A tool for reading, converting, and plotting sensor data from different oceanographic formats.

• Installation
• How to Use SeaSenseLib
• CLI Usage
• Example data
  • Converting a CNV file to netCDF
  • Showing the summary of a netCDF
  • Plotting a T-S diagram, depth profile and time series
• Extending SeaSenseLib with Plugins
• Development

To install SeaSenseLib, we strongly recommend using a scientific Python distribution. If you already have Python, you can install SeaSenseLib with:

pip install seasenselib

Now you're ready to use the library.

SeaSenseLib is designed to make working with oceanographic data easy and intuitive, whether you're analyzing CTD profiles, processing mooring data, or creating publication-ready plots in Jupyter notebooks.

The most common workflow: read sensor data, analyze it, create plots, and save results.

import seasenselib as ssl

# 1. Read CTD data (auto-detects .cnv format)
ds = ssl.read("profile.cnv")

# 2. Quick data overview
print(ds)

# 3. Create plots
ssl.plot('time-series', ds, parameters=['temperature', 'salinity'])
ssl.plot('ts-diagram', ds) 

# 4. Save data as netCDF (auto-detects .nc format)
ssl.write(ds, 'profile.nc')

SeaSenseLib supports different oceanographic instruments. Here's how to work with different formats by specifying the format or letting it auto-detect based on file extension:

import seasenselib as ssl

# Seabird CTD data
sbe_data = ssl.read("station_001.cnv", file_format='sbe-cnv')

# RBR logger data  
rbr_data = ssl.read("temperature_logger.rsk", file_format='rbr-rsk')

# See all supported readers
readers = ssl.list_readers()
for reader in readers:
    print(f"- {reader['key']:<20} : {reader['name']} ")

# Auto-detect format from file extension
data = ssl.read("myfile.cnv")  # Automatically detects 'sbe-cnv'

Example code for using SeaSenseLib with explicit usage of reader, writer, and plotter classes:

import seasenselib as ssl

# Read CTD data from CNV file
reader = ssl.readers.SbeCnvReader("profile.cnv")
ds = reader.data

# Write dataset with CTD data to netCDF file
writer = ssl.writers.NetCdfWriter(ds)
writer.write('profile.nc')

# Plot CTD data
plotter = ssl.plotters.TimeSeriesPlotter(ds)
plotter.plot(parameters=['temperature'])

You can use the library for reading, converting, and plotting data based on different sensor files. This chapter describes how to run the program from CLI.

After installing as a Python package, you can run it via CLI by just using the package name:

seasenselib

The various features of the library can be executed by using different commands. To invoke a command, simply append it as an argument to the program call via CLI (see following example section for some examples). The following table gives a short overview of the available commands.

Every command uses different parameters. To get more information about how to use the program and each command, just run it with the --help (or -h) argument:

seasenselib --help

To get help for a single command, add --help (or -h) argument after typing the command name:

seasenselib convert --help

In the examples directory of the code repository you'll find example files from real research cruises.

• The file sea-practical-2023.cnv contains data from a vertical CTD profile (one downcast) with parameters temperature, salinity, pressure, oxygen, turbidity.
• The file denmark-strait-ds-m1-17.cnv contains data from an instrument moored over six days in a depth of around 650 m with parameters temperature, salinity, pressure.

The following examples will guide you through all available commands using the file sea-practical-2023.cnv. (Please note: these examples are the simplest way to work with data. The behavior of the program can be adjusted with additional arguments, as you can figure out by calling the help via CLI.)

Use the following command to convert a CNV file to a netCDF file:

seasenselib convert -i examples/sea-practical-2023.cnv -o output/sea-practical-2023.nc

As you can see, format detection works for this command via file extension (.nc for netCDF or .csv for CSV), but you can also specify it via argument --format (or -f).

Important note: Our example files work out of the box. But in some cases your Seabird CNV files are using column names (so called "channels") for the parameter values, which are not known of our program or the pycnv library which we're using. If you get an error due to missing parameters while converting or if you miss parameters during further data processing, e.g. something essential like the temperature, then a parameter mapping might be necessary. A parameter mapping is performed with the argument --mapping (or -m), which is followed by a list of mapping pairs separated with spaces. A mapping pair consists of a standard parameter name that we use within the program and the corresponding name of the column or channel from the Seabird CNV file. Example for a mapping which works for the example above:

seasenselib convert -i examples/sea-practical-2023.cnv -o output/sea-practical-2023.nc -m temperature=tv290C pressure=prdM salinity=sal00 depth=depSM

For the created netCDF file:

seasenselib show -i output/sea-practical-2023.nc

Format detection works also for this command via file extension (.nc for netCDF).

Plot a T-S diagram:

seasenselib plot ts-diagram -i examples/sea-practical-2023.cnv

Plot a CTD depth profile:

seasenselib plot depth-profile -i examples/sea-practical-2023.cnv

Plot a time series for 'temperature' parameter:

seasenselib plot time-series -i examples/sea-practical-2023.cnv -p temperature salinity --dual-axis

To save the plots into a file instead showing on screen, just add the parameter --output (or -o) followed by the path of the output file. The file extension determines in which format the plot is saved. Use .png for PNG, .pdf for PDF, and .svg for SVG.

SeaSenseLib supports a plugin system that allows you to add support for additional data formats without modifying the core library. Plugins use Python entry points for automatic discovery.

1. Install the example plugin:

pip install examples/example-plugin

2. Use it immediately:

# Plugin appears automatically (here: example-json)
seasenselib list readers

# Use like any built-in format
seasenselib convert -i examples/example-plugin/data.json -o output.nc

1. Create a reader class:

# my_plugin/my_reader.py
from seasenselib.readers.base import AbstractReader
import xarray as xr

class MyFormatReader(AbstractReader):
    def __init__(self, input_file: str):
        self.input_file = input_file
        self._read_file()

    def _read_file(self):
        # Implement your file reading logic here.
        # For example, read the file and store data in self.data
        pass
    
    @staticmethod
    def format_key() -> str:
        return "my-format"
    
    @staticmethod
    def format_name() -> str:
        return "My Custom Format"
    
    @staticmethod
    def file_extension() -> str:
        return ".myf"

2. Register via entry points in pyproject.toml:

[project.entry-points."seasenselib.readers"]
my_format = "my_plugin.my_reader:MyFormatReader"

3. Install and use:

pip install -e .
seasenselib convert -i data.myf -o output.nc 

Your plugin must:

• Inherit from AbstractReader, AbstractWriter, or AbstractPlotter
• Implement format_key() and format_name() class methods (using @classmethod)
• Provide a data property (for readers) or write() method (for writers)

• Example Plugin - Working reference implementation (JSON reader/writer)
• Entry Point Groups: seasenselib.readers, seasenselib.writers, seasenselib.plotters

Start here to set up your local development environment: clone the repository, create and activate a Python virtual environment, install all dependencies, and run tests or build the package. These steps ensure you work in an isolated, reproducible setup so you can experiment with the code, add new features, or fix issues before submitting changes.

1. Clone the repo

   git clone https://github.com/ocean-uhh/seasenselib.git
   cd seasenselib

2. Create and activate a virtual environment

   • Linux/macOS:

     python3 -m venv venv
     source venv/bin/activate

   • Windows (CMD):

     python -m venv venv
     venv\Scripts\activate.bat
     

   • Windows (PowerShell):

     python -m venv venv
     venv\Scripts\Activate.ps1
     

3. Upgrade packaging tools and install dependencies

   pip install --upgrade pip setuptools wheel
   pip install -e ".[dev]"

The environment is now ready.

Useful commands:

• Run tests

  python -m unittest discover tests/

• Execute the application

  python -m seasenselib

• Build distributions

  python -m build

• Deactivate/Quit the virtual environment

  deactivate