What does this notebook do?

• Load the exported CGM values from NutriSense
• Print out what days are included in the dataset
• Pair down data to only one day, include CGM values, meals and exercise
• Smooth CGM data and interpolate missing values
• Calculate key metrics for that day
• Pull in Garmin "run activities" and plot them
• Create a chart of the glucose values and include metrics

import pandas as pd
import plotly.express as px
import datetime
from datetime import date
from garminconnect import (
    Garmin,
    GarminConnectConnectionError,
    GarminConnectTooManyRequestsError,
    GarminConnectAuthenticationError,
)

# Read in CSV file
df = pd.read_csv('export.csv')

# Remove "time zone offset" from "occurred_at" column and add new "occurred_at_day" column
df['occurred_at_day'] = df['occurred_at'].apply(lambda x: x[:len(x) - 15])
df['occurred_at'] = df['occurred_at'].apply(lambda x: x[:len(x) - 6])
df.head()

	class	value	time	length	photo_url	description	occurred_at	body	updated_at	started_at	ended_at	created_by	occurred_at_day
0	GlucoseMeasurement	100.0	NaN	NaN	NaN	NaN	2021-08-15 23:48:06	NaN	NaN	NaN	NaN	NaN	2021-08-15
1	GlucoseMeasurement	99.0	NaN	NaN	NaN	NaN	2021-08-15 23:33:06	NaN	NaN	NaN	NaN	NaN	2021-08-15
2	GlucoseMeasurement	99.0	NaN	NaN	NaN	NaN	2021-08-15 23:18:06	NaN	NaN	NaN	NaN	NaN	2021-08-15
3	GlucoseMeasurement	98.0	NaN	NaN	NaN	NaN	2021-08-15 23:03:06	NaN	NaN	NaN	NaN	NaN	2021-08-15
4	GlucoseMeasurement	97.0	NaN	NaN	NaN	NaN	2021-08-15 22:48:06	NaN	NaN	NaN	NaN	NaN	2021-08-15

# Print all days with data
daysWithData = df['occurred_at_day'].unique()
print(daysWithData)

['2021-08-15' '2021-08-14' '2021-08-13' '2021-08-12' '2021-08-11'
 '2021-08-10' '2021-08-09' '2021-08-08' '2021-08-07' '2021-08-06'
 '2021-08-05']

# Filter down to one day, pick the second day in the dataset
df = df[df['occurred_at_day']==daysWithData[2]]
day = daysWithData[2]

# Create a datasets just with glucose measurments
gm = df[df['class']=='GlucoseMeasurement']

# Create a dataset for meals and exercise, sort it
mealsExercise = df[((df['class']=='Meal') | (df['class']=='ExerciseActivity') )]
mealsExerciseSorted = mealsExercise.sort_values(by=["occurred_at"], ascending=True)

# Create a dataset with just 2 columns
gm_data = gm.filter(['occurred_at', 'value'])

# rename the columns for easier readability
gm_data.columns = ['time', 'value']

# turn time column into the index and delete time column
gm_data['time']= pd.to_datetime(gm_data['time'])
gm_data.index = gm_data['time']
del gm_data['time']

gm_data = gm_data.resample('1T').mean() # add rows for every 1 minute
gm_data = gm_data.interpolate(method='cubic') # interpolate the new 1 minute points with data

# Calculate a few metrics
threshold = 120  # this is an arbitrary threshold
above = gm_data[gm_data['value'] > threshold] # create a dataset with glucose measuremnts over threshold
minutesAboveThreshold = above.count()
print('Number of minutes above '+str(threshold)+': '+ minutesAboveThreshold.to_string(index=False))

percentageAboveThreshold = int(round(minutesAboveThreshold/(60*24)*100,0))
print("Time above Threshold = "+str(percentageAboveThreshold)+"%")

averageGlucose = int(round(gm_data['value'].mean()))
medianGlucose = int(round(gm_data['value'].median()))
print("Average Glucose = "+str(averageGlucose))
print("Median Glucose  = "+str(medianGlucose))

Number of minutes above 120: 74
Time above Threshold = 5%
Average Glucose = 94
Median Glucose  = 91

# Get Garmin Data
# This may not be so great, defaulting to simply retrieving the last 100 activities on Garmin.
# If the day that is plotted is further in the past, this may not work.
numberOfActivities = 100
try:
    # Initialize Garmin client with credentials
    # Put your userID and password for https://connect.garmin.com/ here
    client = Garmin("USERID", "PASSWORD")
    # Login to Garmin Connect portal
    client.login()
    # Get running activities
    allActivities = client.get_activities(0,numberOfActivities) # 0=start, numberOfActivities=limit
except (GarminConnectConnectionError, GarminConnectAuthenticationError, GarminConnectTooManyRequestsError,) as err:
    print("Error occured during Garmin Connect Client init: %s" % err)
    quit()
except Exception:
    print("Unknown error occured during Garmin Connect Client init.")

fig = px.line(gm_data, y="value")

# add meals and exercise to the chart
yText = 55
eventColor = "green"
for index, row in mealsExerciseSorted.iterrows():

    # If the activity has "run" in the description, don't use it as it is a duplicate from Garmin
    if "run" in row['description']: continue

    # Convert the time in pandas to something that we can use as an index for the x-axis placement
    time = datetime.datetime.strptime(row['occurred_at'], '%Y-%m-%d %H:%M:%S')

    # Pick a different color depending on the event
    if (row['class'] == "Meal"): eventColor = "black"
    else: eventColor = "green"

    # draw a vertical line at the time of the meal/exercise
    fig.add_shape(type="line", xref="x", yref="y", x0=time, y0=70, x1=time , y1=140, line_color=eventColor,)
    
    # Alternate text placement so adjacent text doesn't overlap
    if (yText == 55): yText = 60
    else: yText = 55
    
    # Add text
    fig.add_annotation(text=row['description'], xref="x", yref="y", x=time, y=yText, showarrow=False, font=dict(color=eventColor))

# Add Garmin running activities
for i in range(numberOfActivities):
    activity = allActivities[i]
    # only activities that are of type "running"
    if activity["activityType"]["typeKey"] == "running":
        activityDateTime = activity['startTimeLocal']
        activityDate = datetime.datetime.strptime(activityDateTime, "%Y-%m-%d %H:%M:%S")
        if str(activityDate.date()) == day:
            # draw a vertical line at the time of the running activity
            fig.add_shape(type="line", xref="x", yref="y", x0=activityDateTime, y0=70, x1=activityDateTime , y1=140, line_color="green",)
            # Add text
            textDescr = str(activity['activityName']) + " " + str(int(round(activity['distance']/1000))) + "K run"
            fig.add_annotation(text=textDescr, xref="x", yref="y", x=activityDateTime, y=65, showarrow=False, font=dict(color="green"))



# Draw a line at the threshold
fig.add_shape(type="line", xref="x", yref="y",
    x0=gm_data.index[0], y0=threshold, x1=gm_data.index.max(), y1=threshold, line_color="red",)

# Show text box with summary values
fig.add_annotation(
                text='Threshold = '+str(threshold)+
                '<br>Minutes above Threshold = '+str(int(round(minutesAboveThreshold,0)))+
                '<br>Time above Threshold = '+str(percentageAboveThreshold)+"%"+
                '<br>Average Glucose = '+str(averageGlucose)+
                '<br>Median Glucose = '+str(medianGlucose),
                align='right', showarrow=False,
                xref='paper', yref='paper', x=0.002, y=0.005,
                bordercolor='black', borderwidth=1
            )

# Set x and y axis title and unit
fig.update_xaxes(title_text=str(day), tickformat='%H:%M')
fig.update_yaxes(title_text='mg/dL')
fig.show()