This is a web application for flight log analysis. It allows users to upload ULog flight logs, and analyze them through the browser.

It uses the bokeh library for plotting and the Tornado Web Server.

Flight Review is deployed at https://review.px4.io.

• Python3 (3.6+ recommended)
• SQLite3
• http://fftw.org/

sudo apt-get install sqlite3 fftw3 libfftw3-dev

Note: Under some Ubuntu and Debian environments you might have to install ATLAS

sudo apt-get install libatlas3-base

macOS already provides SQLite3. Use Homebrew to install fftw:

brew install fftw

# After git clone, enter the directory
git clone https://github.com/PX4/flight_review.git
cd flight_review
pip install -r requirements.txt
# Note: preferably use a virtualenv

• By default the app will load config_default.ini configuration file
• You can override any setting from config_default.ini with a user config file config_user.ini (untracked)
• Any setting on config_user.ini has priority over config_default.ini
• Run setup_db.py to initialize the database.

Note: setup_db.py can also be used to upgrade the database tables, for instance when new entries are added (it automatically detects that).

For local usage, the server can be started directly with a log file name, without having to upload it first:

./serve.py -f <file.ulg>

To start the whole web application:

./serve.py --show

The plot_app directory contains a bokeh server application for plotting. It can be run stand-alone with bokeh serve --show plot_app (or with cd plot_app; bokeh serve --show main.py, to start without the html template).

The whole web application is run with the serve.py script. Run ./serve.py -h for further details.

The plotting can also be used interative using a Jupyter Notebook. It requires python knowledge, but provides full control over what and how to plot with immediate feedback.

• Start the notebook
• Locate and open the test notebook file testing_notebook.ipynb.

# Launch jupyter notebook
jupyter notebook testing_notebook.ipynb

The web site is structured around a bokeh application in plot_app (plot_app/configured_plots.py contains all the configured plots). This application also handles the statistics page, as it contains bokeh plots as well. The other pages (upload, browse, ...) are implemented as tornado handlers in tornado_handlers/.

plot_app/helper.py additionally contains a list of log topics that the plot application can subscribe to. A topic must live in this list in order to be plotted.

Tornado uses a single-threaded event loop. This means all operations should be non-blocking (see also http://www.tornadoweb.org/en/stable/guide/async.html). (This is currently not the case for sending emails).

Reading ULog files is expensive and thus should be avoided if not really necessary. There are two mechanisms helping with that:

• Loaded ULog files are kept in RAM using an LRU cache with configurable size (when using the helper method). This works from different requests and sessions and from all source contexts.
• There's a LogsGenerated DB table, which contains extracted data from ULog for faster access.

In addition to in-memory caching there is also some on-disk caching: KML files are stored on disk. Also the parameters and airframes are cached and downloaded every 24 hours. It is safe to delete these files (but not the cache directory).

Bokeh uses dynamic code loading and the plot_app/main.py gets loaded on each session (page load) to isolate requests. This also means we cannot use relative imports. We have to use sys.path.append to include modules in plot_app from the root directory (Eg tornado_handlers.py). Then to make sure the same module is only loaded once, we use import xy instead of import plot_app.xy. It's useful to look at print('\n'.join(sys.modules.keys())) to check this.

Contributions are welcome! Just open a pull request with detailed description why the changes are needed, or open an issue for bugs, feature requests, etc...