def ingest_products():
valid_uuids = []
for uuid, ls5_dataset_path in example_ls5_dataset_paths.items():
valid_uuids.append(uuid)
index_dataset(ls5_dataset_path)
# Ensure that datasets are actually indexed
ensure_datasets_are_indexed(index, valid_uuids)
# Ingest them
clirunner(['ingest', '--config-file', str(config_path)])
# Validate that the ingestion is working as expected
datasets = index.datasets.search_eager(product='ls5_nbar_albers')
assert len(datasets) > 0
assert datasets[0].managed
check_open_with_api(index, len(valid_uuids))
check_data_with_api(index, len(valid_uuids))
# NetCDF specific checks, based on the saved NetCDF file
ds_path = str(datasets[0].local_path)
with netCDF4.Dataset(ds_path) as nco:
check_data_shape(nco)
check_grid_mapping(nco)
check_cf_compliance(nco)
check_dataset_metadata_in_storage_unit(nco,
example_ls5_dataset_paths)
check_attributes(nco, config['global_attributes'])
name = config['measurements'][0]['name']
check_attributes(nco[name], config['measurements'][0]['attrs'])
check_open_with_xarray(ds_path)
def index_products():
valid_uuids = []
for uuid, ls5_dataset_path in example_ls5_dataset_paths.items():
valid_uuids.append(uuid)
index_dataset(ls5_dataset_path)
# Ensure that datasets are actually indexed
ensure_datasets_are_indexed(index, valid_uuids)
return valid_uuids
def test_index_out_of_bound_error(clirunner, index, tmpdir, example_ls5_dataset_paths, ingest_configs):
"""
Test for the case where ingestor processes upto `--queue-size` number of tasks and not all the available scenes
"""
# Make a test ingestor configuration
config = INGESTER_CONFIGS / ingest_configs['ls5_nbar_albers']
config_path, config = prepare_test_ingestion_configuration(tmpdir, None,
config, mode='fast_ingest')
def index_dataset(path):
return clirunner(['dataset', 'add', str(path)])
# Set the queue size to process 5 tiles
queue_size = 5
valid_uuids = []
for uuid, ls5_dataset_path in example_ls5_dataset_paths.items():
valid_uuids.append(uuid)
index_dataset(ls5_dataset_path)
# Ensure that datasets are actually indexed
ensure_datasets_are_indexed(index, valid_uuids)
# Locationless scenario within database arises when we run the sync tool (with --update-location option)
# on the disk where the actual file is removed and regenerated again with new dataset id.
for indexed_uuid in valid_uuids:
dc1 = datacube.Datacube(index=index)
datasets = dc1.find_datasets(product='ls5_nbar_scene')
try:
# Remove location from the index, to simulate indexed out of range scenario
res = dc1.index.datasets.remove_location(indexed_uuid, datasets[0].local_uri)
except AttributeError:
# Do for one dataset, ignore any other attribute errors
pass
assert res is True, "Error for %r. output: %r" % (indexed_uuid, res)
# Ingest scenes with locationless dataset
clirunner([
'ingest',
'--config-file',
str(config_path),
'--queue-size',
queue_size,
'--allow-product-changes',
])
# Validate that the ingestion is working as expected
datasets = index.datasets.search_eager(product='ls5_nbar_albers')
assert len(datasets) > 0
assert datasets[0].managed
check_open_with_api(index, len(valid_uuids))
# NetCDF specific checks, based on the saved NetCDF file
ds_path = str(datasets[0].local_path)
with netCDF4.Dataset(ds_path) as nco:
check_data_shape(nco)
check_grid_mapping(nco)
check_cf_compliance(nco)
check_dataset_metadata_in_storage_unit(nco, example_ls5_dataset_paths)
check_attributes(nco, config['global_attributes'])
name = config['measurements'][0]['name']
check_attributes(nco[name], config['measurements'][0]['attrs'])
check_open_with_xarray(ds_path)