{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What does this notebook do?**\n",
    "- Load the exported CGM values from NutriSense\n",
    "- Print out what days are included in the dataset\n",
    "- Pair down data to only one day, include CGM values, meals and exercise\n",
    "- Smooth CGM data and interpolate missing values\n",
    "- Pull in Garmin step information and \"run activities\" and plot them\n",
    "- Calculate key metrics for that day, both glucose and steps\n",
    "- Create a chart of the glucose values and include metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import datetime\n",
    "from datetime import date\n",
    "from garminconnect import (\n",
    "    Garmin,\n",
    "    GarminConnectConnectionError,\n",
    "    GarminConnectTooManyRequestsError,\n",
    "    GarminConnectAuthenticationError,\n",
    ")\n",
    "\n",
    "# Read in CSV file\n",
    "df = pd.read_csv('export.csv')\n",
    "\n",
    "# Remove \"time zone offset\" from \"occurred_at\" column and add new \"occurred_at_day\" column\n",
    "df['occurred_at_day'] = df['occurred_at'].apply(lambda x: x[:len(x) - 15])\n",
    "df['occurred_at'] = df['occurred_at'].apply(lambda x: x[:len(x) - 6])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print all days with data\n",
    "daysWithData = df['occurred_at_day'].unique()\n",
    "print(daysWithData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter down to one day, pick the second day in the dataset\n",
    "df = df[df['occurred_at_day']==daysWithData[2]]\n",
    "day = daysWithData[2]\n",
    "\n",
    "# Create a datasets just with glucose measurments\n",
    "gm = df[df['class']=='GlucoseMeasurement']\n",
    "\n",
    "# Create a dataset for meals and exercise, sort it\n",
    "mealsExercise = df[((df['class']=='Meal') | (df['class']=='ExerciseActivity') )]\n",
    "mealsExerciseSorted = mealsExercise.sort_values(by=[\"occurred_at\"], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Garmin Data\n",
    "# This may not be so great, defaulting to simply retrieving the last 100 activities on Garmin.\n",
    "# If the day that is plotted is further in the past, this may not work.\n",
    "numberOfActivities = 100\n",
    "try:\n",
    "    # Initialize Garmin client with credentials\n",
    "    # Put your userID and password for https://connect.garmin.com/ here\n",
    "    client = Garmin(\"USERID\", \"PASSWORD\")\n",
    "    # Login to Garmin Connect portal\n",
    "    client.login()\n",
    "    # Get running activities\n",
    "    allActivities = client.get_activities(0,numberOfActivities) # 0=start, numberOfActivities=limit\n",
    "    dayOfInterest = datetime.datetime.strptime(day, '%Y-%m-%d').date()\n",
    "    allDayStepData = client.get_steps_data(dayOfInterest.isoformat())\n",
    "except (GarminConnectConnectionError, GarminConnectAuthenticationError, GarminConnectTooManyRequestsError,) as err:\n",
    "    print(\"Error occured during Garmin Connect Client init: %s\" % err)\n",
    "    quit()\n",
    "except Exception:\n",
    "    print(\"Unknown error occured during Garmin Connect Client init.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert garmin data in list form to Pandas dataframe\n",
    "dfGarmin = pd.DataFrame.from_dict(allDayStepData)\n",
    "\n",
    "# manipulate start time so that it is local (And not GMT)\n",
    "dfGarmin['time'] = dfGarmin['startGMT'].apply(lambda x: x[:len(x) - 5])\n",
    "dfGarmin['time'] = dfGarmin['time'].apply(lambda x: x[11:])\n",
    "offset = dfGarmin[\"time\"][0]\n",
    "offsetHour = int(offset.split(':')[0])\n",
    "offsetMinutes=int(offset.split(':')[1])\n",
    "dfGarmin['time'] = pd.to_datetime(dfGarmin['startGMT'])\n",
    "dfGarmin['time'] = dfGarmin['time'].apply(lambda x: x - datetime.timedelta(hours=offsetHour, minutes=offsetMinutes))\n",
    "dfGarmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just for exploring the data, lets look at all 15 minute segments that have non-zero steps\n",
    "dfGarmin = dfGarmin[dfGarmin.steps != 0]\n",
    "print(dfGarmin[['time', 'steps', 'primaryActivityLevel']])\n",
    "#dfGarmin.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataset with just 2 columns\n",
    "gm_data = gm.filter(['occurred_at', 'value'])\n",
    "\n",
    "# rename the columns for easier readability\n",
    "gm_data.columns = ['time', 'value']\n",
    "\n",
    "# turn time column into the index and delete time column\n",
    "gm_data['time']= pd.to_datetime(gm_data['time'])\n",
    "gm_data.index = gm_data['time']\n",
    "del gm_data['time']\n",
    "\n",
    "gm_data = gm_data.resample('1T').mean() # add rows for every 1 minute\n",
    "gm_data = gm_data.interpolate(method='cubic') # interpolate the new 1 minute points with data\n",
    "\n",
    "# Calculate a few metrics\n",
    "threshold = 120  # this is an arbitrary threshold\n",
    "above = gm_data[gm_data['value'] > threshold] # create a dataset with glucose measuremnts over threshold\n",
    "minutesAboveThreshold = above.count()\n",
    "print('Number of minutes above '+str(threshold)+': '+ minutesAboveThreshold.to_string(index=False))\n",
    "\n",
    "percentageAboveThreshold = int(round(minutesAboveThreshold/(60*24)*100,0))\n",
    "print(\"Time above Threshold = \"+str(percentageAboveThreshold)+\"%\")\n",
    "\n",
    "averageGlucose = int(round(gm_data['value'].mean()))\n",
    "medianGlucose = int(round(gm_data['value'].median()))\n",
    "print(\"Average Glucose  = \"+str(averageGlucose))\n",
    "print(\"Median Glucose   = \"+str(medianGlucose))\n",
    "\n",
    "# Calculate statistics on the Garmin data\n",
    "numberOfRunningActivitiesToday = 0\n",
    "numberOfActivitiesToday = 0\n",
    "for i in range(numberOfActivities):\n",
    "    activity = allActivities[i]\n",
    "    activityDateTime = activity['startTimeLocal']\n",
    "    activityDate = datetime.datetime.strptime(activityDateTime, \"%Y-%m-%d %H:%M:%S\")\n",
    "    if str(activityDate.date()) == day:\n",
    "        numberOfActivitiesToday = numberOfActivitiesToday + 1\n",
    "        if activity[\"activityType\"][\"typeKey\"] == \"running\":\n",
    "            numberOfRunningActivitiesToday = numberOfRunningActivitiesToday + 1\n",
    "\n",
    "print(\"Steps today      = \"+str(dfGarmin.steps.sum()))\n",
    "print(\"Activities today = \"+str(numberOfActivitiesToday))\n",
    "print(\"Runs today       = \"+str(numberOfRunningActivitiesToday))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using subplots here to easily get a secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "# first add the glucose measurement data\n",
    "fig.add_trace( go.Scatter(x=gm_data.index, y=gm_data.value, mode='lines',line=dict(color=\"purple\")))\n",
    "\n",
    "# add meals and exercise to the chart\n",
    "yText = 145\n",
    "eventColor = \"green\"\n",
    "for index, row in mealsExerciseSorted.iterrows():\n",
    "\n",
    "    # If the activity has \"run\" in the description, don't use it as it is a duplicate from Garmin\n",
    "    if \"run\" in row['description']: continue\n",
    "\n",
    "    # Convert the time in pandas to something that we can use as an index for the x-axis placement\n",
    "    time = datetime.datetime.strptime(row['occurred_at'], '%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "    # Pick a different color depending on the event\n",
    "    if (row['class'] == \"Meal\"): eventColor = \"black\"\n",
    "    else: eventColor = \"green\"\n",
    "\n",
    "    # draw a vertical line at the time of the meal/exercise\n",
    "    fig.add_shape(type=\"line\", xref=\"x\", yref=\"y\", x0=time, y0=70, x1=time , y1=140, line_color=eventColor,)\n",
    "    \n",
    "    # Alternate text placement so adjacent text doesn't overlap\n",
    "    if (yText == 145): yText = 153\n",
    "    else: yText = 145\n",
    "    \n",
    "    # Add text\n",
    "    fig.add_annotation(text=row['description'], xref=\"x\", yref=\"y\", x=time, y=yText, showarrow=False, font=dict(color=eventColor))\n",
    "\n",
    "# Add Garmin running activities\n",
    "for i in range(numberOfActivities):\n",
    "    activity = allActivities[i]\n",
    "    # only activities that are of type \"running\"\n",
    "    if activity[\"activityType\"][\"typeKey\"] == \"running\":\n",
    "        activityDateTime = activity['startTimeLocal']\n",
    "        activityDate = datetime.datetime.strptime(activityDateTime, \"%Y-%m-%d %H:%M:%S\")\n",
    "        if str(activityDate.date()) == day:\n",
    "            # draw a vertical line at the time of the running activity\n",
    "            fig.add_shape(type=\"line\", xref=\"x\", yref=\"y\", x0=activityDateTime, y0=70, x1=activityDateTime , y1=140, line_color=\"green\",)\n",
    "            # Add text... yes this is specific to kilometers. This may need changes for miles.\n",
    "            textDescr = str(activity['activityName']) + \" \" + str(int(round(activity['distance']/1000))) + \"K run\"\n",
    "            fig.add_annotation(text=textDescr, xref=\"x\", yref=\"y\", x=activityDateTime, y=145, showarrow=False, font=dict(color=\"green\"))\n",
    "\n",
    "\n",
    "\n",
    "# Draw a line at the threshold\n",
    "fig.add_shape(type=\"line\", xref=\"x\", yref=\"y\",\n",
    "    x0=gm_data.index[0], y0=threshold, x1=gm_data.index.max(), y1=threshold, line_color=\"red\",)\n",
    "\n",
    "# Show text box with summary values\n",
    "fig.add_annotation(\n",
    "                text='Glucose Threshold = '+str(threshold)+\n",
    "                '<br>Minutes above Threshold = '+str(int(round(minutesAboveThreshold,0)))+\n",
    "                '<br>Time above Threshold = '+str(percentageAboveThreshold)+\"%\"+\n",
    "                '<br>Average Glucose = '+str(averageGlucose)+\n",
    "                '<br>Median Glucose = '+str(medianGlucose)+\n",
    "                '<br>Steps Today = '+str(dfGarmin.steps.sum()),\n",
    "                align='right', showarrow=False,\n",
    "                xref='paper', yref='paper', x=0.002, y=0.005,\n",
    "                bordercolor='black', borderwidth=1\n",
    "            )\n",
    "\n",
    "# Setting primary and secondary y axis titles and ticks\n",
    "fig.update_layout(yaxis = dict(range=[0, 160], tick0=0, dtick=20, title_text='mg/dL'),yaxis2=dict(tick0=0, dtick=500, range=[0,4000], title_text='steps'))\n",
    "# Adding step data to the chart, using the secondary y axis\n",
    "fig.add_trace( go.Bar(x=dfGarmin.time, y=dfGarmin.steps), secondary_y=True)\n",
    "\n",
    "# Set x axis title\n",
    "fig.update_xaxes(title_text=str(day), tickformat='%H:%M')\n",
    "# Hide the legend\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}