Parallel Computing with music21

First we start the cluster system with ipcluster start, which on this six-core Mac Pro gives me 12 threads. Then I'll start iPython notebook with ipython notebook.

from __future__ import print_function
from IPython import parallel
clients = parallel.Client()
clients.block = True
print(clients.ids)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

Now I'll create a view that can balance the load automatically.

view = clients.load_balanced_view()

Next let me get a list of all the Bach chorales' filenames inside music21:

from music21 import *
chorales = list(corpus.chorales.Iterator(returnType = 'filename'))
chorales[0:5]

['bach/bwv269', 'bach/bwv347', 'bach/bwv153.1', 'bach/bwv86.6', 'bach/bwv267']

Now, I can use the view.map function to automatically run a function, in this case corpus.parse on each element of the chorales list.

view.map(corpus.parse, chorales[0:4])

[<music21.stream.Score 4467044944>,
 <music21.stream.Score 4467216976>,
 <music21.stream.Score 4465996368>,
 <music21.stream.Score 4465734224>]

Note though that the overhead of returning a complete music21 Score from each processor is high enough that we don't get much of a savings, if any, from parsing on each core and returning the Score object:

import time
t = time.time()
x = view.map(corpus.parse, chorales[0:30])
print("Multiprocessed", time.time() - t)
t = time.time()
x = [corpus.parse(y) for y in chorales[0:30]]
print("Single processed", time.time() - t)

Multiprocessed 1.7093911171
Single processed 2.04412794113

But let's instead just return the length of each chorale, so we don't need to pass much information back to the main server. First we need to import music21 on each client:

clients[:].execute('from music21 import *')

<AsyncResult: finished>

Now, we'll define a function that parses the chorale and returns how many pitches are in the Chorale:

def parseLength(fn):
    c = corpus.parse(fn)
    return len(c.flat.pitches)

Now we're going to see a big difference:

t = time.time()
x = view.map(parseLength, chorales[0:30])
print("Multiprocessed", time.time() - t)
t = time.time()
x = [parseLength(y) for y in chorales[0:30]]
print("Multiprocessed", time.time() - t)

Multiprocessed 0.59440112114
Multiprocessed 2.97019314766

In fact, we can do the entire chorale dataset in about the same amount of time as it takes to do just the first 30 on single core:

t = time.time()
x = view.map(parseLength, chorales)
print(len(chorales), 'chorales in', time.time() - t, 'seconds')

347 chorales in 5.31799721718 seconds

I hope that this example gives some sense of what might be done w/ a cluster situation in music21. If you can't afford your own Mac Pro or you need even more power, it's possible to rent an hour of cluster computing time at Amazon Web Services for just a few bucks.

Music21 v.2.0.5 (beta) released

The newest version of the beta 2.0 track of music21 has been released. A reminder that the 2.0 track involves potentially incompatible changes w/ 1.X so upgrade slowly and carefully if you need existing programs to work. Changes are being made to simplify and speed up usage and make the system more expandable for the future.

Download at https://github.com/cuthbertLab/music21/releases or with PyPI.

Major Changes

• Complete rewrite of TinyNotation. Tinynotation was one of the oldest modules in music21 and it showed — I was still learning Python when I wrote it. It documents a simple way of getting notation into music21 via a lily-like text interface. It was designed to be subclassable to make it work on whatever notation you wanted to use. And technically it was, but it was so difficult to do as to be nearly impossible. Now you’ll find it much simpler to subclass. Demos of subclassing are included in the code (esp. HarmonyNotation, and trecento.notation); a tutorial to come soon.
• backwards incompatible changes: (1) you used to be able to specify an initial time signature to Tinynotation as corpus.parse(“tinynotation: c4 d e f”, “4/4”); now you must put the time signature string into the text itself, as corpus.parse(“tinynotation: 4/4 c4 d e f”). “cut” and “c” time signatures are no longer supported; use 2/2 and 4/4 instead. (2) calling tinyNotation.TinyNotationStream() directly doesn’t work any more. Use the corpus.parse interface either with the “tinynotation:” header or format=“tinynotation” instead. If you must use the guts, try tinyNotation.Converter(“4/4 c4 d e f”).parse().stream. (3) TinyNotation used to return its own “TinyNotationStream” class, which was basically incompatible with everything. Now it returns a standard stream.Part() (4) TinyNotation did not put notes into measures, etc. you needed to call .makeMeasures() afterwards. If you need the older method, use corpus.parse(‘tinynotation: 4/4 c2 d’, makeNotation=False)
• Musescore works as a PNG/PDF format. First run: us = environment.UserSettings(); us[‘musescoreDirectPNGPath’] = '/Applications/MuseScore 2.app/Contents/MacOS/mscore’ or wherever you have it). Then try calling “.show(‘musicxml.png’)” and watch the image arrive about 100x faster than it would in Lilypond. Thanks MuseScore folks! This is now the default format for .show() in iPython notebook. Examples using lily.png and lily.pdf will migrate to this format, so that lilypond can be moved to deprecated-but-not-to-be-removed status. (I just don’t have time to keep up)
• demos/gatherAccidentals : a good first test programming assignment for students. I use it a lot in teaching.
• musicxml parses clefs mid-measure (thanks fzalkow)
• installer.command updated for OS X (thanks Andrew Hankinson) — let me know if this makes a problem.
• postTonalTools demo in usersGuide.
• DataSet feature extractor gets a .failFast = False option for debugging.

Under the hood / contributors

• music21 now uses coverage checking via coveralls.io. We are at 91.5% code coverage; meaning when the test suite is run, 91% of all the lines of code are tested. Aiming for 95% (100% is impossible). Adding coverage checking let me find a lot of places that weren’t being tested that, lo and behold!, had bugs. What it means for contributors: any commit that is longer than 20 lines of code needs to improve the coverage percentage and help us get to 95%. So make sure that at least 92% (better 99%) of your code is covered by tests.
• the romanText.objects module has been renamed romanText.rtObjects to not conflict with external libraries. It’s an implementation detail.
• added qm_converter.py demo of how to subclass SubConverter.

Minor Changes

• measure number suffixes in musicxml output, not just input.
• language detector can detect Latin and Dutch language texts now.
• fix pitch class errors in microtones.
• midi files with negative durations no long crash the system.
• bugs fixed in tonalCertainty. You can be more certain that it works.
• cPickle is used in Python3 now. Faster.
• midi parsing can specify quantization levels.
• music21.__version__ gives the version (maxalbert did a lot this commit; forgot to shout out before!)
• better detection of lilypond binaries.
• certain Sibelius MusicXML files with UTF-16BOMs can now be read.
• rests imported from MusicXML would not have expressions attached to them — fermatas, etc. fixed
• serial.ToneRow() now has the notes each as quarter notes rather than as zero-length notes; it makes .show() possible; backwards incompatible for the small number of people using it.
• colored notation now works better and in more places.
• better docs.
• about a trillion tiny bugs and untested pieces of code identified and fixed by glasperfan (Hugh Z.)

 

Looking forward to the 2.1 release!

Crunching ballads (Bibliolore)

The RILM blog, Bibliolore, published a recent post about the early history of computational musicology:

In the 1940s Bertrand Harris Bronson became one of the first scholars to use computers for musicological work.

For one of his projects he encoded melodic characteristics of hundreds of tunes collected for the traditional ballad Barbara Allen on punch cards, so a computer could ferret out similarities. His project resulted in four groups of tunes, members of which came from both sides of the Atlantic with varying frequency.

Read more at their site.  The RILM blog is amazing in any case.

OSU Empirical Music Research -- Learn from the Master...

The master of empirical music studies and the inventor of Humdrum, David Huron, announced a workshop to be held in late May in Columbus, Ohio -- probably of great interest to anyone working with music21 who wants to put their work into a broader context:


Methods in Empirical Music Research: A Workshop for Music Scholars.
Monday May 18 to Friday May 22, 2015.
School of Music, Ohio State University

We are pleased to announce a workshop on empirical methods in music research. This is an intensive five-day workshop taught by Prof. David Huron.

The workshop will be of interest to anyone wishing to expand or enhance their research skills in music. Participants will learn how to design and carry out music experiments, and how to apply empirical, systematic and statistical techniques to problems in music history, analysis, performance, education, culture, policy, and other areas. The workshop is designed specifically to develop practical research skills for musicians and music scholars with little or no previous background in empirical methods.

The workshop introduces participants to a number of methods, including descriptive, exploratory and questionnaire methods, field research, interview techniques, correlational and experimental methods, hypothesis testing, theory formation, and other useful research tools and concepts. Participants will also learn how to read and critique published empirical research related to music - identifying strengths and weaknesses in individual music-related studies.

The workshop objectives will be pursued through a series of day-long activities, including lectures and demonstrations, interspersed with twenty hands-on and group activities.

Three different forms of registration are available for workshop participants. The fee for non-credit participation is $450. A fee schedule for continuing education, and graduate course credit (2 credit hours) is pending. Participants are responsible for their own transporation, food and accommodation.

For further details, see http://musiccog.ohio-state.edu/home/index.php/Workshop

Ten(+) Years of music21 (pt. 1)

An important milestone for music21 passed unnoticed by me a few weeks ago: ten years since the first time that code (well mostly documentation and tests) that still remains somewhere in the music21 codebase was run and did something useful (a scrambled loop for a composition project I was working on called Fine dead sound, after Faulkner).  It seemed worth saying a bit about the history of the project and things learned along the way.

Music21 started as a Perl project -- the oldest code I can find that even vaguely resembles the current music21  comes from November 2003, a 30k Perl script to label a Bach chorale with Roman numerals and correct keys (I still need to publish the algorithm somewhere since I haven't seen a better one). It had no graphical output except using David Rakowski's Lassus font. I was able to get it back up and running and through the modern miracle of web fonts, everyone (except IE users) can play with the oldest  music21  system at: http://www.trecento.com/test/lassus.cgi?num=260. If I remember correctly, chords in red had only a passing functionality, the small letters are the roots of chords and the second line of text is the locally active key. Pivot chords are labeled in brackets. This was done for an assignment in Avi Pfeffer's AI and Music class; others in my group had already produced the code (in C I believe) for removing passing tones from the score.

I had begun that project using Humdrum, a system I knew well and still have great admiration for, but I began to need to encapsulate more and more items into individual classes.  I googled around, because I figured someone would have already gotten far in an object-oriented replacement for Humdrum. But I found nothing, so when I got back to the project about a year later I started creating generalized objects which I incorporated into a package called "PMusic" for "Perl Music." (I think I first called it just "Music" but realized that this was going to be a problem.)  Here is part of the PMusic code for Chords, written on 12/31/2004.  Good documentation was already a music21 principle, but names of notes were still based on Kern/Humdrum:

=head1 PMusic::Chord (class)

class for dealing with chords

  $ch1 = PMusic::Chord->new(['CC', 'E', 'g', 'b-']);

Note double set of brackets, indicating a perl array reference.

Doesnt deal with rests yet.  Chords of more or less than four notes might

be odd...

Can also give an arrayRef of Note objects if you're that perverse...

=cut

package PMusic::Chord;

use strict;

use Carp;

sub new {

  my $proto = shift;

  my $class = ref($proto) || $proto;

  my %args;

  ## if only one arg, assume it's an arrayref of Note names

  if (scalar @_ == 1) { $args{Notes} = $_[0] }

  else { %args = @_ };

  my $self  = \%args;

  bless $self, $class;

}

### for now, first note is always bass of chord (does tenor ever go below bass? sometimes!)

=item NoteObjs()

Returns an array ref of PMusic::Note objects

=cut

sub NoteObjs {

  my $self = shift;

  return $self->{NoteObjs} if $self->{NoteObjs};

  my @objs = ();

  foreach (@{$self->{Notes}}) {

    if (ref($_)) { push @objs, $_; }

    else         { push @objs, Note->new($_); }

  }

  $self->{NoteObjs} = \@objs;

}

=item Bass([note PMusic::Note])

returns the bass note or sets it to note.  Usually defined as the first noteObj

but we want to be able to override this.  You might want an implied

bass for instance...  v o9.

=cut

sub Bass {

  my $self = shift;

  $self->{Bass} = $_[0] if defined $_[0];

  $self->{Bass} ||= $self->NoteObjs->[0];

  return $self->{Bass};

}

At this point I was living in Rome at the American Academy as a Rome Prize fellow in Medieval Studies, and halfway through my fellowship I realized that none of this code was going to write a six-hundred page dissertation on fourteenth-century music fragments, and besides, I had job interviews to prepare for, and later, students to teach at Smith College, Mount Holyoke College, and then MIT, none of which (yes, including MIT) cared that I was working on generalized software for musical analysis as a side project. I also saw how long this project was going to take.  And besides, I was still convinced that someone had already written a generalized OO toolkit for musical analysis, I just wasn't searching well enough.  So the project was put aside for almost two years before it reemerged in Python, as the (to be written) Part 2 explains.