mobility-report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apple Health Mobility Report\n",
    "\n",
    "Our goal is to build a report with the Apple Health data that can be used by Physical Therapists to assess the progress of their patients. To do this, we selected some exercise metrics that we believe have an impact in maintaining or improving the mobility of people affected by Multiple Sclerosis (MS) or stroke.\n",
    "\n",
    "1. Distance Cycling\n",
    "2. Double support time\n",
    "3. Exercise minutes\n",
    "4. Six minute walk\n",
    "5. Stair speed down\n",
    "6. Stair speed up\n",
    "7. Step length\n",
    "8. Walking and running distance\n",
    "9. Walking assymetry\n",
    "10. Walking speed\n",
    "11. Walking steadiness (last 4 weeks)\n",
    "\n",
    "These metrics are a part of [Apple's Healthkit](https://developer.apple.com/documentation/healthkit) and the user can access them in individual charts. There is no native option on the iPhone and looking at the metrics in separate charts makes it harder to find correlations or justify discrepancies in the data.\n",
    "\n",
    "This report aims to gather the above metrics in a way that can be analysed further and be more useful to understand the patient's progress. \n",
    "\n",
    "To do this we will use the full export of the Apple Health App and [convert it to a Pandas Dataframe using Milind Ganjoo's Apple Health Exporter code](https://github.com/mganjoo/apple-health-exporter).\n",
    "\n",
    "## Getting the user data to display\n",
    "\n",
    "1. Export Apple Health data from within the Health app.\n",
    "\n",
    "![click on \"user\" icon and then on \"export health data\"](images/exporting.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import os \n",
    "import sys \n",
    "\n",
    "!{sys.executable} -m pip install --upgrade pip\n",
    "!{sys.executable} -m pip install pandas pyarrow\n",
    "!{sys.executable} -m pip install matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proceeding with default values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: apple-health-exporter in ./env/lib/python3.9/site-packages (1.0.2)\n",
      "Requirement already satisfied: pyarrow<8.0.0,>=7.0.0 in ./env/lib/python3.9/site-packages (from apple-health-exporter) (7.0.0)\n",
      "Requirement already satisfied: pandas<2.0.0,>=1.4.1 in ./env/lib/python3.9/site-packages (from apple-health-exporter) (1.4.1)\n",
      "Requirement already satisfied: lxml<5.0.0,>=4.8.0 in ./env/lib/python3.9/site-packages (from apple-health-exporter) (4.8.0)\n",
      "Requirement already satisfied: numpy>=1.18.5 in ./env/lib/python3.9/site-packages (from pandas<2.0.0,>=1.4.1->apple-health-exporter) (1.22.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in ./env/lib/python3.9/site-packages (from pandas<2.0.0,>=1.4.1->apple-health-exporter) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./env/lib/python3.9/site-packages (from pandas<2.0.0,>=1.4.1->apple-health-exporter) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in ./env/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas<2.0.0,>=1.4.1->apple-health-exporter) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!{sys.executable} -m pip install apple-health-exporter\n",
    "\n",
    "from apple_health_exporter import health_xml_to_feather\n",
    "output_file='patient-data/data.feather'\n",
    "health_xml_to_feather(zip_file='patient-data/export.zip',output_file=output_file)\n",
    "\n",
    "import pandas as pd\n",
    "data = pd.read_feather(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics and their respective data types\n",
    "\n",
    "1. **Distance Cycling** : HKQuantityTypeIdentifierDistanceCycling\n",
    "2. **Double support time** : HKQuantityTypeIdentifierWalkingDoubleSupportPercentage\n",
    "3. **Exercise minutes** : HKQuantityTypeIdentifierAppleExerciseTime\n",
    "4. **Six minute walk** : HKQuantityTypeIdentifierSixMinuteWalkTestDistance\n",
    "5. **Stair speed down** : HKQuantityTypeIdentifierStairDescentSpeed\n",
    "6. **Stair speed up** : HKQuantityTypeIdentifierStairAscentSpeed\n",
    "7. **Step length** : HKQuantityTypeIdentifierWalkingStepLength\n",
    "8. **Walking and running distance** : HKQuantityTypeIdentifierDistanceWalkingRunning\n",
    "9. **Walking assymetry** : HKQuantityTypeIdentifierWalkingAsymmetryPercentage \n",
    "10. **Walking speed** : HKQuantityTypeIdentifierWalkingSpeed \n",
    "11. **Walking steadiness** (last 4 weeks) : HKQuantityTypeIdentifierAppleWalkingSteadiness\n",
    "\n",
    "\n",
    "## Distance cycling\n",
    "\n",
    "This value is calculated using the iPhone's GPS every time the user registers a Cycling Workout.\n",
    "\n",
    "\n",
    "## Double support time \n",
    "\n",
    "> This is the percentage of time during a walk that both feet are on the ground. A lower value means you spend more of your walk with your weight on one foot instead of two, which can be a sign of better balance. During a typical walk this measure will fall between 20 and 40%.\n",
    ">\n",
    "> Walking requires strength and coordination. Changes in these can affect your balance and two-foot contact time.\n",
    ">\n",
    "> Double support time naturally varies with how fast vou walk and the terrain, but may increase with age.\n",
    ">\n",
    "> Double support time is recorded automatically on iPhone when you carry your phone near vour waist, such as in a trouser pocket, and walk steadily over flat ground.\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "## Exercise minutes\n",
    "\n",
    "> Every full minute of movement equal to or\n",
    "exceeding the intensity of a brisk walk for vou\n",
    "counts towards our dailv Exercise minutes.\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "## Six minute walk\n",
    "\n",
    "> This is an estimate of how far you can walk on\n",
    "flat ground in six minutes based on a week of\n",
    "vour motion and workout data. The fitter vou\n",
    "are, the higher this number will be.\n",
    ">\n",
    "> This prediction is considered a measure of your\n",
    "capacity for exercise. Longer distances are\n",
    "associated with healthier cardiac, respiratory,\n",
    "circulatory and neuromuscular function, and\n",
    "may reflect your ability to perform everyday\n",
    "activities. Improvements in fitness and mobility\n",
    "can increase our six-minute walk distance.\n",
    ">\n",
    "> The standard six-minute walk test measures\n",
    "the maximum number of metres you can walk\n",
    "on an unobstructed, flat course doing laps of a\n",
    "standard length. The test is often given to\n",
    "those who are recovering from a cardiac or\n",
    "respiratory condition. You can get a predicted\n",
    "six-minute walk distance up to 500 metres\n",
    "using data from a device, such as Apple Watch.\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "## Stair speed down\n",
    "\n",
    "> This is a measurement of how fast vou walk\n",
    "down the stairs.\n",
    ">\n",
    "> Descending stairs safely is an important\n",
    "indicator of our overall mobility.\n",
    "> Descending stairs requires strength and\n",
    "coordination. Changes in these factors, or pain,\n",
    "can affect your speed.\n",
    "> Apple Watch records your vertical speed when\n",
    "you go down a flight that is at least 3 metres\n",
    "(10 feet).\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "\n",
    "## Stair speed up \n",
    "\n",
    ">This is a measurement of how fast vou walk up\n",
    "the stairs.\n",
    ">\n",
    "> Climbing stairs quickly can be a sign of strong\n",
    "cardiovascular and musculoskeletal health.\n",
    ">\n",
    "> Walking up stairs requires strength,\n",
    "coordination and aerobic fitness. Changes in\n",
    "these factors, or pain, can affect your speed.\n",
    "Apple Watch records your vertical speed when\n",
    "you go up a flight that is at least 3 metres (10\n",
    "feet).\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "## Step lenght \n",
    "\n",
    "## Walking and running distance\n",
    "\n",
    "## Walking assymetry\n",
    "\n",
    "> In a healthy walking pattern, the timing of the steps you take with each foot are very similar. Walking asymmetry is the per cent of time your steps with one foot are faster or slower than the other foot. This means the lower the percentage of asymmetry, the healthier your walking pattern.\n",
    ">\n",
    "> Uneven walking patterns, such as limping, can\n",
    "be a sign of disease, injury or other health\n",
    "issues. An even or symmetrical walk is often an\n",
    "important physical theraov goal when\n",
    "recovering from injury.\n",
    "> \n",
    "> Your iPhone can record walking asymmetry\n",
    "automaticallv when vou carrv vour phone near\n",
    "your waist - such as in a pocket - and walk\n",
    "steadilv on flat ground.\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "\n",
    "\n",
    "## Walking speed\n",
    "\n",
    "\n",
    "\n",
    "## Walking steadiness\n",
    "\n",
    "> This is an estimate of your stability while\n",
    "walking. iPhone calculates Walking Steadiness\n",
    "using your walking speed, step length, double\n",
    "support time and walking asymmetry data\n",
    "that's stored in Health. This provides a sense of\n",
    "the way you walk.\n",
    ">\n",
    "> Your steadiness is also related to your fall risk.\n",
    "As steadiness goes down, your risk of falling\n",
    "goes up. Walking Steadiness is not an\n",
    "indication of how likelv vou are to fall in anv\n",
    "given moment, but an overall sense of fall risk\n",
    "in the next 12 months.\n",
    "> \n",
    "> iPhone can automatically record your Walking\n",
    "Steadiness in Health when you carry your\n",
    "iPhone in a pocket or holder near our waist.\n",
    "You can also receive a notification if vour\n",
    "Walking Steadiness is low or very low. You can\n",
    "manage these notifications in Health Checklist.\n",
    "\n",
    "**Source:** Apple Health App on iOS 15.4\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to know what sort of data we are working with. The command will show what columns we have in the DataFrame and their data type (dtype)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type                                        object\n",
       "sourceName                                  object\n",
       "unit                                        object\n",
       "startDate     datetime64[ns, pytz.FixedOffset(60)]\n",
       "endDate       datetime64[ns, pytz.FixedOffset(60)]\n",
       "value                                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `type` column identifies the sort of data that we have available, as listed before in [Metrics and their respective data types](#Metrics-and-their-respective-data-types)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "HKDataTypeSleepDurationGoal                                    1\n",
       "HKQuantityTypeIdentifierActiveEnergyBurned                715914\n",
       "HKQuantityTypeIdentifierAppleExerciseTime                  21536\n",
       "HKQuantityTypeIdentifierAppleStandTime                     20921\n",
       "HKQuantityTypeIdentifierAppleWalkingSteadiness                27\n",
       "HKQuantityTypeIdentifierBasalEnergyBurned                 215522\n",
       "HKQuantityTypeIdentifierBloodAlcoholContent                   11\n",
       "HKQuantityTypeIdentifierBodyFatPercentage                   2505\n",
       "HKQuantityTypeIdentifierBodyMass                            2512\n",
       "HKQuantityTypeIdentifierBodyMassIndex                       3358\n",
       "HKQuantityTypeIdentifierBodyTemperature                        1\n",
       "HKQuantityTypeIdentifierDietaryCaffeine                       89\n",
       "HKQuantityTypeIdentifierDietaryCarbohydrates                 161\n",
       "HKQuantityTypeIdentifierDietaryEnergyConsumed                163\n",
       "HKQuantityTypeIdentifierDietaryFatTotal                      130\n",
       "HKQuantityTypeIdentifierDietaryProtein                       156\n",
       "HKQuantityTypeIdentifierDietarySugar                          82\n",
       "HKQuantityTypeIdentifierDietaryWater                         445\n",
       "HKQuantityTypeIdentifierDistanceCycling                    95022\n",
       "HKQuantityTypeIdentifierDistanceWalkingRunning            146517\n",
       "HKQuantityTypeIdentifierEnvironmentalAudioExposure         27318\n",
       "HKQuantityTypeIdentifierFlightsClimbed                     18932\n",
       "HKQuantityTypeIdentifierHeadphoneAudioExposure              6097\n",
       "HKQuantityTypeIdentifierHeartRate                         227528\n",
       "HKQuantityTypeIdentifierHeartRateVariabilitySDNN            4100\n",
       "HKQuantityTypeIdentifierHeight                                 1\n",
       "HKQuantityTypeIdentifierLeanBodyMass                         305\n",
       "HKQuantityTypeIdentifierRespiratoryRate                     4563\n",
       "HKQuantityTypeIdentifierRestingHeartRate                     695\n",
       "HKQuantityTypeIdentifierSixMinuteWalkTestDistance             65\n",
       "HKQuantityTypeIdentifierStairAscentSpeed                     523\n",
       "HKQuantityTypeIdentifierStairDescentSpeed                    572\n",
       "HKQuantityTypeIdentifierStepCount                         138009\n",
       "HKQuantityTypeIdentifierVO2Max                                89\n",
       "HKQuantityTypeIdentifierWalkingAsymmetryPercentage          2089\n",
       "HKQuantityTypeIdentifierWalkingDoubleSupportPercentage      4091\n",
       "HKQuantityTypeIdentifierWalkingHeartRateAverage              652\n",
       "HKQuantityTypeIdentifierWalkingSpeed                        5270\n",
       "HKQuantityTypeIdentifierWalkingStepLength                   5277\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"type\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sourceName` column contains the name of the App or device that collected the information. We can get the listing of the devices by calling the `unique()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Water Reminder', 'Sync Solver', 'Power Sync', 'Asana Rebel',\n",
       "       'Health', 'Shortcuts', 'Sonic Screwdriver', 'Chronograph',\n",
       "       'Sleep Cycle', 'Bruno’s Apple\\xa0Watch', 'SonicScrewdriver',\n",
       "       'Nike Training', 'Today', 'Sleep'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sourceName'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first exercise, we want to get the walking distance for the last 30 days and plot that chart to see if it matches with the user's data on the iPhone. We are calculating per week and using sunday as the start of the week to keep aligned with how the HealthKit shows its charts.\n",
    "\n",
    "The dataframe contains a column called `sourceName` that indicates where the user entered their data. In our case, the user has an Apple Watch and and iPhone. The `sourceName` value can be either \"SonicScrewdriver\", the iPhone, or \"Chronograph\", the Apple Watch.\n",
    "\n",
    "We want to use the Apple Watch to get the walking distance. Since the user doesn't always carry the iPhone, the pedometer of the Apple Watch is more reliable than the movement chip in the iPhone. \n",
    "\n",
    "In this case, the Apple Watch was named `Chronograph` by the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2020-09-22 00:00:00+01:00</th>\n",
       "      <td>0.983081</td>\n",
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       "      <th>2020-09-25 00:00:00+01:00</th>\n",
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       "      <th>2020-09-26 00:00:00+01:00</th>\n",
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       "      <th>2022-03-29 00:00:00+01:00</th>\n",
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       "    <tr>\n",
       "      <th>2022-03-30 00:00:00+01:00</th>\n",
       "      <td>3.093462</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-31 00:00:00+01:00</th>\n",
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       "    <tr>\n",
       "      <th>2022-04-01 00:00:00+01:00</th>\n",
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       "      <th>2022-04-02 00:00:00+01:00</th>\n",
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       "                              value\n",
       "startDate                          \n",
       "2020-09-22 00:00:00+01:00  0.983081\n",
       "2020-09-23 00:00:00+01:00  2.476899\n",
       "2020-09-24 00:00:00+01:00  2.774861\n",
       "2020-09-25 00:00:00+01:00  2.037014\n",
       "2020-09-26 00:00:00+01:00  6.501673\n",
       "...                             ...\n",
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       "2022-04-02 00:00:00+01:00  2.512661\n",
       "\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_watch_name = 'Chronograph'\n",
    "walking_distance = data.loc[data['type'] == 'HKQuantityTypeIdentifierDistanceWalkingRunning'][['startDate','endDate','sourceName','unit','value']]\n",
    "walking_distance = walking_distance.loc[walking_distance['sourceName']==apple_watch_name]\n",
    "walking_distance_day=walking_distance.groupby(pd.Grouper(key='startDate', freq='D')).sum()\n",
    "walking_distance_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We tested the values by looking at them side by side with the charts in the iOS app and they match, apart from the fact that the numbers shown to the user are rounded down.\n",
    "\n",
    "![](./images/2022-04-03_00.51.09.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to get the last 30 days of walking distance plotted in a chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6bVNNcDkuHktF+1uC3VCvXhXdAkBtmQtjoH0gsS0yYtXpCe6FmB4MAO6+eS3F+U4+++DRhPd8ilQiq4kqRaQ09Hk+cDMws9j2QeC9oc9vB35v0u0npNJKIGA40u4JK19gKXXpmQbhau4ZptbtIs9pn7qtobyAojwHB+Ow+WxPUy82gR0ryqL6/qm9Bmm+VNTpGcMmUFmYe8Ht7oIcPvm6dexp7uWRw50pGt3sEjkzqAZ2i8gh4CWCOYOHRORzIvLm0DX3A+Uicgq4G7gngeNRWeBM3ygjE/6w8gUWPdMgfE2hstLpbDZhU018ksh7mnvZuKxk1jbV4agrz4xDbjoHvVQW5c7aeO9dO+pYv7SIf/x1I2MT6bPclchqokPGmK3GmE3GmEuNMZ8L3f5pY8yDoc+9xpi3G2NWG2N2GGMyo4mHSpnzbavDnxmUuJwMjE6m3bQ83QQChlfPDV+QL7BsqinlWMdQTE3Xxib8HGgZiHqJCGBJUR45dlvaH3LT4fFOJY9nstuCyeSzA2N868mmJI9sbroDWWWUI+2DOO3C2iUL97KxuF05TPgDjGVI98hUafeM4Z0MXDQzANhcU4IvYGjsiD7x+fKZfib8Aa5eGX0wsNmE5e582tJ8r0FXaI/BXK5aWc5tm6r51pNNtKXJLEeDgcooR9o9rF1SFNFmJas/kW48m1/TtJ5EM22qKQXglbPR5w32NJ/DbhOuiDJfYKlxp/9eg855ZgaWv37jBkTgnx5uTNKo5qfBQGUMYwxH2gcXbFs9U4nVuXREk8jzaZ6lrNRSXZJHRWFuTDuR9zT1sqmmhMJcR9SPAdYhN+kbDEYnfAx6fSwtmb9v1rLSfD58/WoefqWT55qSc9b0fDQYqIzR4fHSNzLBxjDaUEzn1jMNwtLUM0xRnoOKwos3g4kIm2NIIo+M+zjU5olpichSW+aif3SSIW96/nt2eqw9BrkLXAl3XbeSGnc+n33waNw29UVLg4HKGNEkjyF42hnomQYLae4ZYVVl4ZzN/zbVlHKqZ5jhcV/Ej/3S6T58ARNT8thS6w5VFKVp3sAKBksWWCYCyHPa+dtbL+F41xA/eiG1fYs0GKiMcaR9EJsED12JhJ5pEJ6mnuFZk8eWTbUlGHM+KEdiT1MvTruwvT62fAEE+xNB+paXWmdGVy+wTGR5/cYl7FpdzpcePU5fCpcyNRiojHGk3cPKykJcOZGtOeuZBgsbHvfRNTg+a77AsjmURI5mqWhPcy9ba93k59gXvngB52cG6RkMOqxlojBmBhBcgvvMmzYyMuHnC4+kLpmswUBljGDyOLJ8AQRbH7ty7DozmMf55PHcM4Oyghxq3PkR70Qe9E5y+KyHq+KwRATBHeVFuQ7a+tNzmahr0EtJvjOiwLd2SRF3XbeSn+1t47GjXQkc3dw0GKiMcG54nA6PN+J8gaU036mlpfNonqesdLrNoWMwI/Ficx8BQ1ySxxB8J11T5krbjWfhlJXO5hOvXcsl1cXc8/ND9KTgnGcNBiojHGkPta2OsJLIUurKwaNnGsypqWcYm5xv9zCXTTUltPaNRbS2vae5lxyHja11pTGO8rxad37aLhN1LrDhbC45DhtfeecWhsZ93PPzQ0nfMa/BQGWEI+3RVRJZ3AU6M5hPU88wdWUuch3zL21siiJv8FxTL5fXuS9ofher2jIXbf1jadliJNqZAQSXi/7qlvX87lg3P3mxNc4jm58GA5URTnUPs7Q4L+oGZ3qmwfyC5x4vfD7EpcuLEYFDYeYN+kcmaOwYZGec8gWWujIXY5N+zg2n17/ppD9Az/A4S6KYGVjev7OBXavL+YeHjk4dNpQMGgxURmjtG11wCWM+pS4nHp0ZzMofMDSfG5k3eWwpynOysqIg7JnBC68GjyeJx/6C6dK1vLRnaBxjgju2o2WzCV98+2acduETPz2QtM1oGgxURjjTO0p9WWzBYGBMO5fOpn1gjAlfYMHksWVzTSkH2zxh/Sz3NPWS77RPLS/FS7qWl1p7DKJdJrJUl+Tzj39wGQdaB/j67lPxGNqCNBiotDc24ad7aJy6GIKB25WDP2AYimL3bLY7FSorDWeZCIJJ5J6h8akXvvnsae5le4M7osaC4ahJ12Dgufi4y2i9afMy3rplGV/7/Sn2t/TH/HgL0WCg0p7V4jeWZSIr1zAwoktFM50vK114mQhgU20pwIJN63qGxjnRNRz3JSKA/Bw7lUW5adeSojPCDWcL+exbLmVJUS53/+wgoxOJfSOjwUClvTO9oWAQ48wAYEDLSy/S1DNMSb6TsoKLG9TN5pLqYhw2WTBv8HxzMF+wc1VFrEOcVW0atrLuHPSS47BR6oqu0GGmknwnX/pfWzjdO8I//jqxu5M1GKi0Z20uiikYFOiZBnNp7hlmVWXBnA3qZspz2lm3tGjBiqI9zb0U5jqi2jUejto03HjW6fFSXZIX9s8yHFevKueD167kRy+08PtjidudrMFApb2WvlEKcx1hv3OdjXWmgfYnulhTmGWl020K7USeL4n8fFMvO1aUzXoOcDzUul10eLwpb/08XafHG1a30kh98nVrWb+0iE/9v1foHU7M7mQNBirttfSNUlvmiundlnYund2gd5Keofkb1M1mc00Jg14fp3tnf2fe6fHSfG4kbi0oZlNX5sIfMFON4dJB56A3prLSueQ67HzlnVsYHJvknl+8kpCqOA0GKu219I1SVxZeO+C5WAlk3Xh2ISt5PF/r6tkstBN5T3Pw5K5EJI8tNdZegzRZKjLGBFtRJGBmALB+aTGfumUdjx3t4md74787WYPBInO8c4ij7dEfap5sgYChtW+U+vLIXqxmcthtFOU5dGYwQ1P33EddzmftkkLynLY5K4r2NPVSku9kQ3Vi8gVwfq9BuuQN+kcnmfAFErJMZPnfu1awc1U5n/3VUc70xnd3sgaDReYjP9nHx/5zf6qHEbbuoXHGfQFqY0geW0pdTs0ZzNB8bhiHTaiPsGzXYbexcdncx2Duae7lyhVl2G3xS6TOVF2Sh90maVNRZJWVJmKZyGLtTnbYgruTJ+OYL9FgsIic6R3hRNcwJ7uH6Qpjw1A6iEclkcXtymFAz0G+QFP3CHVlLpxRJHk31ZRwpH3wogRuW/8orX1jCV0igmBAWlaalzZ7DToHg+OIpS9ROJaV5vP5P7iMfS0DfOAHexmJ00ZKDQaLyOON3VOfP9d0LoUjCZ81FY6lFYWlRM80uEjzueGIK4ksm2tKGZv0T+1gtuxpSkw/otnUlbnSaGYQrPJJ5MzA8ubNy/jCH17G0yd7eNd3no9LhZEGg0Xk8aNdrKkqpNTl5LlTvakeTlha+0axSfDdUKzcrhw8ukw0xR8wnD43GvbO45k21QTbiR+akTfY09RLWUEOa6siO6s6GrVuV/rMDDxj2AQqC3OT8nzv3FHHt+/czrHOIW7/1p6YE+kaDBYJz+gkL57u43Ubl3DVinKea+rNiKZtLX2jLCvNj0tvG7dLZwbTtfWPMuEPv0HdTA3lBRTlOTg4LW9gjGFPcy9XryzHlsB8gaW2zMW54fGEt2oIR+egl4rC3ITtq5jNzZcs4ccfvJK+kQn+8JvPcfhsZEeSTqfBYJF44kQ3/oDhpg1L2LW6nLMDY1NtHtLZmb7RuOQLAEpcOQx6J/EH0j8IJkPTVIO66GYGNpuwqabkgp3IZ3pH6fB443be8UJq3MEZYzqch9w5OJ6UJaKZLq8v4+d/ejVOm/DO+57nuVPRLQFrMFgkHm/spqIwhy01pexcHewV81xT+i8VtcYxGLhdToyBQU0iA+GfezyfTTWlHOscZNznB4JVRBC/844XYv1upMNeg07PWELLSuezuqqIn394J8tL83nvf7zIrw62R/wYGgwWgQlfgCeOd3PT+iXYbMLKigKWFufxbJonkUfGfZwbnoipW+l0VvMw3XgW1NQzjNvlxB1Dm4/NNSVM+g2NHUNAMF9QWZQbdR4iUrVpFQwSs/s4XNUl+fzsT65ma62bj/xkP9975tWIvl+DwSLw0uk+hrw+XnvJEgBEhJ2rytnT1EsgjZdM4llWClA61blUZwYQ7EkUy6wALtyJbIzhuaZgviCejdrmU16QQ77TTkuKk8ijEz4Gvb6El5UupMTl5IE/3sHrNy7hcw8d5QuPHAs7N5iwYCAitSKyW0SOisgREfnYLNdcLyIeETkQ+vh0osazmD12tItch41rVp9vJbxzdQV9IxMc7xpK4cjmF/dgYJ1poDMDwOpWGlswqC7Jo6Iwh4OtHpp6hjk3PJ6UklKLiFBblvpW1snYcBauPKed//Puy3n3lXV868kmPvlfB8PanOZI4Jh8wCeNMftEpAh4WUQeM8YcnXHd08aY2xI4jkXNGMPvjnVxzeoK8nPsU7dbB5Q/e+pcQlsGxMKa+teXxWfJYepMA60owjM6ybnhiaiTxxYRmepgau0v2JnEYAChvQYpXiayTn1LVc5gJrtN+PxbL2VJcR5ffuwEvcMLvwFK2MzAGNNhjNkX+nwIaASWJ+r51OxOdA3T2jc2tURkWVaaz4qKgrROIp/pHaU4z0FJnA4KOZ8z0GDQdC66nkSz2VRTwqmeYR5v7GZZSV7cZnLhqnG7aOsfS2mpdLxPOIsHEeGjN62Z2py2kKTkDESkAdgKvDDL3VeLyEEReURENiZjPIvJ443BwzBuWl910X07V5XzQnNvXPubxFNLHBrUTVec58QmukwE5xvUxTozgOBOZGPgyRM9XLUqefkCS22Zi+FxX0pnfNbMIB5nH8ebtTltIQkPBiJSCPwc+LgxZma7zH1AvTFmM/A14JdzPMZdIrJXRPb29Cwc4dR5jx3tYnNtKVWzvGPZtbqCkQn/gidWpUpLHMtKIVgXX5Lv1GUioPncCE67xKUBoLUTGZJXUjpdbWivQSq7l3Z6vBTnOXDlJHLlPXo3z1gZmE1Cg4GIOAkGgh8ZY34x835jzKAxZjj0+cOAU0QuOjDVGHOfMWa7MWZ7ZWVlIoecFj7zP4f52u9Oxvw43UNeDrQOcPOGi2cFAFeF/uNGu0klkfwBQ1v/aFxerKYrdeVoaSnBmUG0DepmKi/MZXmoXUgyk8cWq/Q4lUnkYFlp7C1TUimR1UQC3A80GmO+PMc1S0PXISI7QuNJ30XsJHnwYDtf+/0pOjyxlcvtPhZsTHfThtnfFZQV5HBJdXFa7jfoHPQy6TcRt1ZeSKnLiSfG0tJvPdk0lSzNVM3nYi8rne7KlWWsqSqkxp3cfAGcP9cglT2KOge9KS8rjVUiZwa7gDuBG6eVjr5RRD4kIh8KXXM7cFhEDgJfBd5pMqFhTgINj/uCh2T4A3z7yeaYHuuxo90sL81n/dK5G4btWl3OvjMDeCf9MT1XvFndSuOdjCzNd8Y0M5jwBfi33x7n/mdi+7dJJZ8/wJneyM89ns8/vOVSfvYnV8ft8SJREDofO9Uzg6XFyWlQlyiJrCZ6xhgjxphNxpgtoY+HjTHfMsZ8K3TN140xG40xm40xVxljnkvUeDKFVSK3pDiXH7/YQneU5w6MTfh55lQPN1+yZN6E3s5VFUz4A+w93R/V8yRKa5z3GFjcrpyYcgane0fwBwz7WuY/DD6dtfaPMek3cd0lXJDriGknc6xq3fkpKy+d9AfoGR5nqS4TqXiykmB/e+sl+AOGbz8V3TvQZ0+dwzsZ4LVzLBFZdqwow2GTtFsqaukbxWGTuG/iKXHFlkC2qnD6RiYyotHfbKaOuqyK38wg1WpSuNegZ2gcY9KrrDQaGgzSjPULfc3qCt66ZTk/euEMPUORH1zxeGMXRbkOdqwom/e6glwHW2pL026/wZneUZa78+PeDtjtymF43Bd1Oe2p7vMHubx8Jr1mU+FqtvYYVGRPMKgrc3F2YCwlHWmtstJ02H0cCw0Gaaatf4zCXAelLid/dsMqJnwBvvt0ZLODQMDwu2PdXLeuMqxzAHauKueVtoGYE6vxFM9updNZG8+inR2c6hlmaXEeRbkO9rVkZjBo6h6hojAnbpv50kGt28Wk36TkOFdrw1m67D6OlgaDNNPaN0qNOx8RYWVlIW/evIwH9pyJ6Fi7Q2c99AyNc/MCS0SWnasrCBh4oTl9ZgfxPMdguqlmdVEmkZt6hlmzpJAtdaXsaxmI48iSp6lnmJVZNCsAqC1L3V6Dqd3HOjNQ8TRzo9Wf37gar8/PdyNoR/v40S7sNuH6deHtydhaV0qe05Y2S0WesUkGRicTEgzc1swgillQIGBo6h5hdVUhW+vcHO8cZDhOh5EnU/O5EVZVJafFdLKcLy9NfjDoGvSS47BN/W5lqrCDgYhsEpE3i8gfWh+JHNhiZIyhrX/sgo1Wq6uKuPWyah547jT9I+G9m328sYsrGtxT74IXkuuwc0VDGc+lSRJ5qkFdnPcYAJTmB38m4f4sp2v3jDE26Wd1VSHb6koJGDjUOhDnESZW/8gEfSMTWTczWFaaj02ClVLJ1uHxsrQ4L+ltOOItrGAgIt8Dvge8DXhT6EM7jcbZueEJxib9U9vrLR+5cQ0jE36+9+zCs4PWvlGOdQ4tWEU0085VFZzoGqZ7KPlrrjNZU/147z6GaTmDKGYGTdNOBtta6wbIuLzBVPI4y2YGOQ4b1SX5tKVimWjQm/FLRBB+C+urjDGXJHQkas4XwXVLi3jjZUv5/rOn+cA1K+dN/P3OakwXYTDYtTrYRmBPUy9v2ZLa5rLxPsdguvMJ5MhnBlYl0eqqQkpcTtZUFWZcRVFTdzCgZdvMAILnIadi41mnx8uW2tKkP2+8hbtMtEdENBgkWFv/3C+Cf37DGobGffzHc/PPDh5v7GZ1VSErKiJ757dxWQnFeQ6eO5X6vEFL3yhlBTkU5cV/DbYw14HDJlFVE53qHqbU5aQ8tLlqW52b/a2Ztfms6dwwOXbb1EHy2aS2zJX0BLIxJmtmBuEGgwcIBoTjInJIRF4RkUOJHNhiZK2Vz9bf5ZJlxbzukiV875lXGfTO/kI26J3k+ebeiJeIIHgYxlUry9Ni81lLb/wb1FlEhFKXM6ozDZpCJ4NZa8Pb6ksZGJ2k+dxIvIeZME3dI9SXu+K+fyMd1JW56BocT2prlf7RSSZ8gYzfcAbhB4P7CfYZuoXz+YI3JWpQi1Vr3xgVhbkXnEg23UdvWsOg18cDz52e9f4nj/fgCxhuvmT2LqUL2bW6grb+MVpSvLM23q2rZyp15US1TNTUPczqaf18ttWF8gYZtFTUfC72oy7TlVVeenYgeUnkbCkrhfCDQY8x5kFjzKvGmDPWR0JHtggFXwTnnr5furyEm9ZX8d1nXp21pPHxxi7KCnLYEkpuRsrKG6SyqsjnD3B2YIz6BAYDdxQtKfpHJugdmWD1tBYOqyoLKc5zZMx+g0l/gJbe0axLHltSUV7alcaH2kQq3GCwX0R+LCJ3aGlp4rSG0b//IzetYWB0kh/uuTAWT/oD7D7WzY3rq7DboitxW1VZSFVRLs+mcL9B+4AXf8AkdGZQkh/5mQZNPRdX4dhswpY6N/szpKKopW8UX8BkZfIYzhdeJDMYdKThcZfRCjcY5APjwOvQ0tKEmPQH6PB4p97dzGVLbSmvWVvJd55uZnTi/Oxg7+l+Br2+qPIFFhFh56py9jSdS1lSdKqSKAF7DCzuKM40mKokqrywHfi2ulKOdw0xNEceJ51kY4O66SoLc8l12JK616Bz0IsIVBZldvtqWCAYiEgtgDHm/TM/gItOLksnB1oHeOs3nuXxo12pHkpYOkLviGvnWSayfPSmNfSNTPCj51umbnu8sYsch41r11x0UFxEdq6u4NzwBMe7hmJ6nGid6UvMOQbTBRPIkc0MTnUPk+uwsXxGFc7l9W6MCf6+pTtrn0Q8zj1ORzabBMtLw5wZdHq8/O0vX+E933sRX5SNCzs9Y1QW5sblxLhUW+hv8FjoMPsLiMj7gX9PyIhiZIzhu083c/s3n+NA6wAPH+5I9ZDCYtVHh1NFc3m9m2tWV/Dtp5oZm/BjjOHxxi52rSqnIDe2M1h3rrKOwkzNUlFL3yg5dltCm36VunLwTgYiqjpp6hlmZWXhRUtwW2pLEYF9ZwbiPMr4a+4ZprIol+IElOymi9oy14J7DbqHvHz2V0e47t9283+fb+GpEz1R5306B8ezIl8ACweDu4FHRWSNdYOI/H+h21+TyIFFo39kgg/8YC+f/3UjN66vYnu9m2MdqXmHGynr3cxCy0SWj960hnPD4/zkxRZOdQ9zpnc04o1ms6lxu6gvd6UsidzaN0pNWX7UeY9wRNO59FTP8KyHwRTlOVlbVZQRO5FPdA1dUA2VjWrdrjmPv+wdHuefHm7kun/dzQN7zvDWLct45GPX4rAJu493R/V8nZ6xrMgXwALBIHRI/Z8Cj4jIpSLyFYL5guuMMW1JGF/YXj7Tx61ffZqnTvbwmTddwrfvvJzLG9yc6h6Ound9MrX2j2KP4DCXHSvKuGplGd96solfvxKc/dw0x8H3kdq5qoIXmvuinjrH4kxvYstKIXimAcDAWHhLRd5JP239YxdUEk23rb6U/S39BFLQSz9c3kk/RzsG2VRbkuqhJFRtWT6esckLckIDoxP822+Pce2/7uY7Tzfzhkur+d3dr+Ffb9/Mhupitje4p84Lj1SnJzs2nEEYCWRjzO+A9wNPACuBG40xafM2KBAwfPOJJv7Xt5/HYbfx8z/dyft3rUBE2LC0mAl/gFczYFNQS98Yy0sjO8zlYzetpXtonG/sPsVly0uojtOxeztXlTM07uOVs564PF64jDG0JCEYlOYHZwb9I+HNDJp7RjCGOYPB1jo3g17fVN+fdHS0Y5BJv2FrFrRNmE/dtIqiQe8kX3n8BNf+y26+sbuJG9ZX8dgnruPed2yhYdoO/RvWVXGsc2hqz0C4Rid8DHp9WRMM5l1gFpEhwAAC5AI3Ad0S3IJpjDHFiR/i3HqHx7n7Zwd58kQPt15WzT+/7bIL1kPXVwcrPxo7Blm7ZO5D4dNBa99oWMnj6a5aWcaOhjJePN0XUxXRTFN5g6ZettZFt2chGp6xSYbGfYkPBhGeaXDKKiudY4nl/OazAVZXpefv2YHQmni0e1AyhbV7/6u/O8kLr/bhGZvk9RuX8Imb17J+6ewvVzesr+KfHznGE8e7eeeOurCfqzOLykph4WWiImNMcejPHGNMwbSvUxoInm/u5Y1ffZo9zb18/q2X8vV3bb0oMbayohCnXWjMgLxBW/9o2PkCi4jwydetpSjXwa2bquM2lvLCXNYvLeLZU8nNG1hnCid8maggss6lp7qHsQlz9ntaWVFASb4zrfMGB1oHWFqclzXvYudiFWA8erSLy+vdPPSRa/j2ndvnDAQAa6oKWV6aH3HewDruMluCQWylJyny1d+d5CuPn6ChvID/eN8OLlk2+z90jsPG6qoijnUOJnmEkRkZ93FueCKqfjxXrizn0N+/Lu691HeuquD/vnAG76SfPOfs7THirWXqHIPElj5OnWkQ5sygqWeY2jLXnD8Hm03YWlea1h1M97f2s7WuNNXDSLiSfCffeNc2qkvzpmZsCxEJHgT1y/1nmfAFwjoqFrKrFQVk4Elnr54b4cuPneDNm5fx4EeumTMQWDYsLUr7iqK20CaZaJuzJeJQjV2ry5nwBZLad+d8C+/EdtTMz7GT67DhCbOaqKl74X4+l9e5Odk9nFbnSFt6h8dp7RvLijbL4bh1U3XYgcBy/boqRib87D3dF/b3dGZRKwrIwGAwMuHjX952Gfe+YwuFYdTUr68uonPQG9XJVslyvqw0fdoK71hRht0mSe1i2tI7SkVhLq6cxE9Yw9145g8Yms+NzJk8tmyrD774pOPmM2tMiyUYRGPnqnJy7DaeONET9vd0ebwU5zmS8vuaDBkXDFZXFvGOK+rCfjdsrRU2pvFSUSJP9opWUZ6TzTUlPJvEzWctfaMJOepyNm5XTlj7DNr6R5nwBRasz99cW4pN0rOD6YHWAew24bKa7C4rjUVBroMrV5ZFVGLakUVlpZCBwSDPGdmQrYqidF4qau0fxZVjnzo0JV3sXFXBobaBOc9PiLdEt66eriQ/vM6lp7rDOyayMNfB2iXpufnsQOsAa5cUZc072ES5fl0VJ7uHw25n0TXoTehO+WTLuGAQqcrCXMoLctI6idzaN0at25V2B2rvXFVOwJCUxOiEL0C7ZyxpsyO3K7zOpXM1qJvNtno3B1oH0mrzWSBgONA6oEtEYbhhXSVA2EtFHR5v2JtEM0HWBwMRYUN1Mcc603dm0NYf+R6DZFi3NPgC2NyT+E17ZwfGMIaEnmMwXanLGVZp6anuYSoKc+c9d9qyrc7NkNc3tS8hHTSfG2bI61sUlUSxWlFRQH25iyfCWCqa9AfoGR7PmrJSWATBAGD90iKOdw7hT6N3bBZjDC19o7MedZlqZQU5FOU6ONOb+GBgPUciW1dPZ512tlCr7qY5ehLNZlvoBTedSkz3hzabZfvO43gQEa5fW8mzTecWbGLYMzSOMbA0Trv+08HiCAbVxYz70rMtRd/IBKMT/qStlUdCRKivcE1tBkska502WT8Ht8vJpN8wOjH3f3pjDKe6hxesJLKsqCjA7XKmVRL5QOsARbmOrD3qMt6uX1+FdzLAi6/OX2J6vqw0888xsCyOYBBa7kjHvEFrjHsMEq2+vCApM4OWvlFyHTaqknRIiNW5dL68Qc/wOINeX9jBQETYVudOqyTygdYBNtWWYEtgF9hscvXKcnIdtgV3I3eFNpxpAjnDrK4K9qFPx4qiZG20ilZDuYu2/rGEd361upUmK4l+vj/R3HmDpu5gEAw3GEAwidzUMxJ236NEGpvwc6xzSJPHEchz2tm5qpwnjs+fRLaOu4xXc8h0kLBgICK1IrJbRI6KyBER+dgs14iIfFVETonIIRHZloix5DntrKwoSM+ZQYTnGCRbfXkBvoChfSCxRwkmc48BnO9cOl8wWKhB3WysRO3+NNh8drjdgz9g2Jrlzeni7Yb1Vbx6bmTeZeWuQS85DhvuMAoLMkUiZwY+4JPGmEuAq4A/E5FLZlzzBmBN6OMu4JuJGsyG6uK0bFjX1j9KeUFOzCeUJYpV3XM6gXkDY0yoa2vygoG7YOEzDZq6hynIsUdUPri5Jrj5bH8a5A32h5artmglUUSuXxs8F+SJeZaKOjxelhbnpV05eCwSFgyMMR3GmH2hz4eARmD5jMveAjxggp4HSkUkfu03p1lfXcTZgbGkbaAKV2vfGDVpmi8Apvq+tyQwb9A7MsFIkpPoU2cazLdM1DPMqqrCiP7DF+Q6WL+0mJfTIG9woHWAGnc+FYXZk+RMhrpyFysrC9g9z1JR56A3q8pKIUk5g9A5yluBF2bctRxonfZ1GxcHDETkLhHZKyJ7e3rC7x0y3YZQW4p0yxu09I2mVU+imaqKcslz2hI6MzjfrTR5wcDaNzAwT8+qU2E0qJvNtvpSDrQMpLyU+UCLbjaL1g3rqni+uZexOarNuga9LMmiDWeQhGAgIoXAz4GPG2OiWrQ3xtxnjNlujNleWVkZ1Tim2lKkUd7AH1qLT8eyUouI0JDgiqKWJJ1jMF2uw44rxz7nxrPhcR8dHm9EyWPLtjo3IxN+TnSl7o1H96CXdo9Xg0GUblhXxYQvwJ7mixs1GmOybvcxJDgYiIiTYCD4kTHmF7NcchaonfZ1Tei2uFtanEdJvjOt8gYdnjF8AZO2ZaWW+nJXUmYGyd54N19LiuYokseWy0MdTFNZYmolsHXncXSuWOHGlWNn97GLVyIGRieZ8AWyqqwUEltNJMD9QKMx5stzXPYg8J5QVdFVgMcY05Gg8bChOr0OumlJ80oiS0N5AS29owlb9mjpG2VpcV7SDtGxlLqcc55pMNWTaIEGdbOpK3NRXpDDvjMDsQwvJgdaB3DahY3LtFNpNHIddnatrmD38e6LdqmfLyvVYBCuXcCdwI0iciD08UYR+ZCIfCh0zcNAM3AK+A7w4QSOh/VLizneOZQ2jcTa+oLlmum8TATBhNqEPzC16zLeWnqT1610uvnONDjVPYzDJlGduiYibK1zT1XzpMKBlgE2VBcnPcBmkxvWVdHWP0bTjF5TXYPZt+EMEnjspTHmGWDeMgwTDLl/lqgxzLShuojRCT8tfaNTVTKp1No/ik2gujS9f6kaQi+IZ3pHWF4a/2R3S98o16ypiPvjLqTUlUOHZ/aZYlPPMPXlLpz26N4vbasv5fHGLvpGJihLcmtyf8BwqG2At11ek9TnzTbXh7qY7j7Ww+qq811rdWaQBayDbtJlqai1b5TqkvyoX3CSxarySUSPIu+kn85Bb2pmBvOcaRBtJZHFOnYxFbODk91DjEz4NXkco2Wl+axbUsQTJy7cb9A56EUEKpPUOiVZ0vtVKM7WLilChLRJIrf0pWfr6pmqS/LJsds4nYCKorb+5FcSWdyhzqUzlw0n/QHO9I5GVUlk2VRTgt0mKUkiHwh1KtVgELvr11fy4qt9DI/7pm7r8nipKMxN+zdxkcquv80C8nPsrChPn7YUrf3pXVZqsduE2rJ8zpyL/8zASqInq3X1dKUuJwEDQ9P+o0NwBuQLmJiCgSvHwYbqopQkkQ+0DlCS72RFGiyFZrob1lUx6Tc8e+p8iWnHYPaVlcIiCwZA2hx0Mzbhp2doPO0riSwN5QUJmRmkYo+B5XyzuguTyFNHXcbY9nlbnZuDbQP4EtzkbybrZLNsapWQKpfXuynKdVzQmqLLk13HXVoWXTBYv7SIM72jjMx4N5hs1vJIuu8xsNSVu2jpG13wMJhInelL3fnPVpOxmXkDq3pkVQwzAwi+kIxO+DmexM1nw+M+TnRpp9J4cdptXLu2gt3HeqZ+9zs8YzozyAbrq60kcmpnB60ZFgwaygsYnfDTMzwe18dt7Utu6+rp5jrToKl7mOqSPApjbB5oJZH3hdbwk+FQ2wABo83p4un6dVV0Dno51jnE6ISPQa9PZwbZIF0Oumntsw61Sf8EMiSuouhMivYYwPllIs+MlhSnemKrJLJYTeKS2cH0QGjn8Zaa0qQ9Z7a7fm2wxPSJ4z10hspKs61JHSRwn0G6qnHnU5TrSHnDuta+UfKcNiozpKOktdfg9LkRrmgoi8tjWuc/W/XcyTbVuXRaszpjDE3dw7x9e+1c3xa24Mlnpbzc0o930s/YhJ+RCV/oTz+jEz5Gx/2MTvoZm/AxMu5nbNLPdWsquawmup3DB1oGaCh3TbXoVrGrKs5j47Jidh/vZnNt8N8lG5eJFl0wEBHWp0Fbipa+UWrcqVkeicZydz52m8R1ZtAzNM64L5CymUHJLG2sOwe9jEz4Y84XWC6vd/Po0S7W/91vwv6eHz1/ht//xfUR7x42xnCgdYCdq8ojHaZawA3rqvjmk02c7Armk7KtYykswmAAwc1nv9x/FmNMyl6MM6Ws1OK026hx58e1ouhMX2rzJg67jaI8xwXLRFM9ieJ0gPzbt9finQzgsAuuHDsFOQ7yc+wU5NrJdzooyA12T83PcVCQY+eVsx7uvP9F7n/mVf7shtURPVeHx0v30Dhb6/Rks3i7YX0lX999iv96OdhxX5eJssT66iKGnvfR1j+WkhciYwxtfaPsaMis/7R1Za64zgysstJo+v/Ey8zOpVNlpVE0qJtNWUEOH3vtmrCvv3ZNJa/dUMU3n2jiHVfURnQwzVS+QCuJ4m5LrZtSl5PDZwcpynOk7cmEsVh0CWSY3pYiNXmDgdFJhsZ9GVNJZLH2GsSrvLSlbxQREtLvKFylrgtbUjT1DFOc50hpLueeN2xgbNLPVx4/EdH37W/pJ8dhY0OoYk7Fj90mXLcmmNvKxlkBLNJgsM6qKOpITd4g08pKLfXlLoa8vnkPkY9ES98oy0ryyXGk7tewNNSSwnKqe5jVER51GW+rqwp595V1/OTFVk51h/+G5UDrABuXFaf055nNrEKHpVmYL4BFGgwKcx3UlblSNjOYKivNkN3HlqmKojjlDVr6UldWanG7nBecdnaqeyQuZaWx+thNa3A57fzzw8fCun7SH+CVsx5dIkqg69ZWIqIzg6yzobqIxhRVFE0dapMhewwsDRXx3WuQDsGgNN85VVrqGZ3k3PB4TD2J4qW8MJcP37Ca3x3r5rlTFx+9ONPxziG8kwENBglUUZjL5968kTuvrk/1UBJi0QaD9UuLOX1uZM4DrxOptX+UUpeTojxn0p87FsFS2PjMDEYnfPQMjaekQd10pa4cBr0+/AHDqR7rdLPUBwOA9+9qYHlpPp//deOCp8xZyeNtWkmUUHde3cCmLN3Qt2iDwYbqIgKGlBxa3poG74ijkee0U12cN1UFFIvGUL4m1S+8VksKz9gkTXFqUBcveU47n7plHUc7Bvnv/fMfDX6gdYDyghxq3Jk121TpY9EGg1QedNPWP5Zx+QJLfZy6l1qtnVP9TtYdaknRPzpBU88wOQ5bWiX237x5GZtrS/nib4/PO4vd39KvnUpVTBZtMKgrc5HvtEd10I0xhn96uJHHj3ZF/L3+gKGtf5SaDMsXWBoq4rPXYH9rP7Vl+Sk/LapkWufSU93DrKwowG5LnxdUEeFvb91A56CX7zzdPOs1nrFJmnpGNF+gYrJog4HNJqxbGl1bigcPtnPfU8189qEjC67lztQ16GXSbzJymQiCM4PekQkGvbGVl+47M8DW2tSvb7unnWkQrwZ18XZFQxm3bFzKt55sojt0GPt0h9oGAO1UqmKzaIMBnD/oJpJNVJ6xSf7hoUbcLietfWP89khnRM/ZalUSZegyUUMo4RtL3qB9YIzOQS/b0uDFy2pW1zU4TmvfaNx6EsXbPW9Yz6Q/wL2zbESzjrnM1sSmSo5FHgyKGBidpHOWd1tz+dKjx+kbGef7799BfbmL+55qjiiYtKS4H0+s6spi32tgnQu8rT59ZgYHWvsJmNQntOfSUFHAnVc18NOXWjk+Y3/MgdYBVlcVTjXeUyoaizoYTCWRw8wbHGob4IfPn+HOq+rZXFvKH1+zggOtA7wcQb/61v4xRGBZaWZuXInHuQb7WwbITZO2CUV5DmwCe08H/w1XVabvucEfvWk1hbkO/vHhxqnbrE6lmi9QsVrUwcBqSxHO5jN/wPA3/32YisJcPvn6dQDcfnkNpS7nnIm92bT1jVJdnEeuI7L2xOmiINdBZVEuZ2KcGWyqKcFpT/2vn80mlOQ7aT43gkj6lJXOptSVw0dvWsNTJ3p48kQPENzN3jsyocFAxSz1/xtTqCTfyfLS/LBmBj964QyvnPXwd7ddQnFos5grx8EfXVnPo0e7ePVceC+Orf2j1GToEpGlodzF6ShnBuM+P0fODqa8pHQ6a6moxp0f8RkCyXbn1fXUlbn454eDG9H2twZnNBoMVKwWdTCAYN5goYqi7kEv//ab41yzuoI3baq+4L737KzHabPxvWdeDev5WvpGMzZ5bKkvL4h6ZnD47CAT/kBa9dy3ykvjdYZBIuU67NzzhvUc6xziv/a2cqB1gDynbeo4V6WiteiDwfqlxTT1jOCdnHtDz+d/3ci4L8Dn3rLxok09VUV5vHXrMv7r5dYLjk+cjXfST9fgeMaWlVoayl10DY4zOuGL+Hv3W8njNKgkslgzg3ReIpruDZcu5fJ6N1967AR7mnq5bHkJjjRYclOZbdH/Bq2vLgr2pQm1IpjpmZPnePBgOx+6fhUr53ix+MC1K/FOBvi/z5+Z97nODoS6lWbohjNLXah7qVUZFYn9LQMsL82nKo06P1rlpelaSTSTiPA3t26gZ2icY51DaTXLUplLg8E8B92M+/x8+n8OU1/u4sPXr5rzMdYuKeI1ayv5wZ4z884wMr2s1NIQQ0XRvpb+tCgpna40NDPIlGAAwTYet4WWLDVfoOJh0QeDhnIXuQ7brAfdfPvJZprPjfC5t1y6YGLxrutWcm54nAcPtM95TVuGbziz1If2GkSaN+jwjNHhSY/NZtNVFOWkfSXRbP7m1g28Y3st166pSPVQVBbIvoM8I+Sw21i7pOiimcHpcyN8ffcpbt1UzWvWVi74ODtXlbOhupjvPN3M27fXzNowrLV/jByHjaoU9+OJVYnLidvljLiiaH9op2w6VRIB3HFFHZcuK8FdkJPqoUSkuiSff7l9U6qHobJEwmYGIvI9EekWkcNz3H+9iHhE5EDo49OJGstCZlYUGWP49INHyLHb+PRtl4T1GCLCXdet4GT3ME+EasBnau0bpcadjy2NGqFFK5qKon1n+tNms9l07oIcrgsj4CuVzRK5TPR94JYFrnnaGLMl9PG5BI5lXuuXFnNueILuoWBbiodf6eSpEz3cffNalkSQ6Lxt0zKWFufxnadm34SWDWWlloZyF6fPRTYz2NfSz2XLS/SMXqXSUML+VxpjngL6EvX48bS+OlijfaxjiCHvJJ976AiXVBfzngiPt3PabbxvVwPPNfVypN1z0f2ZeqjNbOrKC2j3jDHuC++kuHGfn8NnB9MueayUCkr1W7SrReSgiDwiIhvnukhE7hKRvSKyt6dn9iWYWEw/6Obex07SPTTOP/7BpVHVbt+xo46CHDvfffrCTWie0UkGvb6MLyu1NJS7MCZ4UE84jrYHN5ulW/JYKRWUymCwD6g3xmwGvgb8cq4LjTH3GWO2G2O2V1bGf223rCCHJcW5/OpgB99/7lXetaMu6trtknwn77iijl8dbKfDc/6FsrU/OyqJLPXlkVUU7Qslj7UmXqn0lLJgYIwZNMYMhz5/GHCKSMpq5NYvLeaVsx7KCnL41OvXx/RY79/VQMAYvv/s6anbWrNkj4HF2msQbt5gX0s/y0vzI8rBKKWSJ2XBQESWSqj+UkR2hMbSm6rxWBUuf/3GDVO9aqJVW+bijZdV8+MXWhgKnQg2NTPIkmBQVpBDUa4j7JnB/jP9bNUlIqXSVsL2GYjIT4DrgQoRaQM+AzgBjDHfAm4H/lREfMAY8E4TySkxcfZHV9WxrDSPP9i6PC6P98FrV/LQoQ5++lIrH7h2Ja19YxTnObLmABIRob4ivO6lnR4v7R4vH9AlIqXSVsKCgTHmjgXu/zrw9UQ9f6Rq3C7ec3VD3B5vc20pOxrK+I9nT/O+nQ3BstIsmRVY6ssKODrLzu2ZrOZ0OjNQKn2lupooq33wupWcHRjj4cOdtPZnT1mppb7cRWvfKD5/YN7r9rX0k+OwsXFZSZJGppSKlAaDBLppfRUrKwr4zlPNtPWPZd3MoKG8AF/A0D4w/xnS+1oGdLOZUmlO/3cmkM0m/O9rVvDKWQ8TvgC17uzYY2CxzkM+PU8SecIX4JWzHrZqZ02l0poGgwR727YaykIN0DL9uMuZGioW3mtwtGOQCV9Adx4rleY0GCRYfo6dO68KtrVYGXrxzBZVRbnkOW3zVhTtO2OdbKbBQKl0tuhbWCfDn92wmitXlk3t2s0WIkJ9WcG8h9zsa+lnWUkeS0t0s5lS6UxnBkmQ47Cxc1V2HkBSX+6ad5lof8uAtqBQKgNoMFAxaago4EzfKIHAxfsFuwe9nB0Y0/0FSmUADQYqJvXlLiZ8AToHLy4v3RfabKbJY6XSnwYDFZOGUB5ktvLSfS0D5NhtbFyWXiebKaUupsFAxcTaazBbEnl/Sz8blxeT67Ane1hKqQhpMFAxqS7Jx2mXi4LBhC/AoTaPlpQqlSE0GKiY2G1CbdnFFUWNHYOM+wIaDJTKEBoMVMwaygsu2nh2PnlcmoIRKaUipcFAxczaazD9OIr9LQMsLc6juiS7+jEpla00GKiYNZQXMDrhp2d4fOq2fS39OitQKoNoMFAxm1lR1D3kpa1/TPMFSmUQDQYqZlbPJSsY7DszAKBtKJTKIBoMVMyWl+Zjt8lURdH+1n6cdtHNZkplEA0GKmY5DhvLS/OnKor2nxlg47IS8py62UypTKHBQMWFVVE06Q9w6OyA5guUyjAaDFRcNJQXcPrcCI0dg3gnA1pJpFSG0WCg4qK+3MWg18fvj3UDmjxWKtNoMFBxYVUU/XL/WZYU57JMTzZTKqNoMFBx0RDaa3C6d5RtdW5EJMUjUkpFQoOBiovaMhfW678mj5XKPBoMVFzkOe1UFweXhvSYS6UyjwYDFTf15QU47cKly0tSPRSlVIQcqR6Ayh5v3rKMdUuLdLOZUhlIg4GKmzt21KV6CEqpKOkykVJKqcQFAxH5noh0i8jhOe4XEfmqiJwSkUMisi1RY1FKKTW/RM4Mvg/cMs/9bwDWhD7uAr6ZwLEopZSaR8KCgTHmKaBvnkveAjxggp4HSkWkOlHjUUopNbdU5gyWA63Tvm4L3XYREblLRPaKyN6enp6kDE4ppRaTjEggG2PuM8ZsN8Zsr6ysTPVwlFIq66QyGJwFaqd9XRO6TSmlVJKlMhg8CLwnVFV0FeAxxnSkcDxKKbVoiTEmMQ8s8hPgeqAC6AI+AzgBjDHfkmBby68TrDgaBd5vjNkbxuMOAccjGEoJ4EnAtYvlsdNpLPrYyX3sdBqLPvbFKoBzETz2OmNM0Zz3GmMy6gPYG+H19yXi2sXy2Ok0Fn1s/bfXx77g2khfC+e9PiMSyDH6VYKuXSyPHen1+tjZ89iRXq+PndzHjquELRMliojsNcZsT/U4lFIqlSJ9LVzo+kycGdyX6gEopVQaiPS1cN7rM25moJRSKv4ycWaQEiJyi4gcDzXWuyd029MiciD00S4iv0zxMNPWfI0LReSTImJEpCIVY8sUIlIrIrtF5KiIHBGRj4Vuf3vo64CI6BLqPOb5GW4RkedD/5f3isiOVI812XRmEAYRsQMngJsJts14CbjDGHN02jU/B/7HGPNAakaZ3kTkOmCYYD+qS6fdXgt8F1gPXG6MiaRUblEJ9e6qNsbsE5Ei4GXgrYABAsC3gb8wYZRoL1bz/Ay/AtxrjHlERN4IfMoYc33KBpoCOjMIzw7glDGm2RgzAfwnwUZ7AIhIMXAj8MvUDC/9mbkbF94LfIrgC5qahzGmwxizL/T5ENAILDfGNBpjItl7s2jN9TMk+PtXHLqsBGhPzQhTR086C89sTfWunPb1W4HfGWMGkzmoTCcibwHOGmMOBvcgqnCJSAOwFXghxUPJWDN+hh8HfisiXyT4Jnln6kaWGjoziI87gJ+kehCZRERcwF8Dn071WDKNiBQCPwc+rm9AojPLz/BPgU8YY2qBTwD3p3J8qaDBIDxzNtULJT13AL9Owbgy2SpgBXBQRE4T/JnuE5GlKR1VmhMRJ8EXsR8ZY36R6vFkojl+hu8FrM//i+D/6UVFg0F4XgLWiMgKEckB3kmw0R7A7cBDxhhvykaXgYwxrxhjqowxDcaYBoJLb9uMMZ0pHlraCvXzuh9oNMZ8OdXjyUTz/AzbgdeEPr8ROJnssaWaVhOFKVRh8BXADnzPGPOPodufAL5gjPlN6kaX/mZrXGiMuX/a/aeB7VpNNDcRuQZ4GniFYPUQBJfacoGvAZXAAHDAGPP6VIwx3c3zMxwE/p1gHtULfNgY83JKBpkiGgyUUkrpMpFSSikNBkoppdBgoJRSCg0GSiml0GCglFIKDQZKKaXQYKCUUgoNBkoppdBgoJRSCg0GSiml0GCglFIKDQZKKaXQYKCUUgoNBkoppdBgoJRSCg0GSiml0GCglFJpTUTeKiJGRNYn8nk0GCilVHq7A3gm9GfYRMQeyfUaDJRSKk2JSCFwDfDHwDtDt10vIk+JyK9F5LiIfEtEbKH7hkXkSyJyELg6kufSYKCUUunrLcBvjDEngF4RuTx0+w7gI8AlwCrgD0O3FwAvGGM2G2OeieSJNBgopVT6ugP4z9Dn/8n5paIXjTHNxhg/8BOCswcAP/DzaJ7IEcsolVJKJYaIlAE3ApeJiAHsgAF+HfpzOutrbyhARExnBkoplZ5uB35ojKk3xjQYY2qBV4FrgR0isiKUK3gHwQRzTDQYKKVUeroD+O8Zt/08dPtLwNeBRoIBYuZ1ERNjZs42lFJKpSsRuR74C2PMbfF8XJ0ZKKWU0pmBUkopnRkopVTaEJFaEdktIkdF5IiIfCx0e5mIPCYiJ0N/ukO3v1tEDonIKyLynIhsnu9x5n1unRkopVR6EJFqoNoYs09EioCXgbcC7wP6jDFfEJF7ALcx5q9EZCfQaIzpF5E3AH9vjLlyrscxxhyd67l1ZqCUUmnCGNNhjNkX+nyIYLXQcoI7kX8QuuwHBAMExpjnjDH9odufB2oWeJw5aTBQSqk0JCINwFbgBWCJMaYjdFcnsGSWb/lj4JEFHmdOugNZKaXSTKhB3c+BjxtjBkVk6j5jjAntSJ5+/Q0Eg8E18z3OfM+pMwOllEojIuIk+AL+I2PML0I3d4XyAFZeoXva9ZuA7wJvMcb0LvA4c9JgoJRSaUKCU4D7CSaFvzztrgeB94Y+fy/wP6Hr64BfAHeGOpsu9DhzP7dWEymlVHoQkWuAp4FXgEDo5r8muN7/M6AOOAP8L2NMn4h8F3hb6DYAnzFm+1yPY4x5eM7n1mCglFJKl4mUUkppMFBKKaXBQCmlFBoMlFJKocFAKaUUGgxUlhGR4Ri/v0FE3jXHffUisk9EDoQ6QX5o2n2XhzpHnhKRr8r0LaPnr/l7ETkb+v6TIvILEbkklvEqFS8aDJS6UAMwazAAOoCrjTFbgCuBe0RkWei+bwIfBNaEPm6Z4zHuNcZsMcasAX4K/F5EKuM0dqWipsFAZT0ReZOIvCAi+0XkcRFZErr9NaF36QdC9xUBXwCuDd32iemPY4yZMMaMh77MJfT/J9QeoNgY87wJbtx5gFBXyfkYY34KPEoo+IjIp0XkJRE5LCL3SdAqEdk37e+yxvpaRL4Q6ld/SES+GNtPSS12GgzUYvAMcJUxZivwn8CnQrf/BfBnoXf61wJjwD3A06F37/fOfKDQoSGHgFbgX4wx7QRbA7dNu6yNBdoFT7MPWB/6/OvGmCuMMZcC+cBtxpgmwCMiW0LXvB/4DxEpB/4A2GiM2QR8PsznU2pWGgzUYlAD/FZEXgH+EtgYuv1Z4Msi8lGg1BjjW+iBjDGtoRff1cB7rVlGDKbnFm4IzWBeAW6cNs7vAu8XETvwDuDHgAfwAveLyB8CozGOQy1yGgzUYvA1gu+6LwP+BMgDMMZ8AfgAwXfhz4rI+rkf4kKhGcFhgjOKs4QOFQmpCd0Wjq1Ao4jkAf8HuD00zu9Y4yTYefINwG3Ay8aY3lDg2gH8v9Dtvwl37ErNRoOBWgxKOP/ibHV+RERWGWNeMcb8C/ASweWaIaBotgcRkRoRyQ997ibYO/546NCRQRG5KlRF9B5CXSXnIyJvA14H/ITzL/znQj3ob7euM8Z4gd8STFL/R+h7C4GSUOOxTwCbw/lBKDUXPdxGZRuXiExfv/8y8PfAf4lIP/B7YEXovo+HDgUJAEcInhIVAPwichD4/oy8wQbgS6GDRQT4ojHmldB9Hwa+T3CW8QiznDgV8gkR+SOggODM4kZjTA+AiHwndFsnweA03Y8I5ggeDX1dBPxPaEYhwN0L/FyUmpd2LVUqA4jIXxCcCfxdqseispPODJRKcyLy38AqgkllpRJCZwZKKaU0gayUUkqDgVJKKTQYKKWUQoOBUkopNBgopZRCg4FSSing/wcHnC4nqoHYjAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "walking_distance_day.last('30D').plot(ylabel='Km',xlabel='Last 30 Days',label='Distance')\n",
    "L=plt.legend()\n",
    "L.get_texts()[0].set_text('Distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining related data to assess mobility\n",
    "\n",
    "https://www.apple.com/ca/healthcare/docs/site/Measuring_Walking_Quality_Through_iPhone_Mobility_Metrics.pdf\n",
    "\n",
    "## Hypothesis 1, the total distance walked in a day impacts the walking assimetry\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the same principles to get the data for walking asymmetry.\n",
    "We will try to follow these steps\n",
    "1. Get walking asymmetry\n",
    "2. Plot the last 30 days\n",
    "3. Merge Walking distance + Walking asymmetry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sonic Screwdriver', 'SonicScrewdriver'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "walking_asymmetry = data.loc[data['type'] == 'HKQuantityTypeIdentifierWalkingAsymmetryPercentage']\n",
    "walking_asymmetry_day=walking_asymmetry.groupby(pd.Grouper(key='startDate', freq='D')).median()\n",
    "walking_asymmetry['sourceName'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, `sourceName` only contains values from the iPhone and we don't need to filter that data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "walking_asymmetry_day.last('30D').plot()\n",
    "L=plt.legend()\n",
    "L.get_texts()[0].set_text('Walking asymmetry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "walking_distance_day.rename(columns={'value':'Km'},inplace=True)\n",
    "walking_asymmetry_day.rename(columns={'value':'Walking asymmetry %'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='startDate', ylabel='%'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=walking_asymmetry_day.last('30D').plot(secondary_y=True,xlabel='Last 30 Days')\n",
    "walking_distance_day.last('30D').plot(ax=ax,ylabel='%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis 2, exercise minutes reduce walking assymetry the next day or in the current day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='startDate', ylabel='%'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exercise_minutes_day = data.loc[data['type'] == 'HKQuantityTypeIdentifierAppleExerciseTime'][['startDate','endDate','sourceName','unit','value']].groupby(pd.Grouper(key='startDate', freq='D')).sum().last('30D')\n",
    "exercise_minutes_day.rename(columns={'value':'Exercise mns'}, inplace=True)\n",
    "ax=walking_asymmetry_day.last('30D').plot(secondary_y=True,xlabel='Last 30 Days')\n",
    "exercise_minutes_day.last('30D').plot(ax=ax,ylabel='%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Hypothesis 3, distance cycled decreases walking assymetry \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='startDate', ylabel='%'>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cycle_distance = data.loc[data['type'] == 'HKQuantityTypeIdentifierDistanceCycling'][['startDate','endDate','sourceName','unit','value']].groupby(pd.Grouper(key='startDate', freq='D')).sum().last('30D')\n",
    "cycle_distance.rename(columns={'value':'Distance cycled'}, inplace=True)\n",
    "ax=walking_asymmetry_day.last('30D').plot(secondary_y=True,xlabel='Last 30 Days')\n",
    "cycle_distance.last('30D').plot(ax=ax,ylabel='%')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "interpreter": {
   "hash": "0277b96b14c425842615228913045a7658c691e0d3917c6c6b1fa9e121a8c5d3"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}