from earthdata import Auth, DataGranules, DataCollections, Store

auth = Auth().login() # if we want to access NASA DATA in the cloud

# To search for collecrtions (datasets)

DatasetQuery = DataCollections().keyword('MODIS').bounding_box(-26.85,62.65,-11.86,67.08)

counts = DatasetQuery.hits()
collections = DatasetQuery.get()


# To search for granules (data files)
GranuleQuery = DataGranules().concept_id('C1711961296-LPCLOUD').bounding_box(-10,20,10,50)

# number of granules (data files) that matched our criteria
counts = GranuleQuery.hits()
# We get the metadata
granules = GranuleQuery.get(10)

# earthdata provides some convenience functions for each data granule
data_links = [granule.data_links() for granule in granules]

# The Store class allows to get the granules from on-prem locations with get()
# NOTE: Some datasets require users to accept a Licence Agreement before accessing them
store = Store(auth)

# This works with both, on-prem or cloud based collections**
store.get(granules, local_path='./data')

# if you're in a AWS instance (us-west-2) you can use open() to get a fileset!
fileset = store.open(granules)

# Given that this is gridded data we could
xarray.open_mfdataset(fileset, combine='by_coords')