Acoustic Localization Toolkit using Acoustic-tags

Overview

The alts.py script is a comprehensive toolkit designed for acoustic line transect analysis with 2 Acoustic Tags. It provides functionality for calculating pulse intervals, filtering data based on specific conditions, and visualizing sound pressure level (SPL) distributions and cross-correlations. This toolkit is intended for researchers and analysts working with acoustic line transect dataset, particularly those involving localization and range detection.

Features

• Pulse Interval Calculation: Computes the pulse interval between consecutive acoustic events.
• Mean Angle Calculation: Estimates the mean angle of pulses within a time bin.
• Filtering Conditions: Offers multiple filtering options for noise conditions, from raw data viewing to advanced parameter settings for noisy environments.
• Data Visualization: Generates plots for SPL time distribution, SPL histograms, and cross-correlation between different SPL measurements.

Installation

To use the alts.py script, you need to have Python installed along with the following libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scipy

Usage

Pulse Interval Calculation

To calculate the pulse interval between consecutive acoustic events, use the calculate_pulse_interval function:

from alts import alts_filter

# Assuming df is your DataFrame containing a 'datetime' column
df = alts_filter.calculate_pulse_interval(df)

Filtering Conditions

You can filter your data based on different noise conditions:

# For raw viewer without any filter
alts_filter.filter_condition(noise_condition='1')

# For mild filter, recommended for most cases
alts_filter.filter_condition(noise_condition='2')

# For very noisy conditions
alts_filter.filter_condition(noise_condition='3')

# Advanced parameter settings
alts_filter.filter_condition(noise_condition='4')

Data Visualization

The toolkit includes several functions for visualizing SPL data:

• SPL Time Distribution Plot

alts_filter.spl_time_distribution_plot(df)

• SPL Distribution Plot

alts_filter.spl_distribution_plot(df)

• SPL Cross-Correlation Plot

alts_filter.spl_cross_corr(df)

Bokeh Visualization Plot

The toolkit includes a function for Bokeh visualization of acoustic data results:

plot_alts_result

This function visualizes acoustic data using Bokeh. The parameters are:

• dff: DataFrame containing the filtered data.
• dfr: DataFrame containing the raw data.
• time_diff: Time difference parameter.
• atag: Annotation tag for the plot.

Example usage:

from alts import alts_visualization

# Assuming dff and dfr are your DataFrames containing filtered and raw data respectively
alts_visualization.plot_alts_result(dff=dff, dfr=dfr, time_diff=0, atag=2)