pixelquery 0.1.8

Turn your COG files into an analysis-ready time-series data cube

PixelQuery

Turn your COG files into an analysis-ready time-series data cube. No infrastructure required.

What is PixelQuery?

PixelQuery converts a directory of Cloud-Optimized GeoTIFFs (COGs) into a queryable time-series data cube backed by Icechunk virtual Zarr storage.

• Zero data copy: Virtual references to original COGs (no duplication)
• Fast ingestion: ~9ms per COG (243 files in 2 seconds)
• Lazy loading: Data reads from COGs only when you call .compute()
• pip install: No STAC server, no database, no infrastructure
• Multi-satellite: Built-in product profiles for Planet, Sentinel-2, Landsat

Quick Start

pip install pixelquery[icechunk]

import pixelquery as pq

# Ingest COGs from a directory
result = pq.ingest("./my_cogs/", band_names=["blue", "green", "red", "nir"])
print(f"Ingested {result.scene_count} scenes in {result.elapsed:.1f}s")

# Query as lazy xarray Dataset
ds = pq.open_xarray("./warehouse")
print(ds)  # Dimensions: (time: 243, band: 4, y: 874, x: 3519)

5-Minute Tutorial

1. Inspect your COG files

import pixelquery as pq

# Check what you have
meta = pq.inspect_cog("./scene.tif")
print(meta)  # CRS, bounds, bands, resolution

2. Ingest

result = pq.ingest(
    "./planet_cogs/",
    warehouse="./warehouse",
    band_names=["blue", "green", "red", "nir"],
    product_id="planet_sr",
)

3. Query

ds = pq.open_xarray("./warehouse")

# Filter by time range
from datetime import datetime
ds = pq.open_xarray(
    "./warehouse",
    time_range=(datetime(2024, 1, 1), datetime(2024, 12, 31)),
    bands=["red", "nir"],
)

4. Compute NDVI

nir = ds["data"].sel(band="nir")
red = ds["data"].sel(band="red")
ndvi = (nir - red) / (nir + red)
ndvi.mean(dim="time").compute()  # Actual COG reads happen here

5. Point time-series

ts = pq.timeseries("./warehouse", lon=127.05, lat=37.55)
ts["data"].sel(band="nir").plot()  # Plot NIR time-series

Product Profiles

Register satellite product definitions for multi-product warehouses:

pq.register_product(
    "sentinel2_l2a",
    bands={"blue": 1, "green": 2, "red": 3, "nir": 7},
    resolution=10.0,
    provider="ESA",
)

# Browse warehouse contents
cat = pq.catalog("./warehouse")
print(cat.summary())
# === PixelQuery Warehouse Summary ===
# Products: 2
#
# planet_sr (Planet)
#   Scenes: 243
#   Bands: blue, green, red, nir
#   Resolution: 3.0m

How It Works

COG files (on disk/S3)
    |
    v
VirtualTIFF parser (reads byte offsets, ~3ms/file)
    |
    v
Icechunk repository (stores virtual chunk references)
    |
    v
xarray.open_zarr() (lazy loading)
    |
    v
.compute() → reads actual pixel data from original COGs

No data is copied during ingestion. Icechunk stores only the byte-range references to the original COG files. Actual pixel data is read on-demand when you call .compute() or .values.

Performance

Operation	Result
Single COG ingest	~3ms (virtual reference)
243 COG batch	2.1s (8.6ms/COG)
Storage overhead	0.2MB for 4.4GB data
Metadata query	59ms
Compute 6 scenes	255ms

Time Travel

Icechunk provides built-in versioning. Every ingest creates a snapshot.

# View history
history = pq.open_xarray("./warehouse", snapshot_id=None)

# Query at a specific point in time
cat = pq.catalog("./warehouse")
snapshots = cat.get_snapshot_history()
old_ds = pq.open_xarray("./warehouse", snapshot_id=snapshots[-1]["snapshot_id"])

API Reference

Core Functions

Function	Description
pq.ingest(source, warehouse, ...)	Auto-scan and ingest COGs
pq.open_xarray(warehouse, ...)	Query as lazy xarray Dataset
pq.timeseries(warehouse, lon, lat, ...)	Extract point time-series

Inspection

Function	Description
pq.inspect_cog(path)	Read COG metadata (CRS, bounds, bands)
pq.inspect_directory(dir)	Scan directory for COGs

Catalog

Function	Description
pq.catalog(warehouse)	Get catalog for warehouse
pq.register_product(...)	Register a product profile
catalog.summary()	Formatted warehouse summary
catalog.products()	List product IDs
catalog.scenes(...)	List scenes with filters

Installation

From PyPI

pip install pixelquery[icechunk]

From Source

git clone https://github.com/yourusername/pixelquery.git
cd pixelquery
pip install -e ".[icechunk,dev]"

When to Use PixelQuery

Scenario	Best Tool
Private COGs -> time-series analysis	PixelQuery
Public satellite data catalog	STAC + stackstac
Enterprise cloud data platform	Arraylake
Planetary-scale analysis	Google Earth Engine

PixelQuery is designed for researchers and developers who have their own COG files and want to query them as a time-series data cube without setting up any infrastructure.

Contributing

Contributions are welcome! Please open an issue or PR.

License

Apache 2.0