Solar & Wind Atlas catalog explorer (offline)¶

Inspect the bundled layer catalog without touching the network: which layers ship, which atlas each comes from, and the transport used to fetch it. This is the no-network companion to the live wind quickstart.

In [1]:

import pandas as pd

from earthlens.solar_wind_atlas import Catalog

import pandas as pd from earthlens.solar_wind_atlas import Catalog

The shipped layers¶

Catalog() loads the bundled sharded catalog/ (solar.yaml + wind.yaml). Each row is a frozen pydantic Layer carrying its atlas, transport, URL, and units.

In [2]:

catalog = Catalog()
rows = [
    {
        "id": lid,
        "atlas": catalog.get(lid).atlas,
        "transport": catalog.get(lid).transport,
        "units": catalog.get(lid).units,
        "long_name": catalog.get(lid).long_name,
    }
    for lid in catalog.available()
]
pd.DataFrame(rows)

catalog = Catalog() rows = [ { "id": lid, "atlas": catalog.get(lid).atlas, "transport": catalog.get(lid).transport, "units": catalog.get(lid).units, "long_name": catalog.get(lid).long_name, } for lid in catalog.available() ] pd.DataFrame(rows)

Out[2]:

	id	atlas	transport	units	long_name
0	air_density_100m	gwa	vsicurl	kg/m3	Mean air density at 100 m above ground level
1	capacity_factor_iec1	gwa	vsicurl	fraction	Wind turbine capacity factor, IEC Class 1
2	capacity_factor_iec2	gwa	vsicurl	fraction	Wind turbine capacity factor, IEC Class 2
3	capacity_factor_iec3	gwa	vsicurl	fraction	Wind turbine capacity factor, IEC Class 3
4	dif	gsa	download_zip	kWh/m2/day	Diffuse horizontal irradiation (long-term aver...
5	dni	gsa	download_zip	kWh/m2/day	Direct normal irradiation (long-term average d...
6	ghi	gsa	download_zip	kWh/m2/day	Global horizontal irradiation (long-term avera...
7	gti	gsa	download_zip	kWh/m2/day	Global irradiation for optimally tilted surfac...
8	opta	gsa	download_zip	degree	Optimum tilt of PV modules
9	pvout	gsa	download_zip	kWh/kWp/day	Photovoltaic power potential — specific PV out...
10	weibull_k_100m	gwa	vsicurl	dimensionless	Weibull shape parameter k at 100 m above groun...
11	weibull_k_10m	gwa	vsicurl	dimensionless	Weibull shape parameter k at 10 m above ground...
12	weibull_k_150m	gwa	vsicurl	dimensionless	Weibull shape parameter k at 150 m above groun...
13	weibull_k_200m	gwa	vsicurl	dimensionless	Weibull shape parameter k at 200 m above groun...
14	weibull_k_50m	gwa	vsicurl	dimensionless	Weibull shape parameter k at 50 m above ground...
15	wind_100m	gwa	vsicurl	m/s	Mean wind speed at 100 m above ground level

Two atlases, two transports¶

• Global Wind Atlas layers (atlas='gwa') use the vsicurl transport: a windowed read straight from the remote Cloud-Optimized GeoTIFF — only the AOI transfers.
• Global Solar Atlas layers (atlas='gsa') use download_zip: the deflate ZIP archive (no random access) is downloaded once into a cache, then cropped locally.

In [3]:

by_transport = {}
for lid in catalog.available():
    by_transport.setdefault(catalog.get(lid).transport, []).append(lid)
by_transport

by_transport = {} for lid in catalog.available(): by_transport.setdefault(catalog.get(lid).transport, []).append(lid) by_transport

Out[3]:

{'vsicurl': ['air_density_100m',
  'capacity_factor_iec1',
  'capacity_factor_iec2',
  'capacity_factor_iec3',
  'weibull_k_100m',
  'weibull_k_10m',
  'weibull_k_150m',
  'weibull_k_200m',
  'weibull_k_50m',
  'wind_100m'],
 'download_zip': ['dif', 'dni', 'ghi', 'gti', 'opta', 'pvout']}

Did-you-mean on an unknown id¶

An unknown layer id raises a ValueError with the closest match.

In [4]:

raises-exception

catalog.get("gho")  # unknown id -> ValueError with a did-you-mean hint

catalog.get("gho") # unknown id -> ValueError with a did-you-mean hint

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[4], line 1
----> 1 catalog.get("gho")  # unknown id -> ValueError with a did-you-mean hint

File C:\gdrive\algorithms\remote-sensing\earthlens\.claude\worktrees\solar-wind-atlas\src\earthlens\solar_wind_atlas\catalog.py:277, in Catalog.get(self, layer_id)
    253 def get(self, layer_id: str) -> Layer:
    254     """Return the `Layer` for a curated id, did-you-mean on miss.
    255 
    256     Args:
   (...)    275             ```
    276     """
--> 277     return self.get_dataset(layer_id)

File C:\gdrive\algorithms\remote-sensing\earthlens\.claude\worktrees\solar-wind-atlas\src\earthlens\base\abstractdatasource.py:1072, in AbstractCatalog.get_dataset(self, name)
   1070 close = difflib.get_close_matches(name, self.datasets, n=1)
   1071 hint = f" Did you mean {close[0]!r}?" if close else ""
-> 1072 raise ValueError(
   1073     f"{name!r} is not in the {self._catalog_kind}. "
   1074     f"Known {self._entry_noun}: {sorted(self.datasets)}.{hint}"
   1075 ) from None

ValueError: 'gho' is not in the Solar & Wind Atlas catalog. Known layers: ['air_density_100m', 'capacity_factor_iec1', 'capacity_factor_iec2', 'capacity_factor_iec3', 'dif', 'dni', 'ghi', 'gti', 'opta', 'pvout', 'weibull_k_100m', 'weibull_k_10m', 'weibull_k_150m', 'weibull_k_200m', 'weibull_k_50m', 'wind_100m']. Did you mean 'ghi'?