The livestream.py script demonstrates real-time plotting of data via
Matplotlib. This code is currently designed to work with a very specific piece
of hardware, but the methods implemented here are pretty general.
Once connected to your favorite Biomonitor device, visualize the live data by running:
$/> python livestream.py
You can pass a few arguments to this script to change the width of the plot, or the maximum and minimum y-scale ranges, turn auto-scaling on and off, and set the number of filter taps. For example, to set the upper limit of the plot to 1500:
$/> python livestream.py --max 1500
To see all available options, run:
$/> python livestream.py -h
usage: livestream.py [-h] [-w [WIDTH]] [-m [MIN]] [-x [MAX]] [-f [FILTER]]
[-c [CHANNEL]] [-a [AUTOSCALE]] [-s [SAVE]]
Visualize data.
optional arguments:
-h, --help show this help message and exit
-w [WIDTH], --width [WIDTH]
Width of the plot, in seconds.
-m [MIN], --min [MIN]
Minimum y-scale of plot (Ohms).
-x [MAX], --max [MAX]
Maximum y-scale of plot (Ohms).
-f [FILTER], --filter [FILTER]
Number of filter taps to use; 0 for no filtering.
-c [CHANNEL], --channel [CHANNEL]
Which channel should we plot?
-a [AUTOSCALE], --autoscale [AUTOSCALE]
Autoscale on (1) or off (0)
-s [SAVE], --save [SAVE]
Save the data to disk (1) or not (0)
If the -s flag is set to 1, data will be written to the disk in the /data
folder. This data can later be visualized and filtered by running the
view_data.py script.