total_heartbeats = 2500000000
+total_heartbeats = 2500000000
# Import all the OAuth secret information from a local file
-from secrets import CLIENT_SECRET, CLIENT_ID, CALLBACK_URL
+# Import all the OAuth secret information from a local file
+from secrets import CLIENT_SECRET, CLIENT_ID, CALLBACK_URL
import urllib
+import urllib
FITBIT_URI = 'https://www.fitbit.com/oauth2/authorize'
params = {
@@ -211,7 +211,7 @@ Application: Thank you, now give me all the data.
In [6]:
-# The `response_url` variable contains the full URL that
+# The `response_url` variable contains the full URL that
# FitBit sent back to us, but most importantly,
# contains the token we need for authorization.
access_token = dict(urllib.parse.parse_qsl(response_url))['access_token']
@@ -238,9 +238,9 @@ Application: Thank you, now give me all the data.
In [7]:
-from requests_oauthlib import OAuth2Session
-import pandas as pd
-from datetime import datetime
+from requests_oauthlib import OAuth2Session
+import pandas as pd
+from datetime import datetime
session = OAuth2Session(token={
'access_token': access_token,
@@ -252,12 +252,12 @@ Application: Thank you, now give me all the data.
end_date = datetime(2016, 3, 31)
dr = pd.date_range(start_date, end_date)
-url = 'https://api.fitbit.com/1/user/-/activities/heart/date/{0}/1d/1min.json'
+url = 'https://api.fitbit.com/1/user/-/activities/heart/date/{0}/1d/1min.json'
hr_responses = [session.get(url.format(d.strftime(format_str))) for d in dr]
def record_to_df(record):
if 'activities-heart' not in record:
- return None
+ return None
date_str = record['activities-heart'][0]['dateTime']
df = pd.DataFrame(record['activities-heart-intraday']['dataset'])
@@ -275,7 +275,7 @@ Application: Thank you, now give me all the data.
freq='min')
hr_df_full = hr_df_concat.reindex(full_daterange, method='nearest')
-print("Heartbeats from {} to {}: {}".format(hr_df_full.index[0],
+print("Heartbeats from {} to {}: {}".format(hr_df_full.index[0],
hr_df_full.index[-1],
hr_df_full['value'].sum()))
@@ -325,7 +325,7 @@ hr_q \cdot n - hr_d \cdot (n-m)
In [8]:
-quarterly_count = hr_df_full['value'].sum()
+quarterly_count = hr_df_full['value'].sum()
leap_day_count = hr_df_full[(hr_df_full.index.month == 2) &
(hr_df_full.index.day == 29)]['value'].sum()
num_quarters = 96
@@ -372,7 +372,7 @@ hr_q \cdot n - hr_d \cdot (n-m)
In [9]:
-minute_mean = hr_df_full['value'].mean()
+minute_mean = hr_df_full['value'].mean()
# Don't you wish you knew?
# birthday_minutes = ???
@@ -381,8 +381,8 @@ hr_q \cdot n - hr_d \cdot (n-m)
heartbeats_until_2016 = int(birthday_heartbeats + jan_92_jan_16)
remaining_2016 = total_heartbeats - heartbeats_until_2016
-print("Heartbeats so far: {}".format(heartbeats_until_2016))
-print("Remaining heartbeats: {}".format(remaining_2016))
+print("Heartbeats so far: {}".format(heartbeats_until_2016))
+print("Remaining heartbeats: {}".format(remaining_2016))
@@ -430,7 +430,7 @@ hr_q \cdot n - hr_d \cdot (n - m)
In [10]:
-heartbeats_4year = quarterly_count * 16 - leap_day_count * (16 - 1)
+heartbeats_4year = quarterly_count * 16 - leap_day_count * (16 - 1)
heartbeats_4year
@@ -470,11 +470,11 @@ hr_q \cdot n - hr_d \cdot (n - m)
In [11]:
-four_year_periods = remaining_2016 // heartbeats_4year
+four_year_periods = remaining_2016 // heartbeats_4year
remaining_4y = remaining_2016 - four_year_periods * heartbeats_4year
-print("Four year periods remaining: {}".format(four_year_periods))
-print("Remaining heartbeats after 4 year periods: {}".format(remaining_4y))
+print("Four year periods remaining: {}".format(four_year_periods))
+print("Remaining heartbeats after 4 year periods: {}".format(remaining_4y))
@@ -509,18 +509,18 @@ Remaining heartbeats after 4 year periods: 48041640
-In [13]:
+In [12]:
-remaining_leap = remaining_4y - leap_day_count
+remaining_leap = remaining_4y - leap_day_count
# Ignore leap day in the data set
heartbeats_quarter = hr_df_full[(hr_df_full.index.month != 2) &
- (hr_df_full.index.day != 29)]['value'].sum() * 4
+ (hr_df_full.index.day != 29)]['value'].sum()
quarters_left = remaining_leap // heartbeats_quarter
remaining_year = remaining_leap - quarters_left * heartbeats_quarter
-print("Quarters left starting 2068: {}".format(quarters_left))
-print("Remaining heartbeats after that: {}".format(remaining_year))
+print("Quarters left starting 2068: {}".format(quarters_left))
+print("Remaining heartbeats after that: {}".format(remaining_year))
@@ -533,7 +533,7 @@ Remaining heartbeats after 4 year periods: 48041640
@@ -548,19 +548,19 @@ Remaining heartbeats after that: 4760716
-So, that analysis gets me through the 2nd quarter in 2068, specifically to June 1st, 2068. Final step, using that minute estimate to figure out how many minutes past that I'm predicted to have:
+So, that analysis gets me through until January 1st 2070. Final step, using that minute estimate to figure out how many minutes past that I'm predicted to have:
-In [14]:
+In [13]:
-from datetime import timedelta
+from datetime import timedelta
-base = datetime(2068, 7, 1)
+base = datetime(2070, 1, 1)
minutes_left = remaining_year // minute_mean
kaput = timedelta(minutes=minutes_left)
@@ -575,11 +575,11 @@ Remaining heartbeats after that: 4760716
-Out[14]:
+Out[13]:
@@ -593,8 +593,8 @@ Remaining heartbeats after that: 4760716
-According to this, I've got until August 23rd, 2069 at 5:28 PM in the evening before my heart gives out.
-Summary¶
Well, that's kind of a creepy date to know. As I said at the top though, this number is totally useless in any medical context. It ignores the rate at which we continue to get better at making people live longer, and is extrapolating from 3 months' worth of data the rest of my life.
+According to this, I've got until February 23rd, 2070 at 5:28 PM in the evening before my heart gives out.
+Summary¶
Well, that's kind of a creepy date to know. As I said at the top though, this number is totally useless in any medical context. It ignores the rate at which we continue to get better at making people live longer, and is extrapolating from 3 months' worth of data the rest of my life. Additionally, throughout my time developing this post I made many minor mistakes. I think they're all fixed now, but it's easy to mix a number up here or there and the analysis gets thrown off by a couple years.
Even still, I think philosophically humans have a desire to know how much time we have left in the world. Man is but a breath, and it's scary to think just how quickly that date may be coming up. This analysis asks an important question though: what are you going to do with the time you have left?
Thanks for sticking with me on this one, I promise it will be much less depressing next time!