Tools#
Dataset compliance#
Verify given dataset conforms to the AVL dataset convention.
CLI:
Usage: avl ver [OPTIONS] DATASET
Verify given dataset conforms to the AVL dataset convention.
Options:
-l, --level [ERROR|WARNING] Level of messages to include.
--help Show this message and exit.
Python API:
import xarray as xr
import s3fs
from avl.verify import verify_dataset
s3 = s3fs.S3FileSystem(anon=True)
store = s3fs.S3Map('agriculture-vlab-data-staging/avl/l3b/2020/bel/S2_L3B_LAI_31UFS.zarr', s3=s3)
dataset = xr.open_zarr(store)
issues = verify_dataset(dataset)
Synthetic example datasets#
Generate AVL sample dataset into current working directory.
CLI:
Usage: avl new [OPTIONS]
Generate AVL sample dataset into current working directory.
Options:
--help Show this message and exit.
Generate simple catalogue#
Generate the markdown page of all available AVL datasets in the AWS S3 buckets.
CLI:
Usage: avl cat [OPTIONS]
Generate the markdown page of all available AVL datasets in the AWS S3
buckets.
Options:
-f, --file JSON_FILE JSON file path
--json Write the JSON_FILE and exit. Ignored if JSON_FILE is
not given.
--help Show this message and exit.
Generate full catalogue#
Generate a tree of markdown files with details of all specified stores and datasets.
Usage: avl catalogue [OPTIONS]
Options:
--max-datasets N maximum number of datasets to catalogue per data
store
--use-stock-map use very low-res stock map tiles instead of web tiles
--stores TEXT comma-separated IDs of stores to catalogue (if
omitted, catalogue all stores)
--suffixes TEXT comma-separated list of data ID suffixes to include
for s3 and file stores (if omitted, include all
suffixes)
--data-id-filter TEXT only include datasets whose ID contains this string
--help Show this message and exit.