argodmqc-owc 0.1.10

OWC salinity calibration in Python

	pyowc is a python library for OWC salinity calibration in Python

This software is a python implementation of the "OWC" salinity calibration method used in Argo floats Delayed Mode Quality Control.

Post an issue to get involved if you're interested.

General Guidance

To use this software, you'll need Python, and ideally a virtual environment with the package installed.
A virtual environment is not absolutely essential — you can install the package globally — but it is recommended to avoid issues.

There are two ways of working with this software:

1. Installation and General Usage via PyPI

This method is intended for general usage, without modifying the codebase.
If you intend to use the software this way, follow the documentation from the General Usage section.

2. Installation and Development Work via GitHub

This method is intended for development work and gives access to the codebase, it is intended for those wanting to develop the code. If this is what you intend to do, follow the documentation from the Developer Usage section.

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Overview

To use the app installed via PyPi there are 3 steps to follow.

1. Pip install it

Run pip install argodmqc-owc

2. Setup folder structure The app requires a specific folder structure, and these folders & files are referenced in the config JSON file. The example structure here is reflected in the example config JSON file.

General Usage

To use the app installed via PyPi there are 3 steps to follow.

1. Pip install it

Run pip install argodmqc-owc

2. Setup folder structure

The app requires a specific folder structure, and these folders & files are referenced in the config JSON file. The example structure here is reflected in the example config JSON file. Please note that the data folder is available here

• data
  • climatology
    • historical_argo
    • historical_bot
    • historical_ctd
  • constants
    • bathymetry
    • coastline
    • reefs
  • float_calib
  • float_mapped
  • float_plots
  • float_source

3. Create the config JSON file

See the example config JSON file here

4. Run the software

Use the example script provided here here to run the DMQC software.

Please note that the config JSON is checked before any processing runs, so any errors in the config will be reported back.

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Developer Usage

Using the app as a developer requires git-cloning rather than pip-installing so direct access to the code is possible, and modifying the code is easy. To use the app as a developer it is recommended you follow the sections below in the order prescribed

1. Virtual Environments
2. Installing Poetry
3. Cloning the repository
4. Installing the dependencies
5. Running the linting & tests and docs builder
6. Executing the DMQC code

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Virtual Environments

A virtual environment is recommended to work in as the dependencies wont conflict with any globally installed packages.

To create a virtual environment:

• Mac/Linux

  python3 -m venv .venv

  source .venv/bin/activate

• Windows

  python -m venv .venv

  .\.venv\Scripts\Activate

Installing Poetry

Poetry is a dependency management tool and the software uses a pyproject.toml file to handle the dependencies.

To Install Poetry, run: pip install poetry

If any messages appear with 'poetry not found', try prefixing your command with python or python -m

Cloning the Repository

The repository can be cloned by clicking the green <> Code button near the top of the main page on Github. Follow the prompts to either clone it via the command line, or open with Github Desktop.

It is recommended to clone the repository to a new folder. Make sure you are in this folder with your virtual environment activated and the repository cloned before moving to the next step. You need to make sure you are at the same level as the pyproject.toml file.

Installing the dependencies

Install the dependencies with: poetry install --no-root

This will take a few seconds, and you should see a list of the installed packages in the terminal window.

Running the linting & tests and docs builder

The dependencies for running these utiltiies are also packaged up with Poetry, and they can be ran as follows:

Running the Linter with Poetry

poetry install --no-root --with lint

poetry run ruff check

Running the Docs Builder with Poetry

poetry install --with docs

cd docs

poetry run sphinx-build -M html source build -W

Running the Tests with Poetry

poetry install --with tests

poetry run pytest

Executing the DMQC code

Open the file start.py

Look at lines 15 & 16, these are to be changed if different floats are to be processed, or a different configuration is to be used.

Run the code (start.py): poetry run run-floats.

A short tutorial is available on the argopy documentation here.

For Python beginners, you can run the pyowc in this way:

In start.py, you can specify the WMO float number that you want to run the analysis on. You can also add more float numbers, then the calculations of all floats will be done at the same time.

    FLOAT_NAMES = ["3901960"]  # add float names here e.g. ["3901960","3901961","3901962"]

Parameters for your analysis

Parameters for the analysis are set in a configuration.py python code. The configuration has the same parameters as the Matlab software (https://github.com/ArgoDMQC/matlab_owc).

• You can change the default directories to locations of your historical data.

        #    Climatology Data Input Paths
        'HISTORICAL_DIRECTORY': "data/climatology/"
        'HISTORICAL_CTD_PREFIX': "/historical_ctd/ctd_"
        'HISTORICAL_BOTTLE_PREFIX': "/historical_bot/bot_"
        'HISTORICAL_ARGO_PREFIX': "/historical_argo/argo_"

• To run the analysis,you need to have the float source file in .mat format.

        #    Float Input Path
        'FLOAT_SOURCE_DIRECTORY': "data/float_source/"
        'FLOAT_SOURCE_POSTFIX': ".mat"

• The output from the analysis will be saved in default directory of the code.You can change the default directories to locations of your constants.

        #    Constants File Path
        'CONFIG_DIRECTORY': "data/constants/"
        'CONFIG_COASTLINES': "coastdat.mat"
        'CONFIG_WMO_BOXES': "wmo_boxes.mat"
        'CONFIG_SAF': "TypicalProfileAroundSAF.mat"

• Final step is to set your objective mapping parameters, e.g.

        'MAP_USE_PV': 0
        'MAP_USE_SAF': 0

        'MAPSCALE_LONGITUDE_LARGE': 8
        'MAPSCALE_LONGITUDE_SMALL': 4
        'MAPSCALE_LATITUDE_LARGE': 4
        'MAPSCALE_LATITUDE_SMALL': 2

• Additionally, you can set a specific ranges of theta bounds for salinity anomaly plot. The code will crete two separate plots with set ranges.

     #    Plotting Parameters
        # Theta bounds for salinity anomaly plot
        'THETA_BOUNDS': [[0, 5], [5, 20]]

Software history

• Major refactoring of the software for performance optimisation and to fully embrace the Pythonic way of doing this !

• UAT: Phase 1, 2, 3

• Migration of the code from from BODC/NOC git to euroargodev/argodmqc_owc. See https://github.com/euroargodev/User-Acceptance-Test-Python-version-of-the-OWC-tool/issues/10 for more details on the migration. Contribution from G. Maze

• Alpha experts user testings with feedbacks available here. Contributions from: K. Walicka, C. Cabanes, A. Wong

• BODC created the first version of the code, following the Matlab implementation. Contributions from: M. Donnelly, E. Small, K. Walicka, A. Hale, T. Gardner.