Bob Troia btroia

## 72-hour-water-fast-data.txt
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


## gist:9dd19781ca8943a1673f
{
  "metrics": {
    "air_temp": {
      "values": [
        82.8,
        82.8,
        82.8,
        82.8,
        82.8,
        82.8,

## gist:bb3002a9d26750c2602b
{
  "content": {
    "activities": [
      {
        "heart_rate": {
          "max": null,
          "avg": null,
          "min": null
        },
        "start_time": {

## sleep-analysis.py
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()

## gist:df1e6454f296ba6173b2
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

## gist:48368b0ab2b260559bf4
db.sleeps.aggregate( [
	{ $unwind : "$content.activities" },
	{ $project :
		{
		"content.activities.date" : 1 ,
		"content.activities.sleep" : 1,
		"content.activities.start_time" : 1
		}
	},
	{ "$group": {

## basis-sleep-mongodb-import.py
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"

## gist:31d54b9785bb0750e8d0
{
  "content": {
    "activities": [
      {
        "heart_rate": {
          "max": null,
          "avg": 47.91773778920309,
          "min": null
        },
        "start_time": {
	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
	{
	"metrics": {
	"air_temp": {
	"values": [
	82.8,
	82.8,
	82.8,
	82.8,
	82.8,
	82.8,
	{
	"content": {
	"activities": [
	{
	"heart_rate": {
	"max": null,
	"avg": null,
	"min": null
	},
	"start_time": {
	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()
	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
	db.sleeps.aggregate( [
	{ $unwind : "$content.activities" },
	{ $project :
	{
	"content.activities.date" : 1 ,
	"content.activities.sleep" : 1,
	"content.activities.start_time" : 1
	}
	},
	{ "$group": {
	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"