Quick start

• The presentation can be found here.
• A preview of the notebook can be seen here, but note that there are cool interactive things that are missing unless you download and run it yourself.

Details

This package is designed to be a simple demonstration of the principles of matched filtering. It uses the analogy of LIGO as a microphone to explain the basic ideas, using a microphone attached to the computer to study data as a function of time, noise sources, and real signals, as well as headphones or a speaker to play back those signals and others. Examples are given where a signal is buried in the noise and extracted using matched filtering. Real LIGO data and accurate gravitational waveforms are also included with this code, and used for further examples to complete the analogy. The concepts introduced here can be applied far more widely in all areas of data analysis.

Fourier transforms are introduced, starting with a simple example of a pure tone (which can be played by the computer), and progressing to Fourier transforms of noise and gravitational-wave signals. The matched filter is then introduced by progressively building the formula with simple explanations for each term. Discussion of these concepts is interwoven with practice using them.

The material is presented as a Jupyter notebook — which looks and acts basically like Mathematica. The notebook includes text explaining the concepts and code. This allows the explanations to be included (with latex equations) right among the code, and all in a live python session. No familiarity with python is necessary for the student, though the computer will need to be set up by someone with good technical skills.

A second notebook is also included for more demonstrations of the Fourier transform. This notebook makes it easy to record a sound — tuning forks, musical instruments, or whatever the student is curious about — and look at its Fourier transform. This encourages the student to play with the ideas a little, experimenting to gain understanding.

To run the code

If you are reading this README from the github project page, you should first download the package to your own computer with something like

git clone --depth 1 https://github.com/moble/MatchedFiltering.git

Otherwise, you are presumably reading this in the code itself.

You also need ipython, matplotlib, numpy, scipy, h5py, jupyter, a very recent (>5.0) version of ipywidgets, and widgetsnbextension installed. The easiest way to do all this is to just install conda and run

conda update -c conda-forge -y --all
conda install -c conda-forge ipython jupyter notebook scipy matplotlib ipywidgets widgetsnbextension

Otherwise, all of these packages can also be installed using pip.

Now, from the MatchedFiltering directory, issue the command

    jupyter notebook MatchedFiltering.ipynb

This will start an ipython session, but should switch to your default web browser, where you will interact with the session.

Notes for classroom use

There are three reasonable ways to deliver this demonstration to students: as a presentation, individually on the students' personal computers, and together in a computer lab.

Most likely, the presentation option is the least useful to students. Most students benefit enormously from being able to interact with the notebook personally. They will be more interested, able to read along at their own pace, and play with the parameters. If this is just not possible, it would be best to go slowly and ask lots of questions of the students, possibly allowing one student to actually run the commands while the teacher engages from off to the side.

A preferable option may be having the students download and run the code themselves. The only caveat here is that the students will need to install the dependencies. With anaconda, this is not a problem. Assuming the students can run it, there are questions included in the notebook. Their answers could be turned in as a homework assignment, or a quiz given on the material to ensure that students actually go through the notebook.

If this will be presented together in a computer lab, it is best if things are set up as much as possible on each computer beforehand. The computers need to be using different accounts (with home directories not on a shared file system), or ipython will get screwed up and run into errors.