Metadata-Version: 2.1
Name: acoular
Version: 24.3
Summary: Python library for acoustic beamforming
Project-URL: homepage, https://acoular.org
Project-URL: documentation, https://acoular.org
Project-URL: repository, https://github.com/acoular/acoular
Author-email: Acoular Development Team <info@acoular.org>
Maintainer-email: Adam Kujawski <adam.kujawski@tu-berlin.de>, Art Pelling <a.pelling@tu-berlin.de>, Ennes Sarradj <ennes.sarradj@tu-berlin.de>, Gert Herold <gert.herold@tu-berlin.de>, Mikolaj Czuchaj <mikolaj.czuchaj@tu-berlin.de>, Simon Jekosch <s.jekosch@tu-berlin.de>
License: Copyright (c) Acoular Development Team.
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License-File: AUTHORS.rst
License-File: LICENSE
Keywords: acoustics,beamforming,microphone array
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
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Description-Content-Type: text/markdown

![Acoular Logo](./docs/source/_static/Acoular_logo.png)

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# Acoular
Acoular is a Python module for acoustic beamforming that is distributed under the new BSD license. 

It is aimed at applications in acoustic testing. Multichannel data recorded by a microphone array can be processed and analyzed in order to generate mappings of sound source distributions. The maps (acoustic photographs) can then be used to locate sources of interest and to characterize them using their spectra. 

# Features
- frequency domain beamforming algorithms: delay & sum, Capon (adaptive), MUSIC, functional beamforming, eigenvalue beamforming
- frequency domain deconvolution algorithms: DAMAS, DAMAS+, Clean, CleanSC, orthogonal deconvolution
- frequency domain inverse methods: CMF (covariance matrix fitting), general inverse beamforming, SODIX
- time domain methods: delay & sum beamforming, CleanT deconvolution
- time domain methods applicable for moving source with arbitrary trajectory (linear, circular, arbitrarily 3D curved), 
- frequency domain methods for rotating sources via virtual array rotation for arbitrary arrays and with different interpolation techniques
- 1D, 2D and 3D mapping grids for all methods
- gridless option for orthogonal deconvolution
- four different built-in steering vector formulations
- arbitrary stationary background flow can be considered for all methods
- efficient cross spectral matrix computation
- flexible modular time domain processing: n-th octave band filters, fast, slow, and impulse weighting, A-, C-, and Z-weighting, filter bank, zero delay filters
- time domain simulation of array microphone signals from fixed and arbitrarily moving sources in arbitrary flow
- fully object-oriented interface
- lazy evaluation: while processing blocks are set up at any time, (expensive) computations are only performed when needed
- intelligent and transparent caching: computed results are automatically saved and loaded on the next run to avoid unnecessary re-computation
- parallel (multithreaded) implementation with Numba for most algorithms
- easily extendable with new algorithms

# License
Acoular is licensed under the BSD 3-clause. See [LICENSE](LICENSE)

# Citing

If you use Acoular for academic work, please consider citing our
[publication](https://doi.org/10.1016/j.apacoust.2016.09.015):

    Ennes Sarradj, Gert Herold,
    A Python framework for microphone array data processing,
    Applied Acoustics, Volume 116, 2017, Pages 50-58

# Dependencies
Acoular runs under Linux, Windows and MacOS and needs Numpy, Scipy, Traits, scikit-learn, pytables, Numba packages available. 
Matplotlib is needed for some of the examples.

If you want to use input from a soundcard hardware, you will also need to install the [sounddevice](https://python-sounddevice.readthedocs.io/en/0.3.12/installation.html) package. Some solvers for the CMF method need [Pylops](https://pylops.readthedocs.io/en/stable/installation.html).

# Installation

Acoular can be installed via [conda](https://docs.conda.io/en/latest/), which is also part of the [Anaconda Python distribution](https://www.anaconda.com/). It is recommended to install into a dedicated [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). After activating this environment, run

    conda install -c acoular acoular

This will install Acoular in your Anaconda Python enviroment and make the Acoular library available from Python. In addition, this will install all dependencies (those other packages mentioned above) if they are not already present on your system. 

A second option is to install Acoular via [pip](https://pip.pypa.io/en/stable/). It is recommended to use a dedicated [virtual environment](https://virtualenv.pypa.io/en/latest/) and then run

    pip install acoular

For more detailed install instructions see the [documentation](http://acoular.org/install/index.html).

# Documentation and help
Documentation is available [here](http://acoular.org) with a
[getting started](http://acoular.org/get_started/index.html) section and
[examples](http://acoular.org/examples/index.html).

The Acoular [blog](https://acoular.github.io/blog/) contains some tutorials.

Problems, suggestions and success using Acoular may be reported via the [acoular-users](https://groups.google.com/forum/#!forum/acoular-users) discussion forum.

# Example
This reads data from 64 microphone channels and computes a beamforming map for the 8kHz third octave band:

```python
from os import path
import acoular
from matplotlib.pylab import figure, plot, axis, imshow, colorbar, show

# this file contains the microphone coordinates
micgeofile = path.join(path.split(acoular.__file__)[0],'xml','array_64.xml')
# set up object managing the microphone coordinates
mg = acoular.MicGeom( from_file=micgeofile )
# set up object managing the microphone array data (usually from measurement)
ts = acoular.TimeSamples( name='three_sources.h5' )
# set up object managing the cross spectral matrix computation
ps = acoular.PowerSpectra( time_data=ts, block_size=128, window='Hanning' )
# set up object managing the mapping grid
rg = acoular.RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \
increment=0.01 )
# set up steering vector, implicitely contains also the standard quiescent 
# environment with standard speed of sound
st = acoular.SteeringVector( grid = rg, mics=mg )
# set up the object managing the delay & sum beamformer
bb = acoular.BeamformerBase( freq_data=ps, steer=st )
# request the result in the 8kHz third octave band from approriate FFT-Lines
# this starts the actual computation (data intake, FFT, Welch CSM, beamforming)
pm = bb.synthetic( 8000, 3 )
# compute the sound pressure level
Lm = acoular.L_p( pm )
# plot the map
imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \
interpolation='bicubic')
colorbar()
```

![result](./docs/source/get_started/three_source_py3_colormap.png)
