Library for easily processing big netCDF files and xarrays, and manage them in hierarchical processing flows.

Ever want to manipulate databases of huge xarrays in an easy, efficient, memory-friendly, and traceable way? You may have noticed memory limitations and loss of overview of all the sorts of the datasets. This library aims to tackle these challenges.

On the one hand, this package implements an apply_func routine that circumvents the memory issue by automatically dividing the xarray into chunks that fit your computer memory. On the fly writing to disk, parallel processing of (overlapping) chunks are supported simultaneously.

On the other hand, the library allows anyone to create modular data processing flows for processing different datasets with different formats, in which the multiple processes are executed in a hierarchical order and in parallel where possible. Herewidth, It implements a data management framework in which process arguments on the one hand and xarray (netcdf) attributes on the other hand are propagated in between the processing modules. Therefore, attributes from the xarray/netcdf files in the databases are tracked with pandas tables.While executing the multiple processing steps, the Pynacolada library checks whether particular data is already (being) generated. In that case, another build of the data is omitted, and only the reference (pandas table) to the existing data is provided instead.

The library is currently used for different projects climate services developed by the Climate team of the Flemish Institute for Technological Research (VITO) in Belgium, amongst others, https://climtag.vito.be. For examples and usage, please contact [email protected].