evutils_logo EV-Utils

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Overview

EV-Utils (evutils) is a performant collection of utilities for working with event-based vision data. Built with minimal dependencies, it relies on a compiled C backend for speed while offering a clean, modular Python interface.

Core Philosophy

  • Fast & Lightweight: Highly optimized C parsers for zero-bottleneck data ingestion.
  • Minimal Footprint: Core features run entirely on NumPy and Numba.
  • Lazy Loading: All heavy integrations (PyTorch, Pandas, HDF5, Polars, etc.) are lazy-loaded. If you don't use them, you don't need them installed, and they won't slow down import times.
  • Simple & Extensible: Clean modular APIs.

Inspirations & Related Work

This project draws inspiration from several excellent libraries in the event-based vision ecosystem and attempts to fill in their shortcomings:

Installation

We recommend installing evutils using uv.

From PyPi

uv add evutils # Basic library
uv add evutils[all] # All groups (pandas, numba, torch, hdf5, etc..)
uv add evutils[dev] # Dev group

From Git

git clone --recurse-submodules https://github.com/mandulaj/evutils.git
cd evutils

uv pip install -e ".[dev]"

Note: You can also install specific optional dependency groups like uv add evutils[torch,hdf5].

Architecture

The library is divided into several discrete modules. Many can be used independently without installing the full suite of dependencies:

└── augment     - Event augmentations
└── dataset     - Wrappers for various dataset loaders
└── events      - Core event handling logic
└── io          - Event reading and writing interfaces
    ├── reader 
    └── writer
└── random      - Random event generation and noise injection
└── torch       - PyTorch integration (requires evutils[torch])
└── types       - Standard types for representing Events in NumPy arrays
└── vis         - Visualization methods
    ├── histogram
    └── reconstructor

Quick API overview

io: Reading and Writing Events

The io module provides methods for reading and writing events into various event formats. It provides a simple .read() and .write() interface as well as more advanced interfaces using iterators and slicing.

Supported formats (see the formats documentation for details):

Format Extensions Read Write Notes
EVT3 / EVT2.1 / EVT2 (Prophesee RAW) .raw, .evt* ✅ ✅ native C decoder, external triggers
DAT (Prophesee) .dat ✅ ✅ native C decoder
AER (Prophesee) .aer ✅ ✅ timestamp generation selectable
AEDAT 1.0 / 2.0 / 3.1 / 4.0 .aedat, .aedat4 ✅ 🚧 AEDAT4 compression: evutils[aedat]
HDF5 (DSEC/RVT layout) .h5, .hdf5 ✅ ✅ evutils[hdf5], ms-index random access
HDF5 (Prophesee layout) .h5, .hdf5 ✅ 🚧 ECF-compressed files need the ECF plugin
NPZ .npz ✅ ✅ streaming, np.load-compatible
CSV / TXT .csv, .txt ✅ ✅ evutils[pandas]
BIN .bin 🚧 🚧 planned 
from evutils.io import EventReader


ev_file = EventReader("raw_file.raw", delta_t=10e3)

events = ev_file.read()

utils

Various utility functions

random

Generating random events and adding noise to event recordings

types

This provides several standard types for representing Events in numpy arrays

vis

The vis moduels provides several methods for visualizing the events (for example as histograms), but also provides a streamlined interface for more complex visualization techneques, such as using the E2Vid reconstructor.

from evutils.vis.reconstructor import RPG_Reconstructor

reconstructor = RPG_Reconstructor(1280, 720)

img = reconstructor.gen_frame(events)

Running tests

Tests are managed via pytest. If you installed the package with the [dev] or [test] flag, you can run them via:

uv run pytest -s

Benchmarks

Read/write throughput benchmarks (using pytest-benchmark) live in benchmarks/ and are kept out of the normal test run. Run them explicitly:

uv run pytest benchmarks/                                   # evutils only
uv run pytest benchmarks/ --benchmark-group-by=param:fmt    # compare libraries per format

The benchmarks download a real Prophesee recording on first use. Optional cross-library comparisons run automatically once the libraries are installed (uv pip install -e ".[compare]"); OpenEB/Metavision is compared via the Docker image in benchmarks/docker/. See benchmarks/README.md for details.

& Roadmap

We aim for universal event format support, prioritizing blazing fast read/write speeds, completeness, and extensibility.

  • Universal format support (.raw, .evt2, .dat, .aedat4, .hdf5, .npz, .csv, etc.)
  • Full Read/Write parity where possible
  • Chunked & Streaming access
  • External trigger data parsing
  • Random access / Timestamp indexing (Big TODO for the future)
  • Arbitrary input sources: memory-mapped IO, pure in-memory streams, HTTP streams
  • On-the-fly Compression wrappers: passing file handles through zstd or lz4 compression transparently before decoding

Acknowledgements

Thanks to all the contributors for supporting this project:

  • Elia Franc
  • Jakub Mandula

Cite

@PhDThesis{2024mandula_evutils,
  author        = {Jakub Mandula},
  title         = {EV-Utils: collection of utilities for working with event-based vision data},
  school        = {Dept. of Information Technology and Electrical Engineering, ETH Zurich},
  year          = 2024
}

Project links

Data verified by PyPI on Jul 6, 2026
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Key dates

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  • Released:
Newer release available (0.3.18)

1 maintainer

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Avatar for zpiman from gravatar.com zpiman

Credits

Author: Jakub Mandula

GitHub Statistics

Data verified by PyPI on Jul 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Copyright (c) 2024 Jakub Mandula

Requires

Python >=3.10

Provides Extra

aedat pandas torch hdf5 vis test compare docs all dev

Tags

events dvs neuromorphic vision event-based

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0.3.18

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