# CampbellDataStreaming
## Description
- A system allowing data collection from a Campbell scientific datalogger using
  a Raspebrry Pi and Python.
  
  As a requirement of the client, the program needs to provide real-time data 
  streaming. This originally meant the chart was to be updated as frequently 
  as possible. The python script allows data to be displayed online within 
  seconds of being collected.

  However, due to the client's data restrictions, it was decided to, instead of
  live-streaming the data, to upload/refresh the data every hour instead. With this 
  decision, the streaming charts were re-implemented as a static html file, 
  building the charts from the uploaded data.

## Requirements:
- A computer running Debian Jessie (I'm using a Raspberry Pi)
- Internet Connection (I'm using a Verizon Jetpack hotspot)
- A serial port to connect to the Campbell, on a Raspberry Pi (I'm using a RS232 cape)

## Repository Layout:
   _____________________________________________________________________________________
   | -DataCollection <- Forked from CampbellDataCollection repository                  | 
   | -Streaming      <- Contains live data streaming code                              |
   | |-Data          <- Holds sample data for testing code                             |        
   | |-cached_streamPlot.py <- First implementation - collect data from logger, parse  |
   | |                          data, and plot it. Not efficient, and suffers from     |
   | |                          producer-consumer problem.                             |
   | |-streamHTML.html      <- Uses uploaded data from Raspberry Pi to render charts   |
   | |                          upon page load. * version currently in production. *   |
   | |-streamPlot.py        <- Live-streaming implementation, updates plotly charts    |
   | |                         every 15 seconds. Also blocks chart updates every 15    |
   | |                         minutes to upload newly collected data to server.       |
   | |                         * Most efficient, but most data-intensive. *            |
   | |-streamPlot_dev.py    <- Development version of streamPlot.py                    |      
   |___________________________________________________________________________________|