ref: fc9c60e88820570a0317023180752a1d10653f50
dir: /python/lib/aubio/__init__.py/
#! /usr/bin/env python
# -*- coding: utf8 -*-
"""
aubio
=====
Provides a number of classes and functions for music and audio signal
analysis.
How to use the documentation
----------------------------
Documentation of the python module is available as docstrings provided
within the code, and a reference guide available online from `the
aubio homepage <https://aubio.org/documentation>`_.
The docstrings examples are written assuming `aubio` and `numpy` have been
imported with:
>>> import aubio
>>> import numpy as np
"""
import numpy
from ._aubio import __version__ as version
from ._aubio import float_type
from ._aubio import *
from .midiconv import *
from .slicing import *
class fvec(numpy.ndarray):
"""fvec(input_arg=1024)
A vector holding float samples.
If `input_arg` is an `int`, a 1-dimensional vector of length `input_arg`
will be created and filled with zeros. Otherwise, if `input_arg` is an
`array_like` object, it will be converted to a 1-dimensional vector of
type :data:`float_type`.
Parameters
----------
input_arg : `int` or `array_like`
Can be a positive integer, or any object that can be converted to
a numpy array with :func:`numpy.array`.
Examples
--------
>>> aubio.fvec(10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)
>>> aubio.fvec([0,1,2])
array([0., 1., 2.], dtype=float32)
>>> a = np.arange(10); type(a), type(aubio.fvec(a))
(<class 'numpy.ndarray'>, <class 'numpy.ndarray'>)
>>> a.dtype, aubio.fvec(a).dtype
(dtype('int64'), dtype('float32'))
Notes
-----
In the Python world, `fvec` is simply a subclass of
:class:`numpy.ndarray`. In practice, any 1-dimensional `numpy.ndarray` of
`dtype` :data:`float_type` may be passed to methods accepting
`fvec` as parameter. For instance, `sink()` or `pvoc()`.
See Also
--------
cvec : a container holding spectral data
numpy.ndarray : parent class of :class:`fvec`
numpy.zeros : create a numpy array filled with zeros
numpy.array : create a numpy array from an existing object
"""
def __new__(cls, input_arg=1024):
if isinstance(input_arg, int):
if input_arg == 0:
raise ValueError("vector length of 1 or more expected")
return numpy.zeros(input_arg, dtype=float_type, order='C')
else:
np_input = numpy.array(input_arg, dtype=float_type, order='C')
if len(np_input.shape) != 1:
raise ValueError("input_arg should have shape (n,)")
if np_input.shape[0] == 0:
raise ValueError("vector length of 1 or more expected")
return np_input