sonarlight

The sonarlight package provides tools for working with Lowrance sonar data in the .sl2 and .sl3 formats. The package includes the Sonar class for reading and parsing these files, as well as methods for extracting various types of information from the data.

Project home is https://github.com/KennethTM/sonarlight.

Installation

To install the sonarlight package, simply run:

pip install sonarlight

The package requires the numpy and pandas packages to be installed.

Example data

Small example files of the .sl2 and .sl3 formats are provided in the example_files folder.

Usage

Once installed, you can use the Sonar class to read and parse sonar data from a .sl2 or .sl3 file.

When reading a file with Sonar() with argument clean=True (default) some light data cleaning is performed including dropping unknown columns and rows and observation where the water depth is 0. Setting augment_coords=True performs augmentation of the recorded coordinates as implemented in SL3Reader. Coordinate augmentation attempts to make up for the reduced precision in the recorded coordinates which are rounded to the nearest meter.

The class contains a few methods for extracting data:

• Sonar.image() method to extract the raw sonar image for a specific channel
• Sonar.sidescan_xyz() method to extract georeferenced sidescan data as XYZ coordinates
• Sonar.water() method to extract the water column part of the the raw sonar imagery for a specific channel
• Sonar.bottom() method to extract the bottom (sediment) part of the the raw sonar imagery for a specific channel
• Sonar.bottom_intensity() method to extract raw sonar intensity at the bottom

The functionality of the class is showcased below and included in the example_notebook.ipynb.

Example of reading a sonar file using the example_files/example_sl2_file.sl2 file:

from sonarlight import Sonar

#Read data from a '.sl2' or '.sl3' file
sl2 = Sonar('path/to/file.sl2')

#See summary of data and available channels
sl2

#Output:
'''
Summary of SL2 file:

- Primary channel with 3182 frames
- Secondary channel with 3182 frames
- Downscan channel with 3182 frames
- Sidescan channel with 3181 frames

Start time: 2023-09-13 08:20:36.840000
End time: 2023-09-13 08:22:52.770999808

File info: version 2, device 2, blocksize 3200, frame version 8
'''

#View raw data store in Pandas dataframe
sl2.df

#Each row contains metadata and pixel for each recorded frame.
#Pixels are stored in the "frames" column.
#The dataframe can be saved for further processing, 
#for example the Parquet file format that supports nested data structues.
sl2.df.to_parquet('sl2.parquet')

#Or to '.csv' file
sl2.df.to_csv("sl2.csv")

#Or to '.csv' file after dropping the "frames" column containing nested arrays
df_csv = sl2.df.copy().drop(["frames"], axis=1)
df_csv.to_csv("sl2.csv")

Examples of further processing and plotting (see also example_notebook.ipynb):

import matplotlib.pyplot as plt

#Plot route and water depth (meters)
route = sl2.df.query("survey == 'primary'")
plt.scatter(route["longitude"], route["latitude"], c=route["water_depth"], s = 3)
plt.colorbar()

#Plot primary channel
prim = sl2.image("primary")
plt.imshow(prim.transpose())

#Plot water column (surface to water_depth) from downscan channel
#Individual frames are linearly interpolated of length 'pixels'
downscan_water = sl2.water("downscan", pixels=300)
plt.imshow(downscan_water.transpose())

#Plot bottom column (water_depth to max sonar range) from primary channel
#Individual frames are subsetted to match the minimum length of the bottom frames
secondary_bottom = sl2.bottom("secondary")
plt.imshow(secondary_bottom.transpose())

#Plot sidescan georeferenced points
#Convert sidescan imagery to XYZ point cloud
#Note that this can result in MANY points, every 10'th point are plotted here
mosaic=sl2.sidescan_xyz()
plt.scatter(mosaic.x[::10], 
            mosaic.y[::10], 
            c=mosaic.z[::10], 
            cmap="cividis", s=0.1)

Ressources

The package is inspired by and builds upon other tools and descriptions for processing Lowrance sonar data, e.g. SL3Reader which includes a usefull paper, python-sllib, sonaR, Navico_SLG_Format notes, older blog post.

TODO

• Notebook with extended processing examples.
• Improve memory/speed efficiency. The package can process large files (>1 GB) rather snappy but does consume some RAM.
• Coordinate augmentation/correction.
• Parse 3D data.

Other image examples