{
"cells": [
{
"cell_type": "markdown",
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"source": [
"# Real-world data in a few minutes with Google Science Journal\n",
"\n",
"**Author:** [Fernando Perez](https://github.com/fperez) ([@fperez_org](https://twitter.com/fperez_org)).\n",
"\n",
"Using an Android phone and Google's new [Science Journal](https://makingscience.withgoogle.com/science-journal/activities) app, it's very easy to collect real-world data. \n",
"\n",
"The first thing I did was to try their basic light example, using the phone's light sensor. I recorded for a few seconds moving my phone under a lamp, this is what the app shows:\n",
"\n",
"\n",
" \n",
"Let's see how we can get a bit more detail with just a few lines of code.\n",
" \n",
"From within the app, it takes just a couple of taps to export the data to a CSV file and put it on dropbox, from where we can pick it up with the computer and continue our experiments here."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a quick peek at the data, it's always a good idea to do this, to know how to best load it:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"timestamp,value\r\n",
"1463893627134,10.0\r\n",
"1463893627209,12.0\r\n",
"1463893627238,12.0\r\n",
"1463893627340,12.0\r\n",
"1463893627441,12.0\r\n",
"1463893627542,12.0\r\n",
"1463893627643,12.0\r\n",
"1463893627743,12.0\r\n",
"1463893627817,10.0\r\n"
]
}
],
"source": [
"fname = \"Untitled Experiment Trial 1.csv\"\n",
"!head \"$fname\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, the timestamps look like miliseconds from the epoch. \n",
"\n",
"For convenience, we'll subtract the first timestamp from all of them to get 0-offset data (basically setting our \"laboratory clock\" at $t=0$ for data collection). This is done by the `tc` function below, applied to column 0 in `converters`.\n",
"\n",
"We can also ask Pandas to use the time directly as our data index (`index_col=0`) and we'll name the columns ourselves, by providing the column names and asking it to skip the header (`header=0`):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
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"\n",
"
\n",
" \n",
" \n",
" | \n",
" lux | \n",
"
\n",
" \n",
" | time | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | 75 | \n",
" 12.0 | \n",
"
\n",
" \n",
" | 104 | \n",
" 12.0 | \n",
"
\n",
" \n",
" | 206 | \n",
" 12.0 | \n",
"
\n",
" \n",
" | 307 | \n",
" 12.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" lux | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 146.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 1604.424658 | \n",
"
\n",
" \n",
" | std | \n",
" 6590.471272 | \n",
"
\n",
" \n",
" | min | \n",
" 6.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 8.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 12.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 23.000000 | \n",
"
\n",
" \n",
" | max | \n",
" 32767.000000 | \n",
"
\n",
" \n",
"
\n",
"