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:

voltage_setup

To start out I want to merely print the voltage value in Arduino Studio to the serial console, my code looks something like this:

void setup() {
  // connect to serial
  Serial.begin(9600);
}

void loop() {

  // read value from analog pin
  int sensorValue = analogRead(A0);
 
  // convert to voltage and print to serial connection
  // https://www.arduino.cc/en/Tutorial/ReadAnalogVoltage
  float voltage = sensorValue * ( 5.0 / 1023.0 );
  Serial.println(voltage);

}

Now that we’ve verified this works, lets make a couple modification to the Arduino code.

Since the value of the analogRead may be over 255 (more than can fit in a single byte), we will need to send two bytes, a high byte, and a low byte. This concept is called most significant byte, and least significant byte.

void setup() {
  // connect to serial
  Serial.begin(9600);
}

void loop() {

  // read value from analog pin
  int sensorValue = analogRead(A0);
 
  // get the high and low byte from value
  byte high = highByte(sensorValue);
  byte low = lowByte(sensorValue);

  // write the high and low byte to serial
  Serial.write(high);
  Serial.write(low);

}

Then on the Python side we can use pySerial to read two bytes, and convert using the formula Arduino gave us.

import serial

# open our serial port at 9600 baud
dev = '/dev/cu.usbmodem1411'
with serial.Serial(dev, 9600, timeout=1) as ser:

  while True:

    # read 2 bytes from our serial connection
    raw = ser.read(size=2)

    if raw:

      # read the high and low byte
      high, low = raw

      # add up our bits from high and low byte
      # to get the final value
      val = ord(high) * 256 + ord(low)

      # print our voltage reading
      # https://www.arduino.cc/en/Tutorial/ReadAnalogVoltage
      print round(val * ( 5.0 / 1023.0), 2)

One thing to take into consideration is, if we do not have voltage sent to the analog pin the result will be random and invalid. You will see this in the video before I connect the battery pack. Keep in mind my battery pack is producing about 5 volts:

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.

I decided instead to write a predictor using Tweets as the learning source, and what better users than the US Presidential candidates!

Let me forewarn, this is merely using term frequencies, and n-grams on the tweets, and probably isn’t really useful or completely accurate, but hey, it could be fun, right? 🙂

In [1]: tweets = get_tweets('HillaryClinton')
In [2]: tweets = get_tweets('realDonaldTrump')

In [3]: h = get_data_frame('HillaryClinton')
In [4]: t = get_data_frame('realDonaldTrump')

In [5]: data = merge_data_frames(t, h)

A good baseline might be to predict on an actual tweet the candidate has posted:

screen-shot-2016-09-29-at-6-41-08-pm

In [1]: predict(data, 'The question in this election: Who can put the plans into action that will make your life better?')
('realDonaldTrump', 0.15506298409438407)
('HillaryClinton', 0.84493701590561299)

Alright that is an 84% to 15% prediction, pretty good.

screen-shot-2016-09-29-at-6-44-35-pm

In [1]: predict(data, 'I won every poll from last nights Presidential Debate - except for the little watched @CNN poll.')
('HillaryClinton', 0.069884565641135613)
('realDonaldTrump', 0.93011543435886102)

This prediction is giving a 93% to 6%, even better.

Now lets have a little fun by throwing in things we would assume, but the candidates did not post:

In [1]: predict(data, 'I have really big hands')
('HillaryClinton', 0.39802148371499757)
('realDonaldTrump', 0.60197851628500265)

In [2]: predict(data, 'I am for woman rights')
('realDonaldTrump', 0.3698772371039914)
('HillaryClinton', 0.63012276289600766)

We could also feed in some famous quotes:

screen-shot-2016-09-29-at-6-48-17-pm

In [1]: predict(data, "Two things are infinite: the universe and human stupidity; and I'm not sure about the universe.")
('realDonaldTrump', 0.28321206465202214)
('HillaryClinton', 0.71678793534798135)

screen-shot-2016-09-29-at-6-49-35-pm

In [1]: predict(data, 'A room without books is like a body without a soul.')
('realDonaldTrump', 0.39169158094239315)
('HillaryClinton', 0.60830841905760524)

Alright, so go have a look at the code, you can find it on my Github page.

Happy Hacking!

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.

Lucky for me, I also picked up an Arduino UNO Starter Kit awhile back. I decided this would be a great time to learn more about converting analog signals to digital (one great thing about the UNO is that it has both digital and analog input/output pins).

As an extra, I learned the Nexcon Solar Charger 5000mAh I bough for hiking and camping works great as a Raspberry Pi power source, in theory I can have a portable motion detector 😀

motion_1

motion_2

The wiring is rather basic, there is no need for resistors or capacitors, just direct connections.

* Connect motion sensor to the Adruino’s 5v power and ground.
* Connect motion sensor’s signal pin to Analog A0 pin on Adruino
* Connect Adruino’s Digital 2 pin to Raspberry Pi’s GPIO 18
* Connect Andruino’s ground to Raspberry Pi’s Ground

screen-shot-2016-09-24-at-2-21-03-pm

Once we are wired up, we can compile and upload the Arduino UNO code using Arduino Studio.

Arduino

/*
OSEPP Motion detector analog to digital convertor
http://nessy.info
*/

int analog = A0;
int digital = 2

void setup(){

 // set our digital pin to OUTPUT
 pinMode(digital, OUTPUT);
}

void loop()
{

 // read value from analog pin
 int analog_value = analogRead(analog);

 // send digital signal when motion detected
 if (analog_value > 0) {
   digitalWrite(digital, HIGH);
 } else {
   digitalWrite(digital, LOW);
 }

 delay(100); // slow down the loop just a bit
}

This Arduino code will read analog input from our motion detector, and any time more than 0v is detected it sends a signal to digital pin 2.

Raspberry Pi (Python)

import time
from datetime import datetime

import RPi.GPIO as GPIO

GPIO.setmode(GPIO.BCM)
GPIO.setup(18, GPIO.IN)

def detect():
  while True:
    if GPIO.input(18):
      print '[%s] Movement Detected!' % datetime.now().ctime()
    time.sleep(1)


detect()  # run movement detection

On the Raspberry Pi side we will listen for signal on GPIO pin 18, and print out a little message, and timestamp.

screen-shot-2016-09-24-at-1-42-55-pm

From here we can do all sort of things, Happy Hacking!