This directory contains some useful utility functions.
Please see plot.py, particularly the comments at the end. This script uses plot.ly so you need a (free) account and API key from there. It can generate plotly graphs for the raw data, labels, and/or detections.
We used this script to generate the plots in the paper. You will need to modify some lines at the bottom of the script to plot specific NAB data and/or results files.
There is currently a simple and somewhat hacky data visualizer available, useful in hand labeling datasets. To use it do the following:
First generate the list of data files and result files:
cd /path/to/nab
ls -1 data/*/*.csv | grep data > scripts/data_file_paths.txt
ls -1 results/*/*/*.csv | grep results | grep -v test_results > scripts/results_file_paths.txt
cd scripts
ln -s ../data
ln -s ../results
From the scripts directory, type:
python -m SimpleHTTPServer 12345
Then, open Chrome (only works on Chrome!) and type this into the url window:
localhost:12345/nab_visualizer.html
To view data, click on "look at data", click in query window and then press RETURN key. This should show all the data files. You can also filter the files by keyword with the query window; it will filter for filenames that contain the (case-sensitive) entered characters.
To get a string of the timestamp at a data point, simply click on the data point.
To zoom in on a region of data, drag the cursor to highlight the section of interest. To zoom back out, double-click the screen.
[CURRENTLY NOT WORKING]To view result files, click on "look at results" first and then click in query window and then press RETURN key. This should show all the data files.