Hyrax provides direct access to subsetting of NASA data using Python or your favorite analysis tool

  • Python
  • Jupyter Notebook
  • Matlab
  • R
  • Panoply
  • ArcGIS Pro
Quickstart to OPeNDAP

Python Java C/C++

Features

Multiple Data Formats

Supports data retrieval in multiple return formats including DAP, DAP4, NetCDF-3, NetCDF-4, JSON, CSV, and ASCII. You can also add custom response types.

RDF

Hyrax provides RDF descriptions of it's data holdings enabling semantic web tools to operate upon the metadata content held in the server

WebStart

Extensible WebStart functionality for Java-based DAP clients

JSON

Both metadata and data are available in a JSON encoding

Server Side Functions

Hyrax supports a number of Server side functions out of the box including (but not limited to):

  • geogrid: Subset applicable DAP Grids using latitude and longitude values.
  • grid: Subset any DAP Grid object using the values of its map vectors.
  • linear_scale: Apply a linear equation to the data returned, including automatic use of CF attributes.
  • version: The version function provides a list of the server-side processing functions available.
  • Easily add your own custom functions.

Interactive Data Access Tutorials

You can find a number of tutorials which explain how to utilize different data access clients to retrieve data from a DAP2 or DAP4 service on our Data Access Tutorials page.

Hyrax and Python/PyDAP Example

Python and PyDAP can be used to access PACE data within NASA's Earthdata in the cloud, as well as data from other sources. Alternatively, the Python netCDF package offers another robust option for accessing similar datasets.

Python packages such as PyDAP and netCDF can be used to access data within NASA's Earthdata in the cloud, such as PACE datasets.

Using Jupyter notebook, follow these steps:

  1. Authenticate using EDL tokens;
  2. Access data in a way that is parallelizable; and
  3. Plot a graph derived from your analysis.

PyDAP is a powerful, parallelizable, python-native tool designed to access data through the OPeNDAP data model. With its ability to handle chunked arrays, complex tabular structures, and groups, PyDAP integrates smoothly with the broad Python-ecosystem. Additionally, PyDAP can serve as an access layer for xarray, making it easier to parallelize and scale a science-driven analysis when working with complex, multidimensional data structures without the need to download and reformat entire copies of datasets.

Setup Earthdata Login Access one variable from the PACE dataset Plot the data using Python and matplotlib

Hyrax and Panoply Example

Panoply plots geo-referenced and other arrays from netCDF, HDF, GRIB, and other datasets. With Panoply 4 (and later) you can Explore remote OpenDAP catalogs and open datasets served from them. For this example we used Panoply version 5.5.0. Older versions may not support URLs that use the dap4:// protocol.

  1. Launch Panoply and click File > Open > Open Remote Dataset
  2. Type dap4://opendap.earthdata.nasa.gov/hyrax/data/nc/coads_climatology.nc into the remote URL dialog
  3. When the data loads, click SST to load the map visualization

The Panoply application is a sophisticated GUI driven data client that is based around the CDM/NetCDF data model and utilizes that NetCDF-Java (and thus the Java DAP implementation) to access remote DAP datasets. Because it has a GUI it can retrieve (and cache for later) users credentials directly from the user.

panoply-1 panoply-2