneBottom:.0f} - {normalZoneTop:.0f} \\n\\\n",
    "    Fat Burn Zone: {fatBurnZoneBottom:.0f} - {fatBurnZoneTop:.0f} \\n\\\n",
    "    Cardio Zone: {cardioZoneBottom:.0f} - {cardioZoneTop:.0f} \\n\\\n",
    "    Peak Zone: {peakZoneBottom:.0f} - {peakZoneTop:.0f} \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plots of my hert rate zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot as stacked bar\n",
    "# create figure\n",
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "\n",
    "xlabels = [\"Normal\", \"Fat Burn\", \"Cardio\", \"Peak\"]\n",
    "\n",
    "# calculate zone sizes\n",
    "# these are needed to plot one above the other in stacked bar chart\n",
    "normalZoneSize = normalZoneTop - normalZoneBottom\n",
    "fatBurnZoneSize = fatBurnZoneTop - fatBurnZoneBottom\n",
    "cardioZoneSize = cardioZoneTop - cardioZoneBottom\n",
    "peakZoneSize = peakZoneTop - peakZoneBottom\n",
    "\n",
    "# add plots\n",
    "plt.bar(xlabels[0], normalZoneSize, bottom=normalZoneBottom)\n",
    "plt.bar(xlabels[1], fatBurnZoneSize, bottom=fatBurnZoneBottom)\n",
    "plt.bar(xlabels[2], cardioZoneSize, bottom=cardioZoneBottom)\n",
    "plt.bar(xlabels[3], peakZoneSize, bottom=peakZoneBottom)\n",
    "\n",
    "# amend y axis\n",
    "plt.ylim(0, 220)\n",
    "\n",
    "# add lines\n",
    "plt.axhline(y=restingHeartRate, color='#E1BE6A', linestyle='--', linewidth=1.5, label=\"Resting heart rate\")\n",
    "plt.axhline(y=maxHeartRate, color='#DC3220', linestyle='--', linewidth=1.5, label=\"Maximum heart rate\")\n",
    "\n",
    "# labels\n",
    "plt.ylabel(\"Beats per min\")\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot as stacked bar\n",
    "# create figure\n",
    "fig, ax = plt.subplots(figsize=(3, 6))\n",
    "\n",
    "xlabel = [\"Zones\"]\n",
    "\n",
    "# calculate zone sizes\n",
    "# these are needed to plot one above the other in stacked bar chart\n",
    "normalZoneSize = normalZoneTop - normalZoneBottom\n",
    "fatBurnZoneSize = fatBurnZoneTop - fatBurnZoneBottom\n",
    "cardioZoneSize = cardioZoneTop - cardioZoneBottom\n",
    "peakZoneSize = peakZoneTop - peakZoneBottom\n",
    "# zone half size list - needed for plot annotation later on\n",
    "zoneHalfSizes = [(normalZoneTop-(normalZoneSize/2)), \n",
    "                (fatBurnZoneTop-(fatBurnZoneSize/2)), \n",
    "                (cardioZoneTop-(cardioZoneSize/2)), \n",
    "                (peakZoneTop-(peakZoneSize/2))]\n",
    "\n",
    "# add plots\n",
    "plt.bar(xlabel, normalZoneSize, bottom=normalZoneBottom) \n",
    "plt.bar(xlabel, fatBurnZoneSize, bottom=fatBurnZoneBottom)\n",
    "plt.bar(xlabel, cardioZoneSize, bottom=cardioZoneBottom)\n",
    "plt.bar(xlabel, peakZoneSize, bottom=peakZoneBottom)\n",
    "\n",
    "# amend y axis\n",
    "plt.ylim(0, 220)\n",
    "\n",
    "# add lines\n",
    "plt.axhline(y=restingHeartRate, color='#E1BE6A', linestyle='--', linewidth=1.5, label=\"Resting heart rate\")\n",
    "plt.axhline(y=maxHeartRate, color='#DC3220', linestyle='--', linewidth=1.5, label=\"Maximum heart rate\")\n",
    "\n",
    "# labels\n",
    "plt.ylabel(\"Beats per min\")\n",
    "\n",
    "barLabels = [\"Normal\", \"Fat Burn\", \"Cardio\", \"Peak\"]\n",
    "hLineLabels = [\"Resting Heart Rate\", \"Maximum Heart Rate\"]\n",
    "for i in range(len(barLabels)):\n",
    "    plt.annotate(barLabels[i], (xlabel, zoneHalfSizes[i]), ha='center', bbox={'facecolor': 'white', 'alpha': 0.4, 'edgecolor': 'none', 'pad': 2})\n",
    "plt.legend()\n",
    "\n",
    "# display\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots of time in each zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>date</th>\n",
       "      <th>weekDay</th>\n",
       "      <th>dayName</th>\n",
       "      <th>normal_minutes</th>\n",
       "      <th>fatBurn_minutes</th>\n",
       "      <th>cardio_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-21</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1396</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-22</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1437</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-23</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1405</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-24</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1439</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-25</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1411</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  weekDay   dayName  normal_minutes  fatBurn_minutes  \\\n",
       "0 2022-04-21        3  Thursday            1396               42   \n",
       "1 2022-04-22        4    Friday            1437                3   \n",
       "2 2022-04-23        5  Saturday            1405               35   \n",
       "3 2022-04-24        6    Sunday            1439                1   \n",
       "4 2022-04-25        0    Monday            1411               28   \n",
       "\n",
       "   cardio_minutes  \n",
       "0               2  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gather data\n",
    "\n",
    "zoneTimeDF = allHeartDF[['date', 'weekDay', 'dayName', 'normal_minutes', 'fatBurn_minutes', 'cardio_minutes']]\n",
    "zoneTimeDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>dayName</th>\n",
       "      <th colspan=\"3\" halign=\"left\">normal_minutes</th>\n",
       "      <th colspan=\"3\" halign=\"left\">fatBurn_minutes</th>\n",
       "      <th colspan=\"3\" halign=\"left\">cardio_minutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>1421.444444</td>\n",
       "      <td>1439</td>\n",
       "      <td>1382</td>\n",
       "      <td>18.111111</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>1418.250000</td>\n",
       "      <td>1435</td>\n",
       "      <td>1401</td>\n",
       "      <td>21.375000</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>1422.888889</td>\n",
       "      <td>1440</td>\n",
       "      <td>1405</td>\n",
       "      <td>17.111111</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1439</td>\n",
       "      <td>1392</td>\n",
       "      <td>18.888889</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>1406.666667</td>\n",
       "      <td>1429</td>\n",
       "      <td>1365</td>\n",
       "      <td>32.777778</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1404.375000</td>\n",
       "      <td>1424</td>\n",
       "      <td>1377</td>\n",
       "      <td>34.125000</td>\n",
       "      <td>63</td>\n",
       "      <td>16</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1415.375000</td>\n",
       "      <td>1436</td>\n",
       "      <td>1380</td>\n",
       "      <td>23.625000</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dayName normal_minutes             fatBurn_minutes          \\\n",
       "                       mean   max   min            mean max min   \n",
       "0     Friday    1421.444444  1439  1382       18.111111  57   1   \n",
       "1     Monday    1418.250000  1435  1401       21.375000  37   5   \n",
       "2   Saturday    1422.888889  1440  1405       17.111111  35   0   \n",
       "3     Sunday    1421.000000  1439  1392       18.888889  48   1   \n",
       "4   Thursday    1406.666667  1429  1365       32.777778  74  11   \n",
       "5    Tuesday    1404.375000  1424  1377       34.125000  63  16   \n",
       "6  Wednesday    1415.375000  1436  1380       23.625000  57   4   \n",
       "\n",
       "  cardio_minutes          \n",
       "            mean max min  \n",
       "0       0.444444   2   0  \n",
       "1       0.375000   2   0  \n",
       "2       0.000000   0   0  \n",
       "3       0.111111   1   0  \n",
       "4       0.555556   2   0  \n",
       "5       1.500000   8   0  \n",
       "6       0.875000   5   0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gropu by weekday and calc mean, min and max for all zones\n",
    "zoneTimeGroupedByDay = zoneTimeDF.groupby('dayName').agg({'normal_minutes': ['mean', 'max', 'min'], 'fatBurn_minutes': ['mean', 'max', 'min'], 'cardio_minutes': ['mean', 'max', 'min']}).reset_index()\n",
    "\n",
    "# rename columns\n",
    "#zoneTimeGroupedByDay.columns['weekDay', 'normal_mean', 'normal_max', 'normal_min', 'fatBurn_mean', 'fatBurn_max', 'fatBurn_min', 'cardio_mean', 'cardio_max', 'cardio_min']\n",
    "\n",
    "zoneTimeGroupedByDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Friday'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoneTimeGroupedByDay.iloc[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot fatburn and cardio minutes by day\n",
    "\n",
    "fix, ax = plt.subplots(figsize=(4, 6))\n",
    "\n",
    "# plot\n",
    "plt.bar(zoneTimeGroupedByDay['dayName'], zoneTimeGroupedByDay['fatBurn_minutes']['mean'])\n",
    "plt.bar(zoneTimeGroupedByDay['dayName'], zoneTimeGroupedByDay['cardio_minutes']['mean'])\n",
    "\n",
    "# add target line\n",
    "plt.axhline(y=22, color='#E1BE6A', linestyle='--', linewidth=1.5, label=\"Zone mins target\")\n",
    "\n",
    "# labels\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90.0)\n",
    "plt.ylabel(\"Active Zone Minutes\")\n",
    "plt.title(\"Average Active Zone Minutes Per Day\")\n",
    "# display\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weekly target met plot (zone mins)\n",
    "Fitbit sets a default goal of 22 Active Zone Minutes per day.\n",
    "1 min in Fat Burn Zone earns 1 Active Zone Minutes\n",
    "2 mins in Cardio or Peak Zones earns 2 Active Zone Minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 1, 0, 0, 0, 1],\n",
       "       [1, 0, 0, 1, 1, 1, 0]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calc active zone minutes and append to DF\n",
    "\n",
    "allHeartDF['zone_mins'] = allHeartDF['fatBurn_minutes'] + (allHeartDF['cardio_minutes'] *2)\n",
    "\n",
    "# create categorical dataset\n",
    "zoneMinsTarget = 22\n",
    "zoneMinsTargetMet = (allHeartDF['zone_mins'].tail(7) > zoneMinsTarget).tolist()\n",
    "# create a [2,7] array of zeroes\n",
    "zoneMinsTargetMetArray = np.zeros([2, 7], dtype=int)\n",
    "\n",
    "# populate with 1/0\n",
    "for i in range(len(zoneMinsTargetMet)):\n",
    "    if zoneMinsTargetMet[i] == True:\n",
    "        zoneMinsTargetMetArray[0][i] +=1\n",
    "    else:\n",
    "        zoneMinsTargetMetArray[1][i] +=1\n",
    "\n",
    "zoneMinsTargetMetArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list out last 7 days\n",
    "heatMapLabels = allHeartDF['dayName'].tail(7).tolist()\n",
    "\n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set x ticklabels as days, rotate, right align\n",
    "ax.set_xticks(np.arange(len(heatMapLabels)), labels=heatMapLabels, rotation=45.0, ha=\"right\", rotation_mode=\"anchor\")\n",
    "# hide tick markers\n",
    "ax.tick_params(which=\"major\", bottom=False, left=False)\n",
    "\n",
    "# from matplotlib import colors\n",
    "im = ax.imshow(zoneMinsTargetMetArray, cmap='autumn')\n",
    "\n",
    "# set y ticks as Yes / No\n",
    "yLabels = [\"Target met\", \"Target not met\"]\n",
    "ax.set_yticks(np.arange(len(yLabels)), labels=yLabels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 ('.fitenv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "be045aa2e5b8a6045b5a2c43a3fede2d65f53664ebbe9ed391d2596ba080a904"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}