System for automated Quality Control (SaQC)

Anomalies and errors are the rule not the exception when working with time series data. This is especially true, if such data originates from in-situ measurements of environmental properties. Almost all applications, however, implicily rely on data, that complies with some definition of 'correct'. In order to infer reliable data products and tools, there is no alternative to quality control. SaQC provides all the building blocks to comfortably bridge the gap between 'usually faulty' and 'expected to be corrected' in a accessible, consistent, objective and reproducible way.

For a (continously improving) overview of features, typical usage patterns, the specific system components and how to customize SaQC to your specific needs, please refer to our online documentation.

• System for automated Quality Control (SaQC)
  • Changelog
  • Installation
  • Usage
    • SaQC as a command line application
    • SaQC as a python module
  • Contributing
  • Copyright and License
  • Acknowledgements
  • Publications
  • How to cite SaQC

Changelog

All notable changes to this project will be documented in CHANGELOG.md.

Installation

SaQC is available on the Python Package Index (PyPI) and can be installed using pip:

python -m pip install saqc

For a more detailed installion guide, see the installation guide.

Usage

SaQC is both, a command line application controlled by a text based configuration and a python module with a simple API.

SaQC as a command line application

The command line application is controlled by a semicolon-separated text file listing the variables in the dataset and the routines to inspect, quality control and/or process them. The content of such a configuration could look like this:

varname    ; test
#----------; -----------------------------------------------------
SM2        ; shift(freq="15Min")
'SM(1|2)+' ; flagMissing()
SM1        ; flagRange(min=10, max=60)
SM2        ; flagRange(min=10, max=40)
SM2        ; flagMAD(window="30d", z=3.5)
Dummy      ; flagGeneric(field=["SM1", "SM2"], func=(isflagged(x) | isflagged(y)))

As soon as the basic inputs, dataset and configuration file, are prepared, run SaQC:

saqc \
    --config PATH_TO_CONFIGURATION \
    --data PATH_TO_DATA \
    --outfile PATH_TO_OUTPUT

A full SaQC run against provided example data can be invoked with:

saqc \
    --config https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/config.csv \
    --data https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/data.csv \
    --outfile saqc_test.csv

SaQC as a python module

The following snippet implements the same configuration given above through the Python-API:

import pandas as pd
from saqc import SaQC

data = pd.read_csv(
    "https://git.ufz.de/rdm-software/saqc/raw/develop/ressources/data/data.csv",
    index_col=0, parse_dates=True,
)

saqc = SaQC(data=data)
saqc = (saqc
        .shift("SM2", freq="15Min")
        .flagMissing("SM(1|2)+", regex=True)
        .flagRange("SM1", min=10, max=60)
        .flagRange("SM2", min=10, max=40)
        .flagMAD("SM2", window="30d", z=3.5)
        .flagGeneric(field=["SM1", "SM2"], target="Dummy", func=lambda x, y: (isflagged(x) | isflagged(y))))

A more detailed description of the Python API is available in the respective section of the documentation.

Contributing

You found a bug or you want to suggest some cool features? Please refer to our contributing guidelines to see how you can contribute to SaQC.

Copyright and License

Copyright(c) 2021, Helmholtz-Zentrum für Umweltforschung GmbH -- UFZ. All rights reserved.

• Documentation: Creative Commons Attribution 4.0 International
• Source code: GNU General Public License 3

For full details, see LICENSE.

Acknowledgements

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Publications

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How to cite SaQC

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