evlib 0.12.0

Event Camera Data Processing Library
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141

[package]
name            = "evlib"
version         = "0.12.0"
authors         = ["Tarek Allam <t.allam.jr@gmail.com>"]
edition         = "2021"
resolver        = "2"
description     = "Event Camera Data Processing Library"
readme          = "README.md"
license         = "MIT"
repository      = "https://github.com/tallamjr/evlib"
homepage        = "https://github.com/tallamjr/evlib"
documentation   = "https://docs.rs/evlib"
keywords        = ["event-camera", "vision", "rust", "python", "pyo3"]
categories      = ["science", "multimedia"]
exclude         = [
    "data/**/*",
    "dist/**/*",
    "examples/**/*",
    "python/**/*",
    "tests/**/*",
    "*.png",
    "*.mp4",
]

[lib]
name           = "evlib"
crate-type     = ["cdylib", "rlib"]

# Rust criterion benches live under benchmarks/rust/ (alongside the Python suite in benchmarks/),
# so the repo has a single top-level benchmarks/ folder rather than a separate benches/ dir.
[[bench]]
name = "memory_efficiency"
path = "benchmarks/rust/memory_efficiency.rs"
harness = false

[[bench]]
name = "streaming_performance"
path = "benchmarks/rust/streaming_performance.rs"
harness = false

[[bench]]
name = "format_comparison"
path = "benchmarks/rust/format_comparison.rs"
harness = false

[[bench]]
name = "basic_performance"
path = "benchmarks/rust/basic_performance.rs"
harness = false

[[bench]]
name = "standalone_benchmark"
path = "benchmarks/rust/standalone_benchmark.rs"
harness = false

[dependencies]
# Python bindings (optional)
numpy          = { version = "0.25.0", optional = true }
pyo3           = { version = "0.25.1", optional = true }
pyo3-ffi       = { version = "0.25.1", optional = true }

# Core data processing
ndarray        = "0.15.6"
ndarray-rand   = "0.14.0"
rand           = "=0.8.5"
rand_distr     = "=0.4.3"
fastrand       = "2.0"

# Torch-style tensor ops removed (was causing objc_exception compilation issues on Linux)
# candle-core    = { version = "0.9.1", default-features = false }
# candle-nn      = { version = "0.9.1", default-features = false }

# Polars for efficient event data processing (optional)
polars         = { version = "0.49.1", default-features = true, features = ["temporal", "dtype-u16", "dtype-i8", "lazy"], optional = true }
pyo3-polars    = { version = "0.22.0", optional = true }

# Apache Arrow for zero-copy data transfer (optional)
arrow          = { version = "55.0", default-features = false, optional = true }
arrow-array    = { version = "55.0", optional = true }
pyo3-arrow     = { version = "0.10.1", optional = true }

# I/O & utilities
memmap2        = "0.5"
lazy_static    = "1.4"
byteorder      = "1.5"
# LZ4 frame decompression for AEDAT 4.0 (DV framework) packet bodies
lz4_flex       = { version = "0.11", default-features = false, features = ["frame"] }

# Runtime loader for the nvcc-built CUDA scatter kernel (.so), used by the optional `cuda` backend.
libloading     = { version = "0.8", optional = true }

serde          = { version = "1.0", features = ["derive"] }
serde_json     = "1.0"
num_cpus       = "1.16"
thiserror      = "1.0"
crc32fast      = "1"
tokio          = { version = "1", features = ["net", "io-util", "rt", "macros"] }

# Platform-specific dependencies
# HDF5 and tracing only on Unix (Linux/macOS) - not available on Windows
# HDF5 crates are optional and enabled via the `hdf5` feature flag.
[target.'cfg(unix)'.dependencies]
hdf5-metno     = { version = "0.10.1", features = ["blosc-all"], optional = true }
hdf5-metno-sys = { version = "0.10.1", optional = true }
tracing        = { version = "0.1.40" }
tracing-subscriber = { version = "0.3.18", features = ["env-filter", "fmt", "json"] }

# Metal GPU scatter-add backend (Apple Silicon), opt-in via the `metal` feature. macOS only.
[target.'cfg(target_os = "macos")'.dependencies]
metal          = { version = "0.27", optional = true }
objc           = { version = "0.2", optional = true }

[dev-dependencies]
tempfile       = "3.8"
criterion      = { version = "0.5", features = ["html_reports"] }

[features]
# Default features - automatically platform-aware:
# - Unix (Linux/macOS): Includes all features (HDF5 and tracing available)
# - Windows: Excludes HDF5 and tracing (not in dependencies on Windows)
default        = ["polars", "python", "arrow"]

# Individual feature flags
python         = ["dep:pyo3", "dep:pyo3-ffi", "dep:numpy", "dep:pyo3-polars"]
# Opt-in PyO3 extension-module mode. Required ONLY for distributable wheels
# (manylinux/macOS/Windows) so the .so does not link libpython directly.
# Deliberately NOT in `default`: with it off, pyo3's build script links the
# present libpython, which lets `cargo test` and `maturin develop` build and
# run without any RUSTFLAGS hack.
extension-module = ["pyo3/extension-module"]
# CUDA scatter-add backend: loads an nvcc-built librvt_scatter.so at runtime (no link-time CUDA).
cuda           = ["dep:libloading"]
# Metal scatter-add backend (Apple Silicon): MSL kernel compiled at runtime via metal-rs.
metal          = ["dep:metal", "dep:objc"]
polars         = ["dep:polars"]
arrow          = ["dep:arrow", "dep:arrow-array", "dep:pyo3-arrow"]
zero-copy      = ["arrow"]  # Alias for clarity
# HDF5 support (Unix-only). Opt-in because the hdf5-metno-sys build script
# requires a compatible system HDF5 install. On Windows the underlying deps
# aren't available, so this feature compiles to no-op stubs there.
hdf5           = ["dep:hdf5-metno", "dep:hdf5-metno-sys"]