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https://github.com/marcecj/faust_python
A Python FAUST wrapper implemented using the CFFI.
https://github.com/marcecj/faust_python
Last synced: 18 days ago
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A Python FAUST wrapper implemented using the CFFI.
- Host: GitHub
- URL: https://github.com/marcecj/faust_python
- Owner: marcecj
- License: other
- Created: 2013-04-28T21:32:34.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2015-06-20T10:06:20.000Z (over 9 years ago)
- Last Synced: 2024-09-22T08:47:45.101Z (about 2 months ago)
- Language: Python
- Size: 477 KB
- Stars: 31
- Watchers: 12
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FAUSTPy
Marc Joliet
A FAUST wrapper for Python.
## Introduction
FAUSTPy is a Python wrapper for the [FAUST](http://faust.grame.fr/) DSP
language. It is implemented using the [CFFI](https://cffi.readthedocs.org/) and
hence creates the wrapper dynamically at run-time.
## Installation
FAUSTPy has the following requirements:
- [FAUST](http://faust.grame.fr/), specifically the FAUST2 branch, because
FAUSTPy requires the C backend.
- [CFFI](https://cffi.readthedocs.org/), tested with version 0.6.
- A C compiler; the default CFLAGS assume a GCC compatible one.
- [NumPy](http://numpy.scipy.org/), tested with version 1.6.
FAUSTPy works with Python 2.7 and 3.2+.
You can install FAUSTPy via the provided setup.py script by running
sudo python setup.py install
or
python setup.py install --user
Although you may want to verify that everything works beforehand by running the
test suite first:
python setup.py test
## Useage
Using FAUSTPy is fairly simple, the main class is FAUSTPy.FAUST, which takes
care of the dirty work. A typical example:
dsp = FAUSTPy.FAUST("faust_file.dsp", fs)
This will create a wrapper that initialises the FAUST DSP with the sampling rate
`fs` and with `FAUSTFLOAT` set to the default value of `float` (the default
precision that is set by the FAUST compiler). Note that this
1. compiles the FAUST DSP to C,
2. compiles and links the C code, and
3. initialises the C objects,
all of which happens in the background, thanks to the CFFI. Furthermore, this
wrapper class
1. initialises the UI as a `ui` attribute of the DSP, and
2. stores the meta-data declared by the DSP as a `metadata` attribute.
To better match the [NumPy](http://numpy.scipy.org/) default of `double`, you
can overload the `faust_float` argument:
dsp = FAUSTPy.FAUST("faust_file.dsp", fs, "double")
To process an array, simply call:
# dsp.dsp is a PythonDSP object wrapped by the FAUST object
audio = numpy.zeros((dsp.dsp.num_in, count))
audio[:,0] = 1
out = dsp.compute(audio)
Here the array `audio` is initialised to the number of inputs of the DSP and
`count` samples; each channel consists of a Kronecker delta, so `out` contains
the impulse response of the DSP. In general `audio` is allowed to have more
channels (rows) than the DSP, in which case the first `dsp.dsp.num_in` channels
are processed, but not less.
You can also pass in-line FAUST code as the first argument, which will be
written to a temporary file and compiled by FAUST as usual. In Python 3:
dsp = FAUSTPy.FAUST(b"process = _:*(0.5);", fs)
Finally, below is a simple IPython example (using Python 2) that shows what a
FAUST object might look like. It is based on the DSP
`dattorro_notch_cut_regalia.dsp` included in this repository.
In [1]: import FAUSTPy
In [2]: import numpy as np
In [3]: fs = 48000
In [4]: dattorro = FAUSTPy.FAUST("dattorro_notch_cut_regalia.dsp", fs, "double")
In [5]: dattorro.
dattorro.compute dattorro.dsp dattorro.FAUST_PATH
dattorro.compute2 dattorro.FAUST_FLAGS
In [5]: dattorro.dsp.
dattorro.dsp.compute dattorro.dsp.faustfloat dattorro.dsp.num_out
dattorro.dsp.compute2 dattorro.dsp.fs dattorro.dsp.ui
dattorro.dsp.dsp dattorro.dsp.metadata
dattorro.dsp.dtype dattorro.dsp.num_in
In [5]: dattorro.dsp.metadata
Out[5]:
{'author': 'Marc Joliet',
'copyright': '(c)Marc Joliet 2013',
'filter.lib/author': 'Julius O. Smith (jos at ccrma.stanford.edu)',
'filter.lib/copyright': 'Julius O. Smith III',
'filter.lib/license': 'STK-4.3',
'filter.lib/name': 'Faust Filter Library',
'filter.lib/reference': 'https://ccrma.stanford.edu/~jos/filters/',
'filter.lib/version': '1.29',
'license': 'MIT',
'math.lib/author': 'GRAME',
'math.lib/copyright': 'GRAME',
'math.lib/license': 'LGPL with exception',
'math.lib/name': 'Math Library',
'math.lib/version': '1.0',
'music.lib/author': 'GRAME',
'music.lib/copyright': 'GRAME',
'music.lib/license': 'LGPL with exception',
'music.lib/name': 'Music Library',
'music.lib/version': '1.0',
'name': 'Dattoro notch filter and resonator (Regalia)',
'version': '0.1'}
In [6]: dattorro.dsp.fs
Out[6]: 48000
In [7]: dattorro.dsp.num_in
Out[7]: 2
In [8]: dattorro.dsp.num_out
Out[8]: 2
In [9]: dattorro.dsp.ui.
dattorro.dsp.ui.label dattorro.dsp.ui.metadata dattorro.dsp.ui.p_Gain
dattorro.dsp.ui.layout dattorro.dsp.ui.p_Center_Freq dattorro.dsp.ui.p_Q
In [9]: dattorro.dsp.ui.label
Out[9]: 'dattorro_notch_cut_regalia'
In [10]: dattorro.dsp.ui.layout
Out[10]: 'vertical'
In [11]: dattorro.dsp.ui.p_Center_Freq
Out[11]:
In [12]: dattorro.dsp.ui.p_Center_Freq.
dattorro.dsp.ui.p_Center_Freq.default dattorro.dsp.ui.p_Center_Freq.min
dattorro.dsp.ui.p_Center_Freq.label dattorro.dsp.ui.p_Center_Freq.step
dattorro.dsp.ui.p_Center_Freq.max dattorro.dsp.ui.p_Center_Freq.type
dattorro.dsp.ui.p_Center_Freq.metadata dattorro.dsp.ui.p_Center_Freq.zone
In [12]: dattorro.dsp.ui.p_Center_Freq.label
Out[12]: 'Center Freq.'
In [13]: dattorro.dsp.ui.p_Center_Freq.metadata
Out[13]: {'unit': 'Hz'}
In [14]: dattorro.dsp.ui.p_Center_Freq.type
Out[14]: 'HorizontalSlider'
In [15]: audio = np.zeros((dattorro.dsp.num_in,fs), dtype=dattorro.dsp.dtype)
In [16]: audio[:,0] = 1
In [17]: audio
Out[17]:
array([[ 1., 0., 0., ..., 0., 0., 0.],
[ 1., 0., 0., ..., 0., 0., 0.]])
In [18]: dattorro.compute(audio)
Out[18]:
array([[ 0.74657288, -0.30020767, 0.0227801 , ..., 0. ,
0. , 0. ],
[ 0.74657288, -0.30020767, 0.0227801 , ..., 0. ,
0. , 0. ]])
For more details, see the built-in documentation (aka `pydoc FAUSTPy`) and - if
you are so inclined - the source code.
## Demo script
The `__main__.py` of the FAUST package contains a small demo application which
plots some magnitude frequency responses of the example FAUST DSP. You can
execute it by executing
PYTHONPATH=. python FAUSTPy
in the source directory. This will display four plots:
- the magnitude frequency response of the FAUST DSP at default settings,
- the magnitude frequency response with varying Q,
- the magnitude frequency response with varying gain, and
- the magnitude frequency response with varying center frequency.
## TODO
- finish the UIGlue wrapper
- finish the test suite
- finish the unit tests
- add functional tests so that you can test how everything works together
(perhaps use "UITester.dsp" and maybe one other DSP from the examples)