Friday, May 23, 2014

Speed, Speed, Speed, ... and news.

The newest GitHub repository contains a huge change to the under-the-hood processing of .getContextByClass() which is used in about a million places in music21.  It is the function that lets any note know what its current TimeSignature (and thus beatStrength, etc.) is, lets us figure out whether the sharp on a given note should be displayed or not given the current KeySignature, etc.  While we had tried to optimize the hell out of it, it’s been a major bottleneck in music21 for working with very large scores. We sped up parsing (at least the second time through) a lot the last commit. This was the time to speed up Context searching.  We now use a form of AVL tree implemented in a new Stream.timespans module — it’s not well-documented yet, so we’re only exposing it directly in one place, stream.asTimespans(recurse=True|False).  You don’t need to know about this unless you’re a developer; but I wanted to let you know that the results are extraordinary.

Here’s a code snippet that loads a score with three parts and 126 measures and many TimeSignatures and calculates the TimeSignature active for every note, clef, etc. and then prints the time it takes to run:

>>> c = corpus.parse('luca/gloria')
>>> def allContext(c):
...     for n in c.recurse():
...         k = n.getContextByClass('TimeSignature')
>>> from time import time as t
>>> x = t(); allContext(c); print t() - x

with the 1.8 release of Music21:
42.9 seconds

with the newest version in GitHub:
0.70 seconds

There’s a lot of caching that happens along the way, so the second call is much faster:

second call with 1.8 release:
44.6 seconds ( = same within a margin of error)
with the newest version in GitHub if the score hasn’t changed:
0.18 seconds

You’ll see the speedup immensely in places where every combination of notes, etc. needs to be found.  For instance, finding all parallel fifths in a large score of 8 parts could have taken hours before. Now you’ll likely get results in under a few seconds.

I have not heard of any issues arising from the change in sorting from the last posting on April 26, so people who were afraid of updating can breath a bit more easily and update to the version of music21 at least as of yesterday. The newer version, like all GitHub commits, should be used with caution until we make a release.

Thanks to the NEH and the Digging into Data Challenge for supporting the creation of tools for working with much bigger scores than before.

In other news: 

Music21j — a Javascript implementation of music21’s core features — is running rapidly towards a public release.  See for an example of usage.  We’ll be integrating it with the Python version over the summer.

Ian Quinn’s review of Music21 appeared in the Journal of the American Musicological society yesterday.  Prior to this issue, no non-book had ever been reviewed. It’s a great feeling to have people not on this list know about the software as well.

Oh, and MIT was foolhardy enough to give me tenure! Largely on the basis of music21.  If you’re an academic working on a large digital project, I still advice proceeding with caution, but know that it can be done.  Thanks everyone for support.

1 comment:

  1. Congratulations, Michael, on getting tenure for your awesome work on music21!!