
Though columnar file formats are popular among HEP users, the process to convert between file formats has multiple steps, and generally requires the use of one I/O package per file format. Often users need to customize the process as well, either due to memory constraints or to modify the data before writing it to a new file. This entails both more lines of code and experience with I/O packages, and in some cases knowledge about each data format.To streamline this process and save user’s time, we are developing the Python package ‘hepconvert.’ This package aims to simplify columnar file conversions and common customizations down to single function between file formats Parquet, ROOT, and HDF5 files. It uses pre-existing functions from reputable columnar I/O packages such as Uproot, Awkward Array, and h5py, with additional builtin features for common customizations. The customizations are added at user request and include automatic reading and writing in batches, compression setting, branch skimming and slimming, histogram summing, and more. In addition to making the features in hepconvert, we are also adding relevant functionality to Uproot that will eventually be included in hepconvert; adding new TBranches to existing TTrees.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
