Underwater Acoustic Data Utilities

This package contains general reading and formatting utilities for common underwater acoustic data formats.

Warning

This package is currently under development. The API is subject to change.

Data Formats

SHRU

Woods Hole Oceanographic Institution's (WHOI) Single-Hydrophone Recording Unit (SHRU) binary data format. All code has been adapted from Y.T. Lin's original MATLAB code.

Note

SHRU data capability is currently implemented.

SIO

Scripps Institution of Oceanography's (SIO) binary data format. Code has been adapted from Hunter Akins and Aaron Thode, with contributions from Geoff Edelman, James Murray, and Dave Ensberg.

Note

SIO data capability is not yet implemented.

WAV

The common .wav audio file format. This is a standard format for storing audio data, and is widely supported by many software packages.

Note

WAV data capability is not yet implemented.

Installation

To install the package, run the following command:

pip install git+https://github.com/NeptuneProjects/uwac-data-utils.git

Usage

The API is designed to efficiently query large amounts of acoustic data. This is done by constructing a catalogue of the data files, which includes metadata such as the file path, start time, and sampling rate. The catalogue can then be queried to retrieve data in a specific time range. Two main classes are provided for this purpose: datautils.query.FileInfoQuery and datautils.query.CatalogueQuery. Each has its own convenient constructor functions which read query data from TOML files.

Data organization

Data organization is flexible due to the reliance on glob patterns to match files. However, data are generally assumed to be organized into distinct categories such as arrays, events, or sensors. For example, data might be organized into a directory structure according to multiple sensor arrays:

/data
├── ARRAY001
│   ├── 2009-05-22
│   │   ├── 11-00-00.D23
│   │   ├── 11-00-01.D23
│   │   ├── 11-00-02.D23
│   │   ├── ...
│   │   ├── 15-00-00.D23
│   │   ├── 15-00-01.D23
│   │   └── 15-00-02.D23
│   ├── 2009-05-23
│   │   ├── 11-00-00.D23
│   │   ├── 11-00-01.D23
│   │   ├── 11-00-02.D23
│   │   ├── ...
│   │   ├── 15-00-00.D23
│   │   ├── 15-00-01.D23
│   │   └── 15-00-02.D23
│   └── ...
├── ARRAY002
│   ├── ...
└── ...

Cataloguing data files with RecordCatalogue

Building a catalogue

To build a catalogue, information about the files must first be gathered. This is done by constructing a datautils.query.FileInfoQuery object, which is then used to build the catalogue. The FileInfoQuery object can be constructed direclty in a Python script, or more conveniently using a TOML file:

[ARRAY001]

[ARRAY001.data]
directory = "/data"
glob_pattern = "ARRAY001*/*.D23"
destination = "/data/cat"
file_format = "SHRU" # Optional

[ARRAY001.clock] # Optional
time_check_0 = 2009-05-22T11:00:00
time_check_1 = 2009-05-22T15:00:00
offset_0 = 200e-6
offset_1 = -1.080e-3

[ARRAY001.hydrophone] # Optional
fixed_gain = [3.0, 3.0, 3.0, 3.0] # dB
sensitivity = [200.0, 200.0, 200.0, 200.0] # dB
serial_number = [0, 1, 2, 3]

# [ARRAY002]
# ...

The above example specifies the following:

• [ARRAY001]: A unique identifier for a specific sensor or array.
• [ARRAY001.data]: Data file and catalogue information.
  • directory: The directory where the data files are located.
  • glob_pattern: A glob pattern to match the data files.
  • destination: The directory where the catalogue will be saved.
  • file_format: The format of the data files. This is optional, and will be inferred from the file extension if not provided.
• [ARRAY001.clock]: Optional metadata about the clock used to timestamp the data. This section is used to correct for clock drift. If not provided, clock drift is assumed to be zero.
  • time_check_0 and time_check_1: The start and end times of the data to be included in the catalogue. This is optional, and will include all data if not provided.
  • offset_0 and offset_1: The time offsets for the start and end times. This is optional, and will default to 0 if not provided.
• [ARRAY001.hydrophone]: Optional metadata about the hydrophone. This section is used to correct for hydrophone gain and sensitivity. If not provided, no corrections are applied.

Multiple arrays can be specified in the same file, as denoted by the additional [ARRAY002] section. The catalogue is contained within the datautils.catalogue.RecordCatalogue object as a polars.DataFrame. To build a catalogues for each of the arrays specified in the above TOML file, use the following code:

from datautils.catalogue import RecordCatalogue
from datautils.query import load_file_query

queries = load_file_query("path/to/query.toml")
catalogues = []
for query in queries:
    catalogues.append(RecordCatalogue.build_catalogue(query))

[print(catalogue.df) for catalogue in catalogues]

Saving a catalogue

The RecordCatalogue object has a save method save the catalogue to disk in .csv, .json, or .MAT formats.

# Save the catalogue to a CSV file (default)
catalogue.save("path/to/catalogue", fmt="csv")

# Save the catalogue to a JSON file
catalogue.save("path/to/catalogue", fmt="json")

# Save the catalogue to a MAT file
catalogue.save("path/to/catalogue", fmt="mat")

# Save the catalogue to all three formats
catalogue.save("path/to/catalogue", fmt=["csv", "json", "mat"])

Building and saving multiple catalogues

The datautils.query.build_and_save_catalogues function can be used to build and save multiple catalogues at once.

from datautils.catalogue import build_and_save_catalogues
from datautils.query import load_file_query

queries = load_file_query("path/to/query.toml")
build_and_save_catalogues(queries, fmt=["csv", "json", "mat"])

Loading a catalogue

A catalogue can be loaded from disk using the datautils.catalogue.load_catalogue function.

from datautils.catalogue import RecordCatalogue

catalogue = RecordCatalogue().load_catalogue("path/to/catalogue.csv")

Reading data using a CatalogueQuery

Once a catalogue has been built, data can be read from disk by querying the catalogue and returning data.

Constructing a catalogue query

Here again, the query can be constructed directly in a Python script, or more conveniently using a TOML file:

[ARRAY001]
catalogue = "path/to/catalogue.csv"
destination = "." # Not currently used
time_start = 2009-05-22T12:00:00
time_end = 2009-05-22T13:00:00
channels = [0, 1, 2, 3]

# [ARRAY002]
# ...

The above example specifies the following:

• [ARRAY001]: A unique identifier for a specific sensor or array.
  • catalogue: The path to the catalogue file.
  • destination: The directory where the data will be saved. This is not currently used.
  • time_start and time_end: The start and end times of the data to be read.
  • channels: The channels to be read.

Multiple arrays can be specified in the same file, as denoted by the additional [ARRAY002] section.

Reading data

To read data for each of the arrays specified in the above TOML file, use the following code:

from datautils.data import read
from datautils.query import load_catalogue_query

queries = load_catalogue_query("path/to/query.toml")
for query in queries:
    cat_query = datautils.query.CatalogueQuery(
          catalogue=query.catalogue,
          destination=query.destination,
          time_start=query.time_start,
          time_end=query.time_end,
          channels=query.channels,
      )
      datastream = read(cat_query)

      # Do something with the datastream
      print(datastream.waveform)

The returned datastream is a datautils.data.DataStream object, which contains the following attributes:

• waveform: The data as a numpy.ndarray with shape (n_samples, n_channels).
• stats: An object containing relevant metadata about the data.
• time: The time of the data as a numpy.ndarray with shape (n_samples,).

Acknowledgements

Package written by William Jenkins, with code derived from:

• Y.T. Lin
• Hunter Akins, Aaron Thode, Geoff Edelman, James Murray, and Dave Ensberg
• The ObsPy Development Team