Ejemplo n.º 1
0
def init_dea(index: Index,
             with_permissions: bool,
             log_header=print_header,
             log=print_):
    """
    Create or update a DEA configured ODC instance.
    """
    log_header(f"ODC init of {index.url}")
    was_created = index.init_db(with_default_types=False,
                                with_permissions=with_permissions)

    if was_created:
        log('Created.')
    else:
        log('Updated.')

    log('Checking indexes/views.')
    index.metadata_types.check_field_indexes(
        allow_table_lock=True,
        rebuild_indexes=False,
        rebuild_views=True,
    )

    log_header('Checking DEA metadata types')
    # Add DEA metadata types, products.
    for _, md_type_def in read_documents(DEA_MD_TYPES):
        md = index.metadata_types.add(
            index.metadata_types.from_doc(md_type_def))
        log(f"{md.name}")

    log_header('Checking DEA products')
    for _, product_def in read_documents(*DEA_PRODUCTS_DIR.glob('*.yaml')):
        product = index.products.add_document(product_def)
        log(f"{product.name}")

    log_header('Checking DEA ingested definitions')

    for path in DEA_INGESTION_DIR.glob('*.yaml'):
        ingest_config = ingest.load_config_from_file(index, path)

        source_type, output_type = ingest.ensure_output_type(
            index, ingest_config, allow_product_changes=True)
        log(f"{output_type.name:<20}\t\t← {source_type.name}")
Ejemplo n.º 2
0
def index(db):
    """
    :type db: datacube.index.postgres._api.PostgresDb
    """
    return Index(db)
Ejemplo n.º 3
0
import os
from datacube.index.postgres._connections import PostgresDb
from datacube.index._api import Index
from datacube.api import GridWorkflow
from datacube.storage.storage import write_dataset_to_netcdf
from pprint import pprint
import numpy

nc_filename = os.path.expanduser(
    '~/datacube_ingest/recipes/ndvi_mean/ndvi_mean_%d_%d_%s.nc' %
    (12, -16, '1987'))

db = PostgresDb.from_config()
i = Index(db)
gwf = GridWorkflow(i, product='ls8_espa_mexico')
cells_list = gwf.list_cells(product='ls8_espa_mexico',
                            x=(-106, -101),
                            y=(19, 23))
sr = gwf.load(cells_list[(12, -16)], dask_chunks={'x': 1000, 'y': 1000})
sr['ndvi'] = (sr.nir - sr.red) / (sr.nir + sr.red) * 10000
ndvi = sr.drop(['pixel_qa', 'blue', 'red', 'green', 'nir', 'swir1', 'swir2'])
# Run temporal reductions and rename DataArrays
ndvi_mean = ndvi.mean('time', keep_attrs=True)
ndvi_mean = ndvi_mean.astype('int16')
ndvi_mean.attrs['crs'] = sr.attrs['crs']
write_dataset_to_netcdf(ndvi_mean, nc_filename)
print(nc_filename)
Ejemplo n.º 4
0
def index(db, local_config):
    """
    :type db: datacube.index.postgres._api.PostgresDb
    """
    return Index(db, local_config)