forked from https://github.com/jmunroe/intake-erddap.

Intake-ERDDAP

Copyright 2022 Axiom Data Science

See LICENSE

Copyright 2022 James Munroe

For changes prior to 2022-10-19, all contributions are Copyright James Munroe, see PREV-LICENSE.

Check out our Read The Docs page for additional documentation

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.

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.

User 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

Developer Installation

Prerequisites

The following are prerequisites for a developer environment for this project:

• conda
• (optional but highly recommended) mamba. Hint: conda install -c conda-forge mamba

Note: if mamba isn't installed, replace all instances of mamba in the following instructions with conda.

1. Create the project environment with:

   mamba env update -f environment.yml
   

2. Install the development environment dependencies:

   mamba env update -f dev-environment.yml
   

3. Activate the new virtual environment:

   conda activate intake-erddap
   

4. Install the project to the virtual environment:

   pip install -e .
   

Note that you need to install with pip install . once to get the entry_points correct too.

Examples

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

import intake_erddap
catalog = intake_erddap.ERDDAPCatalogReader(
    server="https://erddap.sensors.ioos.us/erddap"
).read()

The catalog objects behave like a dictionary with the keys representing the dataset's unique identifier within ERDDAP, and the values being the TableDAPReader objects. To access a Reader object (for a single dataset, in this case for dataset_id "aoos_204"):

dataset = catalog["aoos_204"]

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

df = dataset.read()

Find other dataset_ids available with

list(catalog)

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

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

dataset_id = list(catalog)[0]
print(dataset_id)
df = catalog[dataset_id].read()

Using the standard_names input with intersection="union" searches for datasets that have both "wind_speed" and "wind_from_direction". Using the variables input subsequently narrows the dataset to only those columns, plus "time", "latitude", "longitude", and "z".

                 time (UTC)  latitude (degrees_north)  ...  wind_speed (m.s-1)  wind_from_direction (degrees)
0      2022-01-01T00:00:00Z                    28.508  ...                 3.6                          126.0
1      2022-01-01T00:10:00Z                    28.508  ...                 3.8                          126.0
2      2022-01-01T00:20:00Z                    28.508  ...                 3.6                          124.0
3      2022-01-01T00:30:00Z                    28.508  ...                 3.4                          125.0
4      2022-01-01T00:40:00Z                    28.508  ...                 3.5                          124.0
...                     ...                       ...  ...                 ...                            ...
52524  2022-12-31T23:20:00Z                    28.508  ...                 5.9                          176.0
52525  2022-12-31T23:30:00Z                    28.508  ...                 6.8                          177.0
52526  2022-12-31T23:40:00Z                    28.508  ...                 7.2                          175.0
52527  2022-12-31T23:50:00Z                    28.508  ...                 7.4                          169.0
52528  2023-01-01T00:00:00Z                    28.508  ...                 8.1                          171.0

[52529 rows x 6 columns]