Earthdata catalog explorer¶

Explore the bundled NASA Earthdata catalog without any network or credentials — it ships as package data. The catalog spans nine DAACs reached through one Earthdata Login. See Usage for the download walkthrough.

Setup¶

The only import we need is the Catalog loader. Constructing it parses the package-data YAML into the curated datasets map plus the daacs provider registry — no network call.

In [ ]:

from collections import Counter

from earthlens.earthdata import Catalog

cat = Catalog()

from collections import Counter from earthlens.earthdata import Catalog cat = Catalog()

Catalog at a glance¶

The catalog carries three things: the curated datasets we model in detail, the daacs (CMR providers) they live under, and the much larger informational available_datasets index of every reachable id.

In [ ]:

print(f'curated datasets:        {len(cat.datasets)}')
print(f'CMR providers (DAACs):   {len(cat.daacs)}')
print(f'available_datasets index: {len(cat.available_datasets)}')

print(f'curated datasets: {len(cat.datasets)}') print(f'CMR providers (DAACs): {len(cat.daacs)}') print(f'available_datasets index: {len(cat.available_datasets)}')

Coverage by DAAC¶

Counting curated datasets per daac shows which archives are best represented — LP DAAC and GES DISC lead here.

In [ ]:

by_daac = Counter(ds.daac for ds in cat.datasets.values())
for daac, n in sorted(by_daac.items()):
    print(f'  {daac:12s} {n}')

by_daac = Counter(ds.daac for ds in cat.datasets.values()) for daac, n in sorted(by_daac.items()): print(f' {daac:12s} {n}')

The defining property: output kind is per dataset¶

Unlike every other earthlens backend, Earthdata datasets are not all the same shape — gridded raster, point/profile vector, or tabular. Grouping by output_kind shows the split across the curated set.

In [ ]:

by_kind = Counter(ds.output_kind for ds in cat.datasets.values())
for kind, n in by_kind.most_common():
    print(f'  {kind:8s} {n}')

by_kind = Counter(ds.output_kind for ds in cat.datasets.values()) for kind, n in by_kind.most_common(): print(f' {kind:8s} {n}')

Resolve a dataset and its DAAC¶

get_dataset() returns the modelled row for a concrete id; get_daac() then looks up the provider record (here to read its cloud_region for direct S3 access).

In [ ]:

ds = cat.get_dataset('GPM_3IMERGHHL_07')
print('short_name :', ds.short_name)
print('provider   :', ds.provider)
print('output_kind:', ds.output_kind)
print('cloud_region:', cat.get_daac(ds.provider).cloud_region)

ds = cat.get_dataset('GPM_3IMERGHHL_07') print('short_name :', ds.short_name) print('provider :', ds.provider) print('output_kind:', ds.output_kind) print('cloud_region:', cat.get_daac(ds.provider).cloud_region)

A vector example¶

ICESat-2 ATL08 land/vegetation heights resolve to output_kind == 'vector' — point/profile data rather than a gridded raster.

In [ ]:

print(cat.get_dataset('ATL08_006').output_kind)

print(cat.get_dataset('ATL08_006').output_kind)