Yanked

This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 0.3.18 instead.
Reason given by maintainers: Broken Alpha

EV-Utils

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
}

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evutils 0.3.13

EV-Utils

Overview

Core Philosophy

Inspirations & Related Work

Installation

From PyPi

From Git

Architecture

Quick API overview

io: Reading and Writing Events

utils

random

types

vis

Running tests

Benchmarks

& Roadmap

Acknowledgements

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1 maintainer

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`io`: Reading and Writing Events

`utils`

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