SaQC
is a tool/framework/application to quality control time series data.
It provides
a growing collection of algorithms and methods to analyze, annotate and
process timeseries data. It supports the end to end enrichment of metadata
and provides various user interfaces: 1) a Python API, 2) a command line interface
with a text based configuration system and a
web based user interface
SaQC
is designed with a particular focus on the needs of active data professionals,
including sensor hardware-oriented engineers, domain experts, and data scientists,
all of whom can benefit from its capabilities to improve the quality standards of given data products.
For a (continously improving) overview of features, typical usage patterns,
the specific system components and how to customize SaQC
to your own
needs, please refer to our
online documentation.
SaQC
is available on the Python Package Index (PyPI) and
can be installed using pip:
python -m pip install saqc
Additionally SaQC
is available via conda and can be installed with:
conda create -c conda-forge -n saqc saqc
For more details, see the installation guide.
SaQC
is both, a command line application controlled by a text based configuration
and a python module with a simple API.
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 ; align(freq="15Min")
'SM(1|2)+' ; flagMissing()
SM1 ; flagRange(min=10, max=60)
SM2 ; flagRange(min=10, max=40)
SM2 ; flagZScore(window="30d", thresh=3.5, method='modified', center=False)
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/docs/resources/data/config.csv \
--data https://git.ufz.de/rdm-software/saqc/raw/develop/docs/resources/data/data.csv \
--outfile saqc_test.csv
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/docs/resources/data/data.csv",
index_col=0, parse_dates=True,
)
qc = SaQC(data=data)
qc = (qc
.align("SM2", freq="15Min")
.flagMissing("SM(1|2)+", regex=True)
.flagRange("SM1", min=10, max=60)
.flagRange("SM2", min=10, max=40)
.flagZScore("SM2", window="30d", thresh=3.5, method='modified', center=False)
.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.
You found a bug or you want to suggest new features? Please refer to our contributing guidelines to see how you can contribute to SaQC.
If you need help or have questions, send us an email to [email protected]
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.
Lennart Schmidt, David Schäfer, Juliane Geller, Peter Lünenschloss, Bert Palm, Karsten Rinke, Corinna Rebmann, Michael Rode, Jan Bumberger, System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science, Environmental Modelling & Software, 2023, 105809, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2023.105809. (https://www.sciencedirect.com/science/article/pii/S1364815223001950)
If SaQC is advancing your research, please cite as:
Schäfer, David, Palm, Bert, Lünenschloß, Peter, Schmidt, Lennart, & Bumberger, Jan. (2023). System for automated Quality Control - SaQC (2.3.0). Zenodo. https://doi.org/10.5281/zenodo.5888547
or
Lennart Schmidt, David Schäfer, Juliane Geller, Peter Lünenschloss, Bert Palm, Karsten Rinke, Corinna Rebmann, Michael Rode, Jan Bumberger, System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science, Environmental Modelling & Software, 2023, 105809, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2023.105809. (https://www.sciencedirect.com/science/article/pii/S1364815223001950)