During measurements, several errors can affect the data. These errors often happen due to instrumentation malfunctions and will affect the data’s statistical properties. In order to detect the errors, we implemented a framework to perform QA/QC tests. Autom8QC provides tests to improve data quality. You can use tests alone, or combine them into a test group.
The software is maintained by the IAGOS Group at the Institute for Energy and Climate Research - Troposphere (IEK 8) at Forschungszentrum Jülich, Germany.
This software is under development and opened here primarily to allow a user to contribute, test, and bring in their ideas. At this point, the software is not fit for operational use and we assume no responsibility whatsoever for any damage that could be caused by this software.
You can install the package from the repository directly. If you want to adapt the framework or use it for development, it’s recommended to read the section Installation for Development. If you just want to use it as a user or for a production system, use the following command to install the latest version of the framework.
pip install git+https://jugit.fz-juelich.de/m.kennert/autom8qc
# Generate sample data import numpy as np import pandas as pd np.random.seed(42) mu, sigma = 50, 3 values = np.random.normal(mu, sigma, 500) values[100:140] = 48 values[250:] += 3 values[400:435] = 55 index = pd.date_range(start="1/1/2021", periods=500, freq="min") series = pd.Series(values, index=index) # Perform test and plot the results from autom8qc.qaqc.flatline import FlatLineTest test = FlatLineTest(stuck_points=30) test.plot(series=series, series_name="Example")