Welcome to the documentation page for larcv.

Here, we give an overview of the purpose and use cases of larcv, as well as some overview of required dependencies and installation instructions.

As a summary, larcv is a storage framework for sparse data, typically “voxelized” data (think images) but work for Point Cloud data is also in progress. larcv was born from the MicroBooNE experiment at Fermilab, near Chicago, Illinois. MicroBooNE is a liquid argon time projection chamber, and larcv came about to solve IO issues when storing and retrieving sparse data for machine learning applications. The name is a combination of L iquid Ar gon C omputer V ision, originally as LArCV.


LArCV was originally built in C++, using the ROOT framework for IO and data manipulation. As machine learning applications moved to bigger systems, we adapted larcv to use more industry-standard tools (HDF5 instead of ROOT). We also adapted the data products storable here to be more genericlly useful beyond the MicroBooNE experiment. To not inhibit experiments already using a verion of larcv at the time, we forked the repository and allowed larcv2 (an update to LArCV) and larcv3 to coexist. You’ve found your way to the documentation for larcv3 - apart from this introduction section, anywhere in these docs where you find larcv, we’re refering to larcv3. The repositories for each are:



Now, we use pybind11 to connect the C++ API to Python, and JSON for configuration - an attempt to build a software tool on top of common, open source tools.

Use Cases

larcv is open source and you can use it, modify it, etc., for whatever purpose is needed. If you’re thinking about larcv for your project, here are some good criteria your project should meet for larcv to be useful for you:

  • Your application is either in python or C++. larcv isn’t supported in other languages with no intention to branch out at this time.

  • Your application needs to use sparse input data, probably for machine learning. Right now that means sparse data in a regular, voxelized space, eventually that will include points in more general space, or;

  • Your application uses dense but very regular data. Meaning, the image size for each iteration of your algorithm is the same everytime. larcv does not have restrictions on image size, but dense data is optimized for readback when the images are regular.

  • You need to improve the speed of IO in your application at scale. larcv has native scalability for IO readback at the scale of the largest super computers, up to 30,000+ MPI Ranks.

If this suits your needs, larcv might be useful to you. If it seems almost useful but missing some features, please feel free to open an issue or feature request on our github page.