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{ | |
"content": { | |
"activities": [ | |
{ | |
"heart_rate": { | |
"max": null, | |
"avg": 47.91773778920309, | |
"min": null | |
}, | |
"start_time": { |
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from __future__ import print_function | |
import pymongo | |
import glob | |
import os | |
import json | |
# Define mongoDB database and collection to store sleep data | |
db_name = "mydb" | |
collection_name = "mysleeps" |
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db.sleeps.aggregate( [ | |
{ $unwind : "$content.activities" }, | |
{ $project : | |
{ | |
"content.activities.date" : 1 , | |
"content.activities.sleep" : 1, | |
"content.activities.start_time" : 1 | |
} | |
}, | |
{ "$group": { |
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mongoexport \ | |
--collection sleepexport --type=csv --fields _id,start_time,offset,light_minutes,rem_minutes,deep_minutes,interruption_minutes,unknown_minutes \ | |
--out mysleepdata.csv --db mydb |
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import json | |
import datetime as dt | |
from pprint import pprint | |
# change this to the JSON file you want to analyze | |
json_data = open('data/basis-data-2014-09-15-sleep.json') | |
data = json.load(json_data) | |
json_data.close() |
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{ | |
"content": { | |
"activities": [ | |
{ | |
"heart_rate": { | |
"max": null, | |
"avg": null, | |
"min": null | |
}, | |
"start_time": { |
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{ | |
"metrics": { | |
"air_temp": { | |
"values": [ | |
82.8, | |
82.8, | |
82.8, | |
82.8, | |
82.8, | |
82.8, |
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Time (hours) Glucose (mg/dL) Glucose (mmol/L) Ketones (mmol/L) GKI Weight (lbs) | |
0 87 4.8 0.2 24.2 168.9 | |
11.25 97 5.4 0.6 9.0 | |
23.75 92 5.1 0.6 8.5 165.0 | |
32.25 90 5.0 1.0 5.0 | |
45.92 68 3.8 3.2 1.2 164.4 | |
59.58 70 3.9 3.1 1.3 | |
72.0 70 3.9 4.7 0.8 162.7 | |