ctdam 1.1.3

Library to handle all steps when working with CTD data.

Intro

Welcome to the ctdam software package, a library to help with all the different stages in working with CTD data. For now, it focuses only on Sea-Bird CTD data, but we are open to extend to other CTD data types in the future. At the moment, the focus remains on fast and reliable conversion and processing of Sea-Birds .hex files. Data and metadata are stored inside python objects, with the possibility to export native .cnv files, as well as NetCDFs. In general, there are parsers for all the different Sea-Bird file formats: .hex, .xmlcon, .bl, .btl and .cnv .

Note

The full documentation can be found here.

Conversion

For conversion of raw CTD data, only a .hex file and its corresponding .xmlcon file are needed. Then, you can simply do:

from ctdam.conv import decode_hex

converted_ctd_data = decode_hex('sbs_data/hex/EMB356_11-1.hex')

This assumes that the .xmlcon resides in the same directory as the .hex and is also using a similar name. The code snippet would yield you a CTDData object, a very general representation of CTD data and metadata of a single cast. You could for example do some processing on this data.

Processing

To process CTD data, one needs to define workflow files, called ‘procedures’, .toml files which correspond to python dictionaries and e.g. look like this:

processing_config = {
    "output_type": "cnv",
    "output_dir": ".",
    "modules": {
        "airpressure": {},
        "wildedit_geomar": {'std2': 7},
        "wfilter": {},
        "celltm": {},
        "alignctd": {'Oxygen': 3},
        "SA_from_SP_Baltic": {},
        "binavg": {},
    },

}

All processing module behaviour can be modified via key-values, as seen for ‘wildedit_geomar’ and ‘alignctd’. In the example config you can also see, that the native Sea-Bird processing modules can be mixed with custom ones (airpressure) and all gsw functions (SA_from_SP_Baltic). The Sea-Bird ones are either taken from seabirdscientific or are more efficient rewrites that stick to the same core logic. Its also possible to use the original Sea-Bird processing binaries, as long as they are installed on your machine.

In order to process the converted data from above, you would go ahead and do:

from ctdam.proc import Procedure

processed_ctd_data = Procedure(processing_config).run(converted_ctd_data)

This again results in a CTDData instance, which you now could plot.

Plotting

This library uses bokeh for generating interactive plots, that can be viewed inside your webbrowser:

from ctdam.vis import basic_bokeh_plot

basic_bokeh_plot(processed_ctd_data)

The resulting plot of this operation can be seen here.

The .html files generated like this, are self-contained and can be shared easily without the need of the original data or external tooling.

Data export

To write the data back to disk, two output methods are currently supported, Sea-Bird-like .cnv and NetCDF. In order to write the converted and processed ‘EMB356_11-1.hex’ as .cnv to disk, just do:

processed_ctd_data.to_cnv('processed_EMB356_11-1.cnv')

CLI

Instead of working inside a python environment, the same results can also be obtained by using a command line interface, also called ‘ctdam’:

ctdam run sbs_data/hex/EMB356_11-1.hex proc_workflow.toml
ctdam plot EMB356_11-1.cnv -d '' -sm

With the first command, a processing workflow is used on a .hex file, which also means, that its converted automatically. The second command does plot the new .cnv file and save it in the same directory. Meaning that we have literaly done the exact steps like above.

Installation

The ctdam python package is distributed via PyPi, that means that you can install it inside your python environment using your favorite package manager:

uv add ctdam

poetry add ctdam

Or just plain old pip:

pip install ctdam

This installs only the functionalities. To use features like the CLI, plotting or a GUI to edit processing workflow files, you need to install ctdam with extra optional dependencies. That looks differently dependending on installation type:

uv add ctdam --extra cli

poetry add ctdam[gui]

pip install ctdam[vis]

If you don’t care about find-grained dependency management, you can also just install all of them with the ‘all’ group.

The CLI program can be installed similarly:

$ uv tool install --from 'ctdam[all]' ctdam

$ uv run ctdam check
All set, you are ready to go.

$ pipx install ctdam

$ ctdam check
All set, you are ready to go.

Context

This software is developed for the German Marine Research Alliance (DAM) in the context of the Underway Research Data Project. The converter and parser are tested against a variety of data, acquired on different German research vessels. Because of the ongoing efforts to harmonise these infrastructures, the diversity of the test data may be smaller than thought and your data may pose problems to converter, parser or processing. Please feel free to contribute to this project in order to develop a toolkit, that is as general as possible.