Magnetometer Bike Ride

A bike ride with the magnetometer on my phone recording at about 60Hz.  I've got x, y, z, and r axis data:

And here are the corresponding power spectra (spectrograms):

Bike Ride Spectrogram

This is a spectrogram of the accelerometer data collected on my smartphone as I ride my bike.  The y-axis is the time (it was about a 909 second -- 15m 9s -- ride) and goes from top to bottom.  The x-axis is the frequency (0 to about 100Hz) and goes from left to right.  Lots of interesting harmonics and other complex structures.  This is sampling every 100 measurements, or at about 2 Hz (once every 0.5 seconds):

The plot above is also the sum of the x, y, and z axes.  Here are the individual axes:

X:

Y:

Z:

Here's a plot of the z amplitudes (in g's) versus time (x-axis, seconds).  This is sampling about every 0.5 seconds.

Here's the same data, but just every data point with no skipping:

Here's another ride earlier that day, going the opposite way.  This is the x-axis g-force on the smartphone accelerometer:

Happy Mole Day!

Happy Happy Happy Mole Day!

This is my third favorite number!  Behind pi and e.

Dawn 22 October 2025

 My location:

Messages From God

Stopping for gas in Benson:

typical gas pump, right?

The gas pump stops when the tank is full, so this is how many gallons was needed.

7 is a special number.

Yes, something like that...

Listening To Orion

This is a new and strange form of data analysis, so I'm starting simple and "looking" at some pretty basic stuff.

The constellation of Orion is rising in the early evenings this time of year, so I thought it appropriate to focus on what can be heard when using some data about the seven brightest stars of Orion: Betelgeuse, Rigel, Bellatrix, Mintaka, Alnilam, Alnitak, and Saiph.

What do the angular distances between Betelgeuse and the other six stars sound like?  I calculated these distances and translated them into audio frequencies.  The shorter the distance, the lower the frequency.  And this is what I got:

Orion Angular Distances

I then took the physical distances (in light years) between us and each of these stars, and did the same thing.  Again, the shorter the distance, the lower the frequency:

Orion Physical Distances

Finally, I took the visual magnitudes of these seven stars and translated them into audio frequencies.  In this case, the brighter the star (smaller magnitude), the higher the frequency.  To get this:

Orion Magnitudes

The Future of Data Analysis

Most data analysis today is done visually: we use our eyes to study images or plots.  Ironically, many researchers do this while listening to their favorite music not realizing that a potentially very important data analysis tool is being underutilized or not utilized at all.

I first came up with this idea back in the Summer of 2012, documented on my Astronomy and Music blog post titled 'Feel The Noise'.

What I'm suggesting is that we begin LISTENING to our data.  Our sense of hearing has a dynamic range of 1 trillion!  That's 120 dB!  No other human faculty is as sensitive, yet we hardly ever use it for anything other than listening to our environment.

So in this first very primitive and preliminary example, I've taken data from the JPL Horizons website to calculate the distances between the four Galilean moons as they revolve around Jupiter during the month of December 2015.  Over the course of that month, I measured the distances every 5184 seconds for a total of 500, equally time-spaced samples.

This is what I call "Distance Audio".

Six distance files are created:

• Io - Europa
• Io - Ganymede
• Io - Callisto
• Europa - Ganymede
• Europa - Callisto
• Ganymede - Callisto

I then took those distances and translated them to audio frequencies and mixed them into a single track.

As you listen to this track, keep in mind that the lower tones represent smaller distances and higher tones represent larger distances.

Also, take a look at the following plot, which shows you the distances between the moons (y-axis, in kilometers) as a function of time (x-axis):

And here's a 2D version of the same data, here showing the x, y distances.  The plots above are the vector sum of these distances.  These are what I've been calling "Orbital Ribbons" and they are quite beautiful by themselves.  Combined with the sound they make, it's pretty awesome.

Take a listen!  Please note that there is a periodic popping or "helicopter" noise.  This is an artifact of creating these audio files which I can hopefully eliminate once I understand the process better.  For now, please put up with it!

Click to Listen to 'Jupiter Moon Distances'

This is only the tip of the iceberg!  Any kind of data can be turned into music!
So then I took a random image of M13 from the Sloan Digital Sky Survey:

 and then I blurred it a little...

and then I blurred it a little more.

I then took the pixel values (representing intensity) along a column going right through the brightest part of the cluster (near the left edge) and translated those to audio frequencies for each of the three images.  A higher tone represents a brighter pixel.  I then mixed those into a single track and this is what I got:

This is called "Visual Audio"

A Slice Through M13

And finally,  here are the first five lines of the hydrogen Balmer series, converted from nanometers to Hertz, and mixed into a single five second track.  This can be done for any spectrum and I'm anxious to listen to the spectrum of our sun, other stars, galaxies, etc.

This is called "Spectral Audio"

Hydrogen Balmer Series

More to Come!

This is a new form of data analysis that needs to be explored!  Yes, it can create some interesting and possibly "pretty" sounds, but there is very useful information in those sounds that contains information about the data that might be overlooked!