The goal of this project was to make a very simple python script that runs on a Raspberry Pi and collects data from one or more Thunderboard Sense devices, using the same Google Firebase backend and web application that the official app uses.
To get started, go to https://github.com/siliconlabs/thundercloud, and clone the repository. Follow the instructions and set up a firebase account and project, replacing the database name in src/main.js as instructed. For this example, I have not yet gotten authentication working, so change the database.rules.json for now to allow anyone to write (basically replacing "auth.uid === 'YsGcsiI8hkwjImSrr25uZdqlNw3Qkgi8vUWx9MU6'": with "true")
In the Firebase console you should now see your deployed rules, as well as the application url. Following the URL should bring you to the default page:
Now the next step is to log onto the Raspberry Pi, and install the necessary tools. The script uses bluepy to communicate with the sensor, and python-firebase to push data to the cloud. I did have some trouble installing python-firebase because of the specific version of the requests library, but eventually got it installed.
Remember to put in your own database URL in the thundercloud.py script:
from firebase import firebase
import uuid
import time
class Thundercloud:
def __init__(self):
self.addr = 'https://-- Your database URL --.firebaseio.com/'
self.firebase = firebase.FirebaseApplication(self.addr, None)
The tbsense_scan.py script continuously looks for advertising thunderboards, and automatically connects to the board if a new one is discovered.
$ sudo python3 tbsense_scan.py
No Thunderboard Sense devices found!
No Thunderboard Sense devices found!
Starting thread <Thread(Thread-1, initial)> for 37020
Thunder Sense #37020
Ambient Light: 0.25 Lux
Sound Level: 37.83
tVOC: 0
Humidity: 28.25 %RH
Temperature: 29.86 C
Pressure: 951.586
UV Index: 0
eCO2: 0
When a Thunderboard Sense has been discovered and connected to, the script will print out the read data periodically. Take note of the number in "Thunder Sense #37020". We will need to give the number to the web application.
The script generates a session ID for each board connected, and then continuously generates json strings for the data read from the Thunderboard Sense. The json string is then inserted into the appropriate location in the database. Firebase has a useful live database view that shows us that our data is indeed being pushed into the cloud:
Finally if you go to the app url and append your Thunderboard Sense id, you should see the data being displayed https://<project id>.firebaseapp.com/#/37020
tbsense.py simply discovers and sets up handles to the different characteristics. It also contains functions to read these characteristics:
from bluepy.btle import *
import struct
from time import sleep
class Thunderboard:
def __init__(self, dev):
self.dev = dev
self.char = dict()
self.name = ''
self.session = ''
self.coinCell = False
# Get device name and characteristics
scanData = dev.getScanData()
for (adtype, desc, value) in scanData:
if (desc == 'Complete Local Name'):
self.name = value
ble_service = Peripheral()
ble_service.connect(dev.addr, dev.addrType)
characteristics = ble_service.getCharacteristics()
for k in characteristics:
if k.uuid == '2a6e':
self.char['temperature'] = k
elif k.uuid == '2a6f':
self.char['humidity'] = k
elif k.uuid == '2a76':
self.char['uvIndex'] = k
elif k.uuid == '2a6d':
self.char['pressure'] = k
elif k.uuid == 'c8546913-bfd9-45eb-8dde-9f8754f4a32e':
self.char['ambientLight'] = k
elif k.uuid == 'c8546913-bf02-45eb-8dde-9f8754f4a32e':
self.char['sound'] = k
elif k.uuid == 'efd658ae-c401-ef33-76e7-91b00019103b':
self.char['co2'] = k
elif k.uuid == 'efd658ae-c402-ef33-76e7-91b00019103b':
self.char['voc'] = k
elif k.uuid == 'ec61a454-ed01-a5e8-b8f9-de9ec026ec51':
self.char['power_source_type'] = k
def readTemperature(self):
value = self.char['temperature'].read()
value = struct.unpack('<H', value)
value = value[0] / 100
return value
def readHumidity(self):
value = self.char['humidity'].read()
value = struct.unpack('<H', value)
value = value[0] / 100
return value
def readAmbientLight(self):
value = self.char['ambientLight'].read()
value = struct.unpack('<L', value)
value = value[0] / 100
return value
def readUvIndex(self):
value = self.char['uvIndex'].read()
value = ord(value)
return value
def readCo2(self):
value = self.char['co2'].read()
value = struct.unpack('<h', value)
value = value[0]
return value
def readVoc(self):
value = self.char['voc'].read()
value = struct.unpack('<h', value)
value = value[0]
return value
def readSound(self):
value = self.char['sound'].read()
value = struct.unpack('<h', value)
value = value[0] / 100
return value
def readPressure(self):
value = self.char['pressure'].read()
value = struct.unpack('<L', value)
value = value[0] / 1000
return value
thundercloud.py handles the connection to the Firebase database. getSession() generates a new session ID and is called once for every new Thunderboard Sense connection. putEnvironmentData() inserts the data and updates the timestamps:
Projects
Thunderboard Sense with Raspberry Pi and Python
The goal of this project was to make a very simple python script that runs on a Raspberry Pi and collects data from one or more Thunderboard Sense devices, using the same Google Firebase backend and web application that the official app uses.
To get started, go to https://github.com/siliconlabs/thundercloud, and clone the repository. Follow the instructions and set up a firebase account and project, replacing the database name in src/main.js as instructed. For this example, I have not yet gotten authentication working, so change the database.rules.json for now to allow anyone to write (basically replacing "auth.uid === 'YsGcsiI8hkwjImSrr25uZdqlNw3Qkgi8vUWx9MU6'": with "true")
Now, deploy the app to Firebase using the firebase tools
In the Firebase console you should now see your deployed rules, as well as the application url. Following the URL should bring you to the default page:
Now the next step is to log onto the Raspberry Pi, and install the necessary tools. The script uses bluepy to communicate with the sensor, and python-firebase to push data to the cloud. I did have some trouble installing python-firebase because of the specific version of the requests library, but eventually got it installed.
Remember to put in your own database URL in the thundercloud.py script:
The tbsense_scan.py script continuously looks for advertising thunderboards, and automatically connects to the board if a new one is discovered.
When a Thunderboard Sense has been discovered and connected to, the script will print out the read data periodically. Take note of the number in "Thunder Sense #37020". We will need to give the number to the web application.
The script generates a session ID for each board connected, and then continuously generates json strings for the data read from the Thunderboard Sense. The json string is then inserted into the appropriate location in the database. Firebase has a useful live database view that shows us that our data is indeed being pushed into the cloud:
Finally if you go to the app url and append your Thunderboard Sense id, you should see the data being displayed https://<project id>.firebaseapp.com/#/37020
tbsense.py simply discovers and sets up handles to the different characteristics. It also contains functions to read these characteristics:
thundercloud.py handles the connection to the Firebase database. getSession() generates a new session ID and is called once for every new Thunderboard Sense connection. putEnvironmentData() inserts the data and updates the timestamps:
Finally, tbsense_scan.py continuously searches for new Thunderboard Sense devices, and spawns a new thread for each one successfully connected to: