vibeproj 0.1.6

GPU-accelerated coordinate projection library

vibeProj

GPU-accelerated coordinate projection library. Extracted from RAPIDS cuProj, re-engineered as a pure Python + CuPy package, and expanded from 1 to 20 projections — each with a fused NVRTC kernel that runs the full transform pipeline in a single GPU kernel launch.

[!WARNING] vibeProj is very early in development. Operations may be unoptimized or have multiple Host/Device transfers causing reduced performance. File an issue if you hit a problem!

Performance

On an RTX 4090 vs i9-13900k, 1M coordinates: (Note: datacenter GPUs will see far higher speedups due to better double precision performance)

Projection	GPU	vs CPU
Transverse Mercator / UTM	0.49 ms	183x
Lambert Conformal Conic	0.54 ms	135x
Albers Equal Area	0.27 ms	180x
Web Mercator	0.15 ms	364x
Equal Earth	0.43 ms	154x
Plate Carrée	0.04 ms	702x

All 20 projections run sub-millisecond at 1M coordinates. See full benchmark in the repo.

Supported Projections

Projection	Internal Name	EPSG Examples
Transverse Mercator / UTM	tmerc	32601–32760, 27700
Web Mercator	webmerc	3857
Mercator (ellipsoidal)	merc	3395
Lambert Conformal Conic	lcc	2154
Albers Equal Area	aea	5070
Polar Stereographic	stere	3031, 3413
Lambert Azimuthal Equal Area	laea	3035
Oblique Stereographic	sterea	28992
Plate Carrée	eqc	4087
Sinusoidal	sinu	—
Equal Earth	eqearth	8857
Cylindrical Equal Area	cea	6933
Orthographic	ortho	—
Gnomonic	gnom	—
Mollweide	moll	—
Robinson	robin	—
Winkel Tripel	wintri	—
Natural Earth	natearth	—
Azimuthal Equidistant	aeqd	—
Geostationary Satellite	geos	—

Install

pip install vibeproj            # CPU-only (NumPy fallback)
pip install vibeproj[cu12]      # CUDA 12
pip install vibeproj[cu13]      # CUDA 13

For development:

uv sync                         # CPU-only
uv sync --extra cu12            # CUDA 12
uv sync --extra cu13            # CUDA 13

Usage

from vibeproj import Transformer

# Default: always_xy=True — (lon, lat) order, matches shapely/geopandas
t = Transformer.from_crs("EPSG:4326", "EPSG:32631")
x, y = t.transform(2.0, 49.0)           # (lon, lat) in, (easting, northing) out

# always_xy=False: native CRS axis order (matches pyproj default)
t = Transformer.from_crs("EPSG:4326", "EPSG:32631", always_xy=False)
x, y = t.transform(49.0, 2.0)           # (lat, lon) in, (easting, northing) out

Cross-datum transforms (Helmert)

# Cross-datum: Helmert 7/15-parameter shift applied automatically
t = Transformer.from_crs("EPSG:4326", "EPSG:27700")  # WGS84 → OSGB36
x, y = t.transform(-0.1278, 51.5074)

# With ellipsoidal height — z is transformed through the ECEF intermediate
x, y, z = t.transform(-0.1278, 51.5074, z=45.0)

# Same-datum: z passes through unchanged, zero overhead
t = Transformer.from_crs("EPSG:4326", "EPSG:32631")
x, y, z = t.transform(2.0, 49.0, z=45.0)  # z == 45.0

vibeSpatial Integration (zero-copy GPU)

# Pre-allocated output, no intermediate allocations, stays on GPU
t = Transformer.from_crs(src_crs, dst_crs, always_xy=True)
new_x = cp.empty_like(buf.x)
new_y = cp.empty_like(buf.y)
t.transform_buffers(buf.x, buf.y, out_x=new_x, out_y=new_y)

# 3D: z is transformed through Helmert when crossing datums
new_z = cp.empty_like(buf.z)
t.transform_buffers(buf.x, buf.y, buf.z, out_x=new_x, out_y=new_y, out_z=new_z)

transform_buffers() accepts pre-allocated CuPy output arrays, writes results directly into them, and returns the same objects. No host round-trip, no intermediate allocation. Designed for vibeSpatial's OwnedGeometryArray coordinate buffers.

Architecture

• Pure Python + CuPy — no compiled extensions, no CMake
• Fused NVRTC kernels — each projection's full pipeline (axis swap, deg/rad, central meridian, projection math, scale/offset) runs in a single CUDA kernel launch via CuPy RawKernel
• NumPy fallback — all projections work on CPU when CuPy is unavailable
• Helmert datum shifts — 7/15-parameter (time-dependent) datum transformation with 3D ellipsoidal height support, runs on its own GPU kernel
• pyproj for CRS metadata — EPSG codes resolved via pyproj, transform math is ours
• fp64 I/O — input/output arrays always double precision (ADR-0002 compliant)
• Auto GPU detection — queries SingleToDoublePrecisionPerfRatio to classify consumer vs datacenter GPU

Test

uv run pytest                    # all tests (198 total)
uv run pytest tests/test_fused_kernels.py  # GPU kernel tests (requires CuPy)