Metadata-Version: 2.4
Name: cluster_qc
Version: 2.0.0
Summary: A package for spatial filtering of Argo profiles using a point-in-polygon algorithm and cluster-based quality control analysis.
Home-page: https://github.com/romeroqe/cluster_qc
Author: Emmanuel Romero
Author-email: romeroqe@gmail.com
License: CC-BY-4.0
Classifier: Programming Language :: Python :: 3
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: cartopy
Requires-Dist: gsw
Requires-Dist: xarray
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: shapely
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
Dynamic: summary

# cluster_qc

Argo data goes through two quality processes, in real time and in delayed mode. This library contains the code used to develop the methodology of a publication that is currently under review, filters profiles within a given irregular polygon and offers two filtering methods to discard only the real time quality control data that present salinity drifts, this allows researchers to have a greater amount of data within the study area of their interest in a matter of minutes, as opposed to waiting for quality control in delayed mode that takes up to 12 months to complete. In addition, it provides tools to facilitate the download of the source files of this data and to generate diagrams.

## Installation

To install this package, first clone it on your computer or download the zip file. Then access to its root folder and install it with the command:

```
pip install cluster_qc
```

## Demos

To see package demos go to the `demo` folder and run the files:

- demo1.py: Download the list of profiles and filter them using the point-in-polygon algorithm.
- demo2.py: Extract data from NetCDF file profiles and convert to CSV.
- demo3.py: Visualizing Hydrographic Profiles from an Argo Float.
- demo4.py: Perform group analysis on the data to filter the data in RTQC that contains the same patterns as the DMQC data.

## How to cite

> [!IMPORTANT]
> _If you use this repository, please include a reference to the following:_
> 
> Romero, E., Tenorio-Fernandez, L., Castro, I., and Castro, M.: Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time, Ocean Sci., 17, 1273–1284, https://doi.org/10.5194/os-17-1273-2021, 2021.

## Argo data acknowledgment
These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. ([http://www.argo.ucsd.edu](http://www.argo.ucsd.edu), [http://argo.jcommops.org](http://argo.jcommops.org)). The Argo Program is part of the Global Ocean Observing System.

## License

This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
