PYTHON SAYS, SIMON'S HIPSTER BROTHER

Many of you may remember playing with a Simon Electronic Memory Game when you were younger, you know something that looks like this: At it’s core the game is rather simple, the device lights up random colors, and you need to repeat the pattern. Of course it gets harder the longer you play.

I thought it would be fun to build a Simon game using Raspberry Pi and a few electronic components:

ARDUINO VALUES TO PYTHON OVER SERIAL

I’ve done a little bit of reading on the ReadAnalogVoltage of Arduino’s home page, and they give a straight forward way to read voltage from an analog pin.

I wanted to take this one step further and send the value over serial, then read it in Python using pySerial .

My setup is very straight forward, I have a Arduino UNO , a bread board, and a battery pack holding 4x AA batteries: To start out I want to merely print the voltage value in Arduino Studio to the serial console, my code looks something like this:

PYTHON AND SENTIMENT ANALYSIS

While looking for datasets to throw at sklearn , I came across UCI Sentiment Labelled Sentences Data Set.

UCI is providing us with positive / negative tagging on real world data, the data comes from three sources ( Amazon , Yelp , and IMDB ).

The only problem is the format is a little strange.. We have a .txt file for each source, this is a raw unstructured  formatting, plus not every line is tagged with sentiment.

TEST YOUR MACHINE LEARNING

In my previous post " Python Machine Learning with Presidential Tweets “, I started messing around with sklearn and text classification.

Since then I’ve discovered a great tutorial from SciPy 2015 . This video starts out slow enough for novices, and a reoccurring theme is testing your datasets.

After watching a good chunk of this video, I decided to go back to my code and implement a testing phase. Basically I’ll split my data into two pieces, a training set , and a testing set .

PYTHON MACHINE LEARNING WITH PRESIDENTIAL TWEETS

I’ve been spending a little bit of time researching Machine Learning , and was very happy to come across a Python library called sklearn .

While digging around Google, I came across a fantastic write up on Document Classification by Zac Steward . This article went pretty deep into writing a spam filter using machine learning, and sklearn. After reading the article I wanted to try some of the concepts, but had no interest in writing a spam filter.

SENSORTAG DATA MERGED WITH OPEN WEATHER MAPS

About a week ago I worked on SensorTag metrics .

This week had some interesting weather today here in Austin, and I wanted to see to visualize it as well. Luckily Open Weather Maps offers a free API for gather near real-time weather data based on city code.

def __get_open_weather_data():

  url_path = 'http://api.openweathermap.org/data/2.5/weather'
  api_key = '??????????'
  url = '%s?zip=73301&APPID=%s'

  res = requests.get(url % (url_path, api_key))
  if res:
    if res.json().get('main'):
      return res.json()

  res = requests.get(url % (url_path, api_key))
  if res:
    if res.json().get('main'):
      return res.json()

def get_open_weather():

  data = __get_open_weather_data()

  # format our json response
  temp = round(data['main']['temp'] * 9/5 - 459.67, 2)
  pressure = round(data['main']['pressure'], 1)
  humidity = round(data['main']['humidity'], 2)
  rain = round(data['rain'].get('1h', 0.00), 2)
  clouds = data['clouds']['all']
  wind = data['wind']['speed']

  return dict(
      open_weather_temperature=temp,
      open_weather_pressure=pressure,
      open_weather_humidity=humidity,
      open_weather_rain=rain,
      open_weather_clouds=clouds,
      open_weather_wind=wind
  )

Then I merge with my SensorTag data, appending these new keys to my json file:

ARDUINO MEET RASPBERRY PI

While at the electronics store the other day, I noticed they had motion detectors on sale for only $4. I decided with my latest obsession of electronic tinkering, picking up a OSEEP Passive Infrared Sensor (PIR) Module might be fun.

I guess I should have done a little more reading on the packaging; by the time I was home, I noticed this sensor reported in analog , not digital. This was an issue as the Raspberry Pi only reads digital input.

RASPBERRY PI AND OFFICIAL NFL SCORE BOARD API

Now that I’ve got my hands on a Raspberry Pi 3 Model B Motherboard , I decided nows a good time to play around with the 16x2 LCD Module Controller HD44780 I had laying around (I’ve had this thing since December 2015).

A live NFL (National Football League) score board seemed fitting as the season just started.

I found a really good write up on raspberrypi-spy.co.uk about wiring up the controller and Pi, here is the diagram I used:

SENSORTAG TEMPERATURE READINGS IN PYTHON

I wanted to wrap up my previous post TI SensorTag Temperature Readings ) with a little python example, thus this write is going to be short and sweet.

Using the bluepy python library (written by Ian Harvey) I’ve been able to to capture temperature readings, then covert them to Fahrenheit.

To demonstrate I captured a couple temperature samples, a few while sitting on my desk, and a few held up to my air condition vent:

TELEGRAF AND MISSING CPU INTERRUPTS

As I’ve been playing around with Telegraf and Grafana , I’ve noticed CPU interrupts and context switches are not apart of the standard metric gathering.

We know vmstat shows this information, and can be shown it in a easy processable list form:

$ vmstat -s
 1016888 K total memory
 497920 K used memory
 184412 K active memory
 173296 K inactive memory
 518968 K free memory
 70276 K buffer memory
 86416 K swap cache
 522236 K total swap
 88460 K used swap
 433776 K free swap
 18175 non-nice user cpu ticks
 0 nice user cpu ticks
 17799 system cpu ticks
 172172 idle cpu ticks
 7214 IO-wait cpu ticks
 0 IRQ cpu ticks
 2412 softirq cpu ticks
 0 stolen cpu ticks
 227755 pages paged in
 986944 pages paged out
 2353 pages swapped in
 121572 pages swapped out
 458705 interrupts
 1467529 CPU context switches
 1461773910 boot time
 3456 forks

I decided to hack together a little exec plugin for Telegraf using a Python script, running the script will get you standard out JSON: