Dataset Types

cfdb supports multiple coordinate structures through the dataset_type parameter.

Grid

The default type (dataset_type='grid') follows the standard CF conventions for N-dimensional data:

• Each coordinate is 1-D and represents a single axis (x, y, z, or t)
• Coordinate values must be unique and in ascending order
• The z axis is optional

This is the standard structure for gridded data like climate model output, satellite imagery, or any regular multi-dimensional array.

import cfdb
import numpy as np

with cfdb.open_dataset('grid.cfdb', flag='n', dataset_type='grid') as ds:
    lat = ds.create.coord.lat(data=np.linspace(-90, 90, 181, dtype='float32'))
    lon = ds.create.coord.lon(data=np.linspace(-180, 180, 361, dtype='float32'))
    time = ds.create.coord.time(
        data=np.arange('2020-01-01', '2020-01-31', dtype='datetime64[D]')
    )

    temp = ds.create.data_var.generic(
        'temperature', ('latitude', 'longitude', 'time'), dtype='float32'
    )

Time Series Orthogonal (ts_ortho)

The ts_ortho type implements the Orthogonal multidimensional array representation of time series from the CF conventions.

This is designed for time series data at sparse point locations (e.g., weather stations, monitoring sites):

• A geometry coordinate (Point dtype) represents the xy spatial axis
• A time coordinate is the same as the grid time coordinate
• The z axis is optional

import cfdb
import numpy as np
import shapely

with cfdb.open_dataset('stations.cfdb', flag='n', dataset_type='ts_ortho') as ds:
    # Station locations as Point geometries
    points = shapely.points([175.0, 172.5, 174.8], [-41.0, -43.5, -36.9])
    stations = ds.create.coord.generic(
        'station', data=points, dtype='point', chunk_shape=(3,), axis='xy'
    )

    # Time coordinate
    time = ds.create.coord.time(
        data=np.arange('2020-01-01', '2021-01-01', dtype='datetime64[D]')
    )

    # Data variable indexed by station and time
    temp = ds.create.data_var.generic(
        'temperature', ('station', 'time'), dtype='float32'
    )

    # Set CRS
    ds.create.crs.from_user_input(4326, xy_coord='station')

When to Use Each Type

Use Case	Type
Gridded climate/weather data	grid
Satellite imagery	grid
Regular multi-dimensional arrays	grid
Weather station time series	ts_ortho
Monitoring site observations	ts_ortho
Any sparse-point time series	ts_ortho