Listing and filtering layers

Bio-ORACLE exposes hundreds of layers. list_layers helps you find the ones you need.

All layers

import pyo_oracle as pyo

df = pyo.list_layers()
df.head()

By default a pandas.DataFrame is returned. Pass dataframe=False to get a plain list of dataset IDs instead.

Free-text search

pyo.list_layers(search="Oxygen")
pyo.list_layers(search=["Temperature", "Salinity"])  # OR across terms

The search matches against the dataset ID, title, long name and standard name.

Structured filters

You can combine any of the following filters:

Argument	Valid values
variables	chl, clt, dfe, mlotst, no3, o2, ph, phyc, po4, si, siconc, sithick, so, swd, sws, tas, terrain, thetao
ssp	ssp119, ssp126, ssp245, ssp370, ssp460, ssp585, baseline
time_period	present, future
depth	min, mean, max, surf

# Future projections of phosphate under two SSP scenarios, as IDs
pyo.list_layers(
    variables="po4",
    ssp=["ssp119", "ssp126"],
    time_period="future",
    dataframe=False,
)

Simplify the output

pyo.list_layers(variables="thetao", simplify=True)  # only datasetID + title

Tip

Results are cached, so repeated calls with the same filters (in any order) are instant and return the same object.