Low Tech Graph for Low Tech Needs

Following from where I left off in my data collection phase, I need a way to view my data while on my mac. I usually never have my RPi connected to a display, but rather ssh in. I was thinking about making a live update graph from a javascript library and hosting it on the RPi, but, then I realized that I was only collecting 1 datapoint every 6 minutes (a built in characteristic of Transmission) and thought that it would be overkill, especially since I would only want to see the graph while I was on the same local network.

I settled on a module that is a part of scitools, called aplotter, which makes an ascii (not an image) plot, because it would be able to make a plot straight in the same terminal session, and because my demands are more qualitative when it comes to the graph.

My Script (a link to the one I use here) got a few things done:

• Used regex to parse the strings I had saved in a csv and extracted time and byte strings
  • CSV specific:
    • with open(data_input_path, 'rb') as csvfile:
      • raw_torrenting_data = csv.reader(csvfile, delimiter=',')
        • then you have each line to work with, and then element within that line
  • straightforward: re.search(pattern, source) , then use groups to extract elements
  • would sometimes get a method NoneType error when the parse failed, sometimes for questionable reasons and sporadically, so I introduced a condition the avoid these datapoint errors
  • if str(type(parse_data_point)) == "<type '_sre.SRE_Match'>"
• turn string times into time objects
  • datetime.datetime.strptime(string_a, '%Y-%m-%d %X')
  • can subtract time objects no problem
    • but working with the deltas is interesting, it only stores days, and then leftover seconds, so you can query days and seconds only
    • hour_difference = (4 + diff_obj.days * 24 + (diff_obj.seconds/3600.))
• Did the derivative of the cumulative count of uploaded bytes
  • shifted one list, and subtracted their elements
  • slice notation was interesting
    • original_data = array_of_string_nums[1:]
      • leaves off the first element, 0th index
    • offset_data = array_of_string_nums[:-1]
      • leaves off the last element
• Made converted bytes more readable
• Made the graph for the last day
  • x = x_axis[-145:-1]
  • y = y_axis[-145:-1]
  • scitools.aplotter.plot(x, y)

What it looks like:

Good enough! Can see whether uploading is going well, or not at all. Notice that all the time is spent loading the library for the plotter!

• I found this out by inserting: import time ; start_time = time.time() at the beginning, and then print time.time() - start_time, "seconds after whatever" intermittently

Things to fix later:

• see if its possible to use another library thats quicker, or some workaround
• parse command line arguments to plot more than just the last 24 hours
• consider prettier graphs like the python port of chartkick