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py2max

A pure python3 library without dependencies intended to facilitate the offline generation of Max patcher files (.maxpat, .maxhelp, .rbnopat).

If you are looking for python3 externals for Max/MSP check out the py-js project.

Features

  • Scripted offline generation of Max patcher files using Python objects, corresponding, on a one-to-one basis, with Max/MSP objects stored in the .maxpat JSON-based file format.

  • Round-trip conversion between (JSON) .maxpat files with arbitrary levels of nesting and corresponding Patcher, Box, and Patchline Python objects.

  • Can potentially handle any Max object or maxclass.

  • Lots of unit tests, ~99% coverage.

  • Analysis and offline scripted modification of Max patches in terms of composition, structure (as graphs of objects), object properties and layout (using graph-drawing algorithms).

  • Allows precise layout and configuration of Max objects.

  • Patcher objects have generic methods such as add_textbox and can also have specialized methods such as add_coll. As an example, this method has a dictionary argument to make it easy to prepopulate the coll object (see py2max/tests/test_coll.py).

  • Provides a maxclassdb feature which recalls default configurations of Max Objects.

Possible use cases

  • Scripted patcher file creation.

  • Batch modification of existing .maxpat files.

  • Use the rich python standard library and ecosystem to help create parametrizable objects with configuration from offline sources. For example, one-of-a-kind wavetable oscillators configured from random wavetable files.

  • Generation of test cases and .maxhelp files during external development

  • Takes the pain out of creating objects with lots of parameters

  • Prepopulate containers objects such as coll, dict and table objects with data

  • Help to save time creating many objects with slightly different arguments

  • Use graph drawing / layout algorithms on generated patches.

  • Generative patch generation (-;

  • etc..

Usage examples

p = Patcher('my-patch.maxpat')
osc1 = p.add_textbox('cycle~ 440')
gain = p.add_textbox('gain~')
dac = p.add_textbox('ezdac~')
osc1_gain = p.add_line(osc1, gain) # osc1 outlet 0 -> gain inlet 0
gain_dac0 = p.add_line(gain, dac, outlet=0, inlet=0)
gain_dac1 = p.add_line(gain, dac, outlet=0, inlet=1)
p.save()

By default, objects are returned (including patchlines), and patchline outlets and inlets are set to 0. While returned objects are useful for linking, the returned patchlines are not. Therefore, the above can be written more concisely as:

p = Patcher('my-patch.maxpat')
osc1 = p.add_textbox('cycle~ 440')
gain = p.add_textbox('gain~')
dac = p.add_textbox('ezdac~')
p.add_line(osc1, gain)
p.add_line(gain, dac)
p.add_line(gain, dac, inlet=1)
p.save()

With builtin aliases (.add for .add_* type methods and .link for .add_line), the above example can be written in an even more abbreviated form (and with a vertical layout) as:

p = Patcher('out_vertical.maxpat', layout='vertical')
osc = p.add('cycle~ 440')
gain = p.add('gain~')
dac = p.add('ezdac~')
p.link(osc, gain)
p.link(gain, dac)
p.link(gain, dac, 1)
p.save()

In addition, you can parse existing .maxpat files, change them and then save the changes:

p = Patcher.from_file('example1.maxpat')
# ... make some change
p.save_as('example1_mod.maxpat')

Another example with subpatchers:

p = Patcher('out.maxpat')
sbox = p.add_subpatcher('p mysub')
sp = sbox.subpatcher
in1 = sp.add('inlet')
gain = sp.add('gain~')
out1 = sp.add('outlet')
osc = p.add('cycle~ 440')
dac = p.add('ezdac~')
sp.link(in1, gain)
sp.link(gain, out1)
p.link(osc, sbox)
p.link(sbox, dac)
p.save()

Note that Python classes are basically just simple wrappers around the JSON structures in a .maxpat file, and almost all Max/MSP and Jitter objects can be added to the patcher file with the .add_textbox or the generic .add methods. There are also specialized methods in the form .add_<type> for numbers, numeric parameters, subpatchers, and container-type objects (see the design notes below for more details).

Installation

Simplest way:

git https://github.com/shakfu/py2max.git
cd py2max
pip install . # optional

Note that py2max does not need to be installed to be used, so you can skip the pip install . part if you prefer and just cd into the cloned directory and start using it:

$ cd py2max
$ ipython

In [1]: from py2max import Patcher

In [2]: p = Patcher.from_file("tests/data/simple.maxpat")

In [3]: p._boxes
Out[3]: [Box(id='obj-2', maxclass='ezdac~'), Box(id='obj-1', maxclass='newobj')]

Testing

py2max has an extensive test suite with tests are in the py2max/tests folder.

One can run all tests as follows:

pytest

This will output the results of all tests into outputs folder.

Note that some tests may be skipped if a required package for the test cannot be imported.

You can check which test is skipped by the following:

pytest -v

To check test coverage:

./scripts/coverage.sh

which essentially does the following

mkdir -p outputs
pytest --cov-report html:outputs/_covhtml --cov=py2max tests

To run an individual test:

python3 -m pytest tests.test_basic

Note that because py2max primarily deals with json generation and manipulation, most tests have no dependencies since json is already built into the stdlib.

However, a bunch of tests explore the application of orthogonal graph layout algorithms and for this, a whole bunch of packages have been used, which range from the well-known to the esoteric.

As mentioned above, pytest will skip a test if required packages are not installed, so these are entirely optional tests.

If you insist on diving into the rabbit hole, and want to run all tests you will need the following packages (and their dependencies):

  • networkx: pip install networkx
  • matplotlib: pip install matplotlib
  • pygraphviz: Pygraphviz requires installing the development library of graphviz: https://www.graphviz.org/ (On macOS this can be done via brew install graphviz) -- then you can pip install pygraphviz
  • adaptagrams: First build the adaptagrams c++ libs and then build the swig-based python wrapper.
  • pyhola: a pybind11 wrapper of adaptagrams. Follow build instructions in the README and install from the git repo.
  • tsmpy: install from git repo
  • OrthogonalDrawing: install from git repo

Caveats

  • API Docs are still not available

  • The current default layout algorithm is extremely rudimentary, however there are some promising directions and you can see also see a visual comparison of how well different layout algorithms perform in this context.

  • While generation does not consume the py2max objects, Max does not unfortunately refresh-from-file when it's open, so you will have to keep closing and reopening Max to see the changes to the object tree.

  • For the few objects which have their own methods, the current implementation differentiates tilde objects from non-tilde objects by providing a different method with a _tilde suffix:

    gen = p.add_gen()

gen_tilde = p.add_gen_tilde()

Design Notes

The .maxpat JSON format is actually pretty minimal and hierarchical. It has a parent Patcher and child Box entries and also Patchlines. Certain boxes contain other patcher instances to represent nested subpatchers and gen~ patches, etc..

The above structure directly maps onto the Python implementation which consists of 3 classes: Patcher, Box, and Patchline. These classes are extendable via their respective **kwds and internal__dict__ structures. In fact, this is the how the .from_file patcher classmethod is implemented.

This turns out to be the most maintainable and flexible way to handle all the differences between the hundreds of Max, MSP, and Jitter objects.

A growing list of patcher methods have been implemented to specialize and facilitate the creation of certain classes of objects which require additional configuration:

  • .add_attr
  • .add_beap
  • .add_bpatcher
  • .add_codebox
  • .add_coll
  • .add_comment
  • .add_dict
  • .add_floatbox
  • .add_floatparam
  • .add_gen
  • .add_intbox
  • .add_intparam
  • .add_itable
  • .add_message
  • .add_rnbo
  • .add_subpatcher
  • .add_table
  • .add_textbox
  • .add_umenu

This is a short list, but the add_textbox method alone can handle almost all case. The others are really just there for convenience and to save typing.

Generally, it is recommended to start using py2max's via these add_<type> methods, since they have most of the required parameters built into the methods and you can get IDE completion support. Once you are comfortable with the parameters, then use the generic abbreviated form: add, which is less typing but tbe tradeoff is you lose the IDE parameter completion support.

Scripts

The project has a few of scripts which may be useful:

  • convert.py: convert maxpat to yaml for ease of reading during dev
  • compare.py: compare using deepdiff
  • coverage.sh: run pytest coverage and generate html coverage report

Note that if you want to build py2max as a wheel:

pip install build
cd py2max
python3 -m build .

The wheel then should be in the dist directory.

Examples of Use

Alternative Branches

pydantic2 branch

There is an experimental branch of this project which is based on the pydantic2 project.

This variant has the benefit of the following:

  • Tracks the main branch
  • 100% tests pass
  • More pythonic api
  • Improved serialization / deserialization
  • Widespread use of type validation based on type-hints.
In [1]: from py2max import Patcher

In [2]: p = Patcher(path='outputs/demo.maxpat')

In [3]: msg = p.add_message('set')

In [4]: p.boxes
Out[4]: [Box(id='obj-1', text='set', maxclass='message', numinlets=2, numoutlets=1, outlettype=[''], patching_rect=Rect(x=48.0, y=48.0, w=66.0, h=22.0), patcher=None)]

Another promising direction of this variant is to create specialized classes for objects which have their own unique maxclass. So in this case the above would read:

In [4]: p.boxes
Out[4]: [Message(id='obj-1', text='set', maxclass='message', numinlets=2, numoutlets=1, outlettype=[''], patching_rect=Rect(x=48.0, y=48.0, w=66.0, h=22.0), patcher=None)]

properties branch

There was an early effort to provide property based attribute access and an improved api. It has been supplanted by the pydantic2 branch and will not be developed further.

Credits and Licensing

All rights reserved to the original respective authors:

  • Steve Kieffer, Tim Dwyer, Kim Marriott, and Michael Wybrow. HOLA: Human-like Orthogonal Network Layout. In Visualization and Computer Graphics, IEEE Transactions on, Volume 22, Issue 1, pages 349 - 358. IEEE, 2016. DOI

  • Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart, “Exploring network structure, dynamics, and function using NetworkX”, in Proceedings of the 7th Python in Science Conference (SciPy2008), Gäel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11–15, Aug 2008

  • A Technique for Drawing Directed Graphs Emden R. Gansner, Eleftherios Koutsofios, Stephen C. North, Kiem-phong Vo • IEEE TRANSACTIONS ON SOFTWARE ENGINEERING • Published 1993

  • Gansner, E.R., Koren, Y., North, S. (2005). Graph Drawing by Stress Majorization. In: Pach, J. (eds) Graph Drawing. GD 2004. Lecture Notes in Computer Science, vol 3383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31843-9_25

  • An open graph visualization system and its applications to software engineering Emden R. Gansner, Stephen C. North • SOFTWARE - PRACTICE AND EXPERIENCE • Published 2000