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https://github.com/moble/matchedfiltering

An interactive demonstration of matched filtering with some examples using real gravitational-wave data and your computer's audio.
https://github.com/moble/matchedfiltering

gravitational-waves ligo signal-processing tutorials

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An interactive demonstration of matched filtering with some examples using real gravitational-wave data and your computer's audio.

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README 

        
[![binder-badge]][binder] [![lite-badge]][lite]



[binder-badge]: https://mybinder.org/badge_logo.svg

[binder]: https://mybinder.org/v2/gh/moble/MatchedFiltering/binder?urlpath=lab/tree/content

[lite-badge]: https://jupyterlite.rtfd.io/en/latest/_static/badge.svg

[lite]: https://moble.github.io/MatchedFiltering/



Quick start

===========



There are several quick and easy options:



  * Click the ["binder" button][binder] above to open a live version of the main notebook that runs in the cloud.

    

    This may not work if the Binder service is too busy at the moment, and it will kill your session

    if you don't run any commands for a few minutes.  But if it is available, this will work in any

    modern browser.

  * Click the ["lite now" button][lite] above to open a live version of the main notebook that runs in your browser.

    

    This may not work with certain browser configurations — especially mobile browsers.

  * Run the notebook on your own computer if you have [anaconda](https://www.anaconda.com/)

    installed.  Download the file `content/MatchedFiltering.ipynb` and run these commands:

    ```bash

    conda install -c conda-forge numpy scipy h5py matplotlib ipywidgets jupyterlab

    jupyter lab MatchedFiltering.ipynb

    ```

    This will download and install all the necessary files, then open the notebook in your browser.



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 is an interactive python session, and

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, interactive python session.  No

familiarity with python is necessary for the student, though the setup may require some basic

skills.  A presentation that introduces and motivates the material is in the root directory, and can

be [viewed on Binder

here](https://mybinder.org/v2/gh/moble/MatchedFiltering/binder?filepath=Presentation.ipynb).



To run the code

===============



If you are familiar with python packaging, you can probably figure out how to run this on your own.

Note that the required packages include ipython, jupyter, notebook, scipy, matplotlib, ipywidgets,

and widgetsnbextension.



It is much simpler to just use the [anaconda](https://www.anaconda.com/) python ecosystem.  Once

anaconda is installed, just run the following at the command prompt:



```bash

conda env create moble/gw_matched_filtering_demo

conda activate gw_matched_filtering_demo

gw_matched_filtering_demo

```



This will install all the requirements into a new conda environment, switch to that environment,

then download and run the notebook.



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](https://www.anaconda.com/), 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.



Cool video

==========

A student in one workshop pointed out [this TED Talk by the astronomer Wanda Díaz-Merced](https://www.ted.com/talks/wanda_diaz_merced_how_a_blind_astronomer_found_a_way_to_hear_the_stars)

who lost her sight, and now interacts with data by turning it into sound.  Though she uses very

different techniques from the ones we use for LIGO data, this is a very powerful example of

the importance of exploring data in different ways.