pyo_oracle

Python client for the Bio-ORACLE ERDDAP server.

Discover, inspect, subset, download, and load Bio-ORACLE marine environmental layers (temperature, salinity, nutrients, sea ice, and more) from Python. pyo_oracle is the Python counterpart of the R package biooracler and is built on erddapy.

📖 Documentation: https://bio-oracle.github.io/pyo_oracle/

Installation

# With pip
pip install pyo-oracle

# Load layers as xarray (optional extra)
pip install "pyo-oracle[xarray]"

# With conda
conda create -n pyo_oracle conda-forge::pyo-oracle

Quick start

import pyo_oracle as pyo

# 1. List available layers (filter by search term, variable, scenario, depth, ...)
pyo.list_layers(search="Temperature")

# 2. Inspect a layer: dimension ranges + variables and units
pyo.info_layer("thetao_baseline_2000_2019_depthsurf")

# 3. Build constraints from friendly bounds (no hand-written dicts)
constraints = pyo.build_constraints(
    "thetao_baseline_2000_2019_depthsurf",
    time=("2000-01-01T00:00:00Z", "2010-01-01T00:00:00Z"),
    latitude=(0, 10),
    longitude=(0, 10),
)

# 4a. Load directly into memory (pandas or xarray)
df = pyo.load_layer(
    "thetao_baseline_2000_2019_depthsurf",
    constraints=constraints,
    variables=["thetao_mean"],
)
ds = pyo.load_layer(
    "thetao_baseline_2000_2019_depthsurf",
    constraints=constraints,
    fmt="xarray",
)

# 4b. Or download to a file (NetCDF by default)
pyo.download_layers(
    "thetao_baseline_2000_2019_depthsurf",
    constraints=constraints,
    variables=["thetao_mean"],
)

# 5. See local data
pyo.list_local_data()

Key functions

Contributing

See CLAUDE.md for the dev setup, testing, and release flow. Please open an issue if you experience any problems.