Skip to main content

Facilitate acoustic processing from underwater acoustic recorders

Project description


main PyPI Downloads DOI

pypam is a python package to analyze underwater sound. It is made to make easier the processing of underwater data stored in audio files in chunks. The main classes are AcousticFile, AcousticSurvey and DataSet. The first one is a representation of an audio file together with all the metadata needed to process the data (such as hydrophone used). The second one is the representation of a folder where all the files are stored for one deployment. Here we consider a deployment as a measurement interval corresponding to the time when a hydrophone was in the water, without changing any recording parameters. The Dataset is a combination of different AcousticSurveys in one dataset. This is to be used if the user has made several deployments and wants to process them with the same parameters.

Then pypam allows to go through all the audio files from the deployments only with one line of code and store the output in netCDF files, including metadata. The package can be used to analyze a single file, a folder with files or a group of different deployments. pypam deals with the calibration directly, so the output obtained is already in uPa or dB!

All the documentation can be found on readthedocs

Warning This package is under active development, use with caution.

Installation

Using pip distribution

pip install lifewatch-pypam

Using git clone

  1. Clone the package
    git clone https://github.com/lifewatch/pypam.git
    
  2. Use poetry to install the project dependencies
    poetry install
    
  3. Build the project
    poetry build
    

News from version 0.3.0

In version 0.2.0 we removed the detectors, because there are better maintained packages for these purposes. In version 0.2.1 we added the frequency-dependent calibration! In version 0.3.0 we changed quite some plot function to make them more flexible with other data products. We also changed the mean functions to median, so the output in db is the same than the output in uPa

Quickstart

The package is imported as pypam. The audio file names must comply with the needs of pyhydrophone to be able to read the datetime information.

The user can choose a window chunk size (parameter binsize, in seconds), so all the features / methods are applied to that window. If set to None, the operations are performed along an entire file.

The available methods and features are:

  • Acoustic Indices:
    • ACI
    • BI
    • SH
    • TH
    • NDSI
    • AEI
    • ADI
    • Zero crossing (average)
    • BN peaks
  • time-domain features:
    • rms
    • dynamic_range
    • sel
    • peak
    • rms_envelope
    • spectrum_slope
    • correlation coefficient
  • frequency-domain
    • spectrogram (also octave bands spectrogram)
    • spectrum (density or power)
    • 1/n-octave bands
    • hybrid millidecade bands
    • long-term spectrogram
  • time and frequency
    • SPD

Futhermore, there are several plotting functions

  • SPD
  • spectrum with standard deviation
  • boxplots of time series aggregated data
  • daily patterns
  • LTSA

and some signal-based operations:

  • Signal operations
    • Noise reduction
    • Downsample
    • Band filter
    • Envelope
    • DC noise removal

PyPAM wrappers

PBP (PyPAM Based Processing) is a wrapper for PyPAM to make its use to compute hybrid millidecade bands more effective. PBP allows for local or cloud computing. For more information, you can find it in this repository.

Examples and documentation

See the documentation in readthedocs for a complete reference manual and example gallery.

In this collection of notebooks, some extra examples can be found of how to use pypam (under development).

Under development

Planned:

  • Add function to generate files per included folder (too big deployments)
  • Add options for the user to choose what to do when the blocksize is not multiple of the frames, and to deal with time keeping
  • Add a logger that logs the code that was run and the warnings together with the output
  • Add deep learning feature extraction (vggish and compatibility with koogu and AVES)
  • Add parallel processing options
  • Add support for frequency calibration
  • Support for reading detections

Cite

Parcerisas, C. (2023). PyPAM: a package to process long-term underwater acoustics data in chunks (0.3.0). Zenodo. https://doi.org/10.5281/zenodo.10037826

Acknowledgements

We want to thank Stan Panier for the beautiful logo. This project was possible due to the funding on LifeWatch Belgium


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lifewatch_pypam-0.4.0.tar.gz (60.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lifewatch_pypam-0.4.0-py3-none-any.whl (64.1 kB view details)

Uploaded Python 3

File details

Details for the file lifewatch_pypam-0.4.0.tar.gz.

File metadata

  • Download URL: lifewatch_pypam-0.4.0.tar.gz
  • Upload date:
  • Size: 60.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.11.9 Windows/10

File hashes

Hashes for lifewatch_pypam-0.4.0.tar.gz
Algorithm Hash digest
SHA256 db39dca334ccf6e031beef306c61953740ab1ef7b95cdc471bd1bed4b37d2ec4
MD5 25b002edc03052e490b653b1f1778342
BLAKE2b-256 857efd303f13511df512ef4fe47cf2499c757627d9d342c5d5e656988e595692

See more details on using hashes here.

File details

Details for the file lifewatch_pypam-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: lifewatch_pypam-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 64.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.11.9 Windows/10

File hashes

Hashes for lifewatch_pypam-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 bbf21f762fcfa6880b47d852e2328dc23028afdc5f6e014f3e0ef266a213494a
MD5 a66bbfc22691f27790ee3f94f1bb49e9
BLAKE2b-256 f1120887fd0f886172a567c72adacee83a09cca82af4acec7cf3bd02588e9267

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page