Here is the list of functionalities included in the library:
- Dataset Metadata - retrieves the metadata of a dataset
- Dataset Search - searches PO.DAAC’s dataset catalog, over Level 2, Level 3, and Level 4 datasets
- Dataset Variables - provides list of dataset variables for the dataset
- Granule Metadata - retrieves the metadata of a granule
- Granule Search - does granule searching on PO.DAAC level 2 swath datasets (individual orbits of a satellite), and level 3 & 4 gridded datasets (time averaged to span the globe)
- Granule Preview - the PODAAC preview Image service retrieves pre-generated preview images for selected granules
- Granule Subset - Subset Granule service allows users to submit subset jobs
- Subset Status - Subset Granule Status service allows users to check the status of submitted subset job
- Metadata Compliance Checker - an online tool and web service designed to check and validate the contents of netCDF and HDF granules for the Climate and Forecast (CF) and Attribute Convention for Dataset Discovery (ACDD) metadata conventions.
Here is the sample snippet script to use the podaacpy library, users are encouraged to explore all other functionalities in the podaacpy library.
- Code: Select all
#! /usr/bin/env python2
#
# podaac package
import podaac.podaac as podaac
# podaac_utils package
import podaac.podaac_utils as utils
# create an instance of the Podaac class
p = podaac.Podaac()
# create an instance of the PodaacUtils class
u = utils.PodaacUtils()
# Use Podaac class to search metadata for list of granules archived within the last 24 hours in Datacasting format.
# The return result contains an XML response containing a list of data granules which can be processed appropriately.
result = p.load_last24hours_datacasting_granule_md(dataset_id='PODAAC-GHMG2-2PO01')
print result
# Return meta data of dataset
result = p.dataset_metadata(dataset_id='PODAAC-GHMG2-2PO01')
print result
# Return meta data of granule
result = p.granule_metadata(dataset_id='PODAAC-GHMG2-2PO01', granule_name='20120912-MSG02-OSDPD-L2P-MSG02_0200Z-v01.nc')
print result
# Return datasets with keywaords
result = p.dataset_search(keyword='modis')
print result
# Return granules list within both spatial and temporal ranges
result = p.granule_search(dataset_id='PODAAC-ASOP2-25X01', bbox='0,0,180,90',start_time='2013-01-01T01:30:00Z',end_time='2014-01-01T00:00:00Z',start_index='1')
print result
The podaacpy can be downloaded here:https://github.com/lewismc/podaacpy, and the documentation is shown here: http://podaacpy.readthedocs.io/en/latest/introduction.html
