Set of tools to post-process raw instrument data in yadg's datagram format, NetCDF files, and tabulated data imported into pd.DataFrames.
dgpost is indended to be used as part of your data processing pipeline, and works best with a series of timestamped data.
Write a Recipe in yaml, and post-process your data from NetCDF files, pd.DataFrames, or yadg.datagrams in a reproducible fashion, while keeping provenance information, and without touching the original data files.
Post-process your data into pre-defined figures for your reports, or simply export your collated pd.DataFrame into one of the several supported formats!
Use dgpost in your Jupyter notebooks by importing it as a python package: import dgpost.utils to access the top-level functions for loading, extracting and exporting data; or import dgpost.transform to access the library of validated transform functions.
dgpost can load data from multiple file formats, extract data from those files into pd.DataFrames and automatically interpolate the datapoints along the time-axis (generally the index of the pd.DataFrame) as necessary, pivot selected columns of the tables using another column as index, transform the created tables using functions from the built-in library, plot data from those tables using its matplotlib interface, and save the tables into several output formats.
Of course, dgpost is fully unit-aware, and supports values with uncertainties by using the pint.Quantity and uncertainties.ufloat under the hood.
For a further overview of features, see the project documentation.