1import zarr
2import numpy as np
3                                                                                                                            
4root = zarr.open_group(                                                                                                       
5  "s3://us-west-2.opendata.source.coop/tge-labs/aef-mosaic/",                                                               
6  mode="r",                                                                                                                 
7  storage_options={"anon": True}                                                                                            
8)                                                                                                                             
9embeddings = root["embeddings"]
10
11# Access a spatial subset (year 0, all bands, y slice, x slice)
12data = embeddings[0, :, 516000:516100, 832000:832100]
13
14# De-quantize to float32
15data_float = ((data / 127.5) ** 2) * np.sign(data)

1import zarr
2import numpy as np
3                                                                                                                            
4root = zarr.open_group(                                                                                                       
5  "s3://us-west-2.opendata.source.coop/tge-labs/aef-mosaic/",                                                               
6  mode="r",                                                                                                                 
7  storage_options={"anon": True}                                                                                            
8)                                                                                                                             
9embeddings = root["embeddings"]
10
11# Access a spatial subset (year 0, all bands, y slice, x slice)
12data = embeddings[0, :, 516000:516100, 832000:832100]
13
14# De-quantize to float32
15data_float = ((data / 127.5) ** 2) * np.sign(data)

1import xarray as xr
2
3
4def fetch_point(ds, lat, lon, time=0):
5    """Fetch AEF embedding for a single lat/lon point. Returns shape (64,)."""
6    return ds.isel(time=time).sel(x=lon, y=lat, method="nearest")
7
8
9def fetch_bbox(ds, bbox, time=0):
10    """Fetch AEF embeddings for a bounding box. Returns shape (64, y, x)."""
11    minx, miny, maxx, maxy = bbox
12    return ds.isel(time=time).sel(x=slice(minx, maxx), y=slice(maxy, miny))
13
14
15def main():
16    ds = xr.open_zarr(
17        "s3://us-west-2.opendata.source.coop/tge-labs/aef-mosaic/",
18        storage_options={"anon": True}
19    )
20
21    # Fetch embedding for a single point
22    lat, lon = 34.0522, -118.2437  # Los Angeles
23    point_data = fetch_point(ds, lat, lon).compute()
24    print(f"Point shape: {point_data.embeddings.shape}")
25
26    # Fetch embeddings for a bounding box
27    bbox = [-118.279668, 34.036242, -118.20862, 34.089643]
28    bbox_data = fetch_bbox(ds, bbox).compute()
29    print(f"Bbox shape: {bbox_data.embeddings.shape}")
30
31
32if __name__ == "__main__":
33    main()

1import xarray as xr
2
3
4def fetch_point(ds, lat, lon, time=0):
5    """Fetch AEF embedding for a single lat/lon point. Returns shape (64,)."""
6    return ds.isel(time=time).sel(x=lon, y=lat, method="nearest")
7
8
9def fetch_bbox(ds, bbox, time=0):
10    """Fetch AEF embeddings for a bounding box. Returns shape (64, y, x)."""
11    minx, miny, maxx, maxy = bbox
12    return ds.isel(time=time).sel(x=slice(minx, maxx), y=slice(maxy, miny))
13
14
15def main():
16    ds = xr.open_zarr(
17        "s3://us-west-2.opendata.source.coop/tge-labs/aef-mosaic/",
18        storage_options={"anon": True}
19    )
20
21    # Fetch embedding for a single point
22    lat, lon = 34.0522, -118.2437  # Los Angeles
23    point_data = fetch_point(ds, lat, lon).compute()
24    print(f"Point shape: {point_data.embeddings.shape}")
25
26    # Fetch embeddings for a bounding box
27    bbox = [-118.279668, 34.036242, -118.20862, 34.089643]
28    bbox_data = fetch_bbox(ds, bbox).compute()
29    print(f"Bbox shape: {bbox_data.embeddings.shape}")
30
31
32if __name__ == "__main__":
33    main()

1import asyncio
2from affine import Affine
3from obstore.store import S3Store
4from zarr.storage import ObjectStore
5from zarr.api.asynchronous import open_group
6
7
8store = S3Store(
9    "us-west-2.opendata.source.coop",
10    prefix="tge-labs/aef-mosaic/",
11    region="us-west-2",
12    skip_signature=True,
13)
14zarr_store = ObjectStore(store, read_only=True)
15
16
17def latlon_to_rowcol(lat, lon, transform):
18    """Convert lat/lon to row/col using the inverse transform."""
19    inv = ~transform
20    col, row = inv * (lon, lat)
21    return int(row), int(col)
22
23
24def bbox_to_slices(bbox, transform):
25    """Convert a bounding box to row/col slices."""
26    minx, miny, maxx, maxy = bbox
27    inv = ~transform
28    col1, row1 = inv * (minx, maxy)
29    col2, row2 = inv * (maxx, miny)
30    row_start, row_stop = int(min(row1, row2)), int(max(row1, row2)) + 1
31    col_start, col_stop = int(min(col1, col2)), int(max(col1, col2)) + 1
32    return slice(row_start, row_stop), slice(col_start, col_stop)
33
34
35async def fetch_point(embeddings, transform, lat, lon, year=0):
36    """Fetch AEF embedding for a single lat/lon point. Returns shape (64,)."""
37    row, col = latlon_to_rowcol(lat, lon, transform)
38    return await embeddings.getitem((year, slice(None), row, col))
39
40
41async def fetch_bbox(embeddings, transform, bbox, year=0):
42    """Fetch AEF embeddings for a bounding box. Returns shape (64, rows, cols)."""
43    row_slice, col_slice = bbox_to_slices(bbox, transform)
44    return await embeddings.getitem((year, slice(None), row_slice, col_slice))
45
46
47async def main():
48    root = await open_group(store=zarr_store, mode="r")
49    embeddings = await root.get("embeddings")
50    transform = Affine(*root.metadata.attributes["spatial:transform"])
51
52    # Fetch embedding for a single point
53    lat, lon = 34.0522, -118.2437  # Los Angeles
54    point_data = await fetch_point(embeddings, transform, lat, lon)
55    print(f"Point shape: {point_data.shape}")
56
57    # Fetch embeddings for a bounding box
58    bbox = [-118.279668, 34.036242, -118.20862, 34.089643]
59    bbox_data = await fetch_bbox(embeddings, transform, bbox)
60    print(f"Bbox shape: {bbox_data.shape}")
61
62
63if __name__ == "__main__":
64    asyncio.run(main())

1import asyncio
2from affine import Affine
3from obstore.store import S3Store
4from zarr.storage import ObjectStore
5from zarr.api.asynchronous import open_group
6
7
8store = S3Store(
9    "us-west-2.opendata.source.coop",
10    prefix="tge-labs/aef-mosaic/",
11    region="us-west-2",
12    skip_signature=True,
13)
14zarr_store = ObjectStore(store, read_only=True)
15
16
17def latlon_to_rowcol(lat, lon, transform):
18    """Convert lat/lon to row/col using the inverse transform."""
19    inv = ~transform
20    col, row = inv * (lon, lat)
21    return int(row), int(col)
22
23
24def bbox_to_slices(bbox, transform):
25    """Convert a bounding box to row/col slices."""
26    minx, miny, maxx, maxy = bbox
27    inv = ~transform
28    col1, row1 = inv * (minx, maxy)
29    col2, row2 = inv * (maxx, miny)
30    row_start, row_stop = int(min(row1, row2)), int(max(row1, row2)) + 1
31    col_start, col_stop = int(min(col1, col2)), int(max(col1, col2)) + 1
32    return slice(row_start, row_stop), slice(col_start, col_stop)
33
34
35async def fetch_point(embeddings, transform, lat, lon, year=0):
36    """Fetch AEF embedding for a single lat/lon point. Returns shape (64,)."""
37    row, col = latlon_to_rowcol(lat, lon, transform)
38    return await embeddings.getitem((year, slice(None), row, col))
39
40
41async def fetch_bbox(embeddings, transform, bbox, year=0):
42    """Fetch AEF embeddings for a bounding box. Returns shape (64, rows, cols)."""
43    row_slice, col_slice = bbox_to_slices(bbox, transform)
44    return await embeddings.getitem((year, slice(None), row_slice, col_slice))
45
46
47async def main():
48    root = await open_group(store=zarr_store, mode="r")
49    embeddings = await root.get("embeddings")
50    transform = Affine(*root.metadata.attributes["spatial:transform"])
51
52    # Fetch embedding for a single point
53    lat, lon = 34.0522, -118.2437  # Los Angeles
54    point_data = await fetch_point(embeddings, transform, lat, lon)
55    print(f"Point shape: {point_data.shape}")
56
57    # Fetch embeddings for a bounding box
58    bbox = [-118.279668, 34.036242, -118.20862, 34.089643]
59    bbox_data = await fetch_bbox(embeddings, transform, bbox)
60    print(f"Bbox shape: {bbox_data.shape}")
61
62
63if __name__ == "__main__":
64    asyncio.run(main())