Welcome to intake-erddap’s documentation!

Intake ERDDAP

Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake ERDDAP provides a set of integrations for ERDDAP.

  • Quickly identify all datasets from an ERDDAP service in a geographic region, or containing certain variables.

  • Produce a pandas DataFrame for a given dataset or query.

  • Get an xarray Dataset for the Gridded datasets.

Build Status Code Coverage License:BSD Code Style Status

The project is available on Github.

TODO: Summary

The Key features are:

  • Pandas DataFrames for any TableDAP dataset.

  • xarray Datasets for any GridDAP datasets.

  • Query by any or all:

    • bounding box

    • time

    • CF standard_name

    • variable name

    • Plaintext Search term

  • Save catalogs locally for future use.

Requirements

  • Python >= 3.8

Installation

In the very near future, we will be offering the project on conda. Currently the project is available on PyPI, so it can be installed using pip:

pip install intake-erddap

Examples

To create an intake catalog for all of the ERDDAP’s TableDAP offerings use:

import intake
catalog = intake.open_erddap_cat(
    server="https://erddap.sensors.ioos.us/erddap"
)

The catalog objects behave like a dictionary with the keys representing the dataset’s unique identifier within ERDDAP, and the values being the TableDAPSource objects. To access a source object:

source = catalog["datasetid"]

From the source object, a pandas DataFrame can be retrieved:

df = source.read()

Scenarios

Consider a case where you need to find all wind data near Florida.:

import intake
from datetime import datetime
bbox = (-87.84, 24.05, -77.11, 31.27)
catalog = intake.open_erddap_cat(
    server="https://erddap.sensors.ioos.us/erddap",
    bbox=bbox,
    start_time=datetime(2022, 1, 1),
    end_time=datetime(2023, 1, 1),
    standard_names=["wind_speed", "wind_from_direction"],
)

df = next(catalog.values()).read()
time (UTC) wind_speed (m.s-1) wind_from_direction (degrees)
0 2022-12-14T19:40:00Z 7.0 140.0
1 2022-12-14T19:20:00Z 7.0 120.0
2 2022-12-14T19:10:00Z NaN NaN
3 2022-12-14T19:00:00Z 9.0 130.0
4 2022-12-14T18:50:00Z 9.0 130.0
... ... ... ...
48296 2022-01-01T00:40:00Z 4.0 120.0
48297 2022-01-01T00:30:00Z 3.0 130.0
48298 2022-01-01T00:20:00Z 4.0 120.0
48299 2022-01-01T00:10:00Z 4.0 130.0
48300 2022-01-01T00:00:00Z 4.0 130.0

Indices and tables