Quickstart

This page walks through the full workflow: list → inspect → subset → load/download → manage local data.

1. List available layers

import pyo_oracle as pyo

# All layers as a DataFrame
layers = pyo.list_layers()

# Free-text search
pyo.list_layers(search="Temperature")

# Filter by variable, scenario, time period, depth
pyo.list_layers(variables=["thetao"], time_period="present", depth="surf")

# Just the dataset IDs
ids = pyo.list_layers(variables="thetao", dataframe=False)

2. Inspect a layer

pyo.info_layer("thetao_baseline_2000_2019_depthsurf")

This prints the dimension ranges (time, latitude, longitude, and depth when present) and the available variables with their units. It also returns a dictionary you can use programmatically.

3. Build constraints

Instead of hand-writing the griddap constraint dictionary, use build_constraints:

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),
    latitude_step=2,   # take every 2nd grid cell along latitude
)

When the dataset_id is supplied, requested bounds are checked against the dataset's real ranges and a warning is emitted if they fall outside.

4. Load into memory or download to disk

# In-memory DataFrame
df = pyo.load_layer(
    "thetao_baseline_2000_2019_depthsurf",
    constraints=constraints,
    variables=["thetao_mean"],
)

# In-memory xarray.Dataset (requires the `xarray` extra)
ds = pyo.load_layer(
    "thetao_baseline_2000_2019_depthsurf",
    constraints=constraints,
    fmt="xarray",
)

# Download to a NetCDF file
pyo.download_layers(
    "thetao_baseline_2000_2019_depthsurf",
    constraints=constraints,
    variables=["thetao_mean"],
)

5. Manage local data

pyo.list_local_data()

Configuration

pyo_oracle stores a small config (data directory, ERDDAP server URL, confirmation prompts):

pyo.print_config_values()
pyo.update_setting("skip_confirmation", "True")