def __init__(self, provider, config):
super(Search, self).__init__(provider, config)
# Prepare the metadata mapping
# Do a shallow copy, the structure is flat enough for this to be sufficient
metas = DEFAULT_METADATA_MAPPING.copy()
# Update the defaults with the mapping value. This will add any new key
# added by the provider mapping that is not in the default metadata
metas.update(self.config.metadata_mapping)
self.config.metadata_mapping = mtd_cfg_as_jsonpath(
metas, self.config.metadata_mapping)
def __filter_item_model_properties(self, item_model, product_type):
"""Filter item model depending on product type metadata and its extensions.
Removes not needed parameters, and adds supplementary ones as
part of eodag extension.
:param item_model: item model from stac_config
:type item_model: dict
:param product_type: product type
:type product_type: str
:returns: filtered item model
:rtype: dict
"""
try:
product_type_dict = [
pt for pt in self.eodag_api.list_product_types(
provider=self.provider) if pt["ID"] == product_type
][0]
except IndexError:
raise MisconfiguredError(
"Product type {} not available for {}".format(
product_type, self.provider))
result_item_model = copy.deepcopy(item_model)
if product_type_dict["sensorType"] != "RADAR":
result_item_model["stac_extensions"].remove("sar")
extensions_names = self.get_stac_extensions_dict(
result_item_model["stac_extensions"]).keys()
for k, v in item_model["properties"].items():
# remove key if extension not in stac_extensions
if ":" in k and k.split(":")[0] not in extensions_names:
result_item_model["properties"].pop(k, None)
# build jsonpath for eodag product properties and adapt path
eodag_properties_dict = {
k: string_to_jsonpath(k, v.replace("$.", "$.product."))
for k, v in DEFAULT_METADATA_MAPPING.items()
if "$.properties." in v
}
# add missing properties as oseo:missingProperty
for k, v in eodag_properties_dict.items():
if (v not in result_item_model["properties"].values()
and k not in self.stac_config["metadata_ignore"]):
result_item_model["properties"]["oseo:" +
k] = string_to_jsonpath(k, v)
return result_item_model
def query(self, product_type=None, **kwargs):
"""Search for data on USGS catalogues
.. versionchanged::
1.0
* ``product_type`` is no longer mandatory
"""
product_type = kwargs.get("productType")
if product_type is None:
return [], 0
api.login(
self.config.credentials["username"],
self.config.credentials["password"],
save=True,
)
usgs_dataset = self.config.products[product_type]["dataset"]
usgs_catalog_node = self.config.products[product_type]["catalog_node"]
start_date = kwargs.pop("startTimeFromAscendingNode", None)
end_date = kwargs.pop("completionTimeFromAscendingNode", None)
footprint = kwargs.pop("geometry", None)
# Configuration to generate the download url of search results
result_summary_pattern = re.compile(
r"^ID: .+, Acquisition Date: .+, Path: (?P<path>\d+), Row: (?P<row>\d+)$" # noqa
)
# See https://pyformat.info/, on section "Padding and aligning strings" to
# understand {path:0>3} and {row:0>3}.
# It roughly means: 'if the string that will be passed as "path" has length < 3,
# prepend as much "0"s as needed to reach length 3' and same for "row"
dl_url_pattern = "{base_url}/L8/{path:0>3}/{row:0>3}/{entity}.tar.bz"
final = []
if footprint and len(footprint.keys()) == 4: # a rectangle (or bbox)
lower_left = {
"longitude": footprint["lonmin"],
"latitude": footprint["latmin"],
}
upper_right = {
"longitude": footprint["lonmax"],
"latitude": footprint["latmax"],
}
else:
lower_left, upper_right = None, None
try:
results = api.search(
usgs_dataset,
usgs_catalog_node,
start_date=start_date,
end_date=end_date,
ll=lower_left,
ur=upper_right,
)
for result in results["data"]["results"]:
r_lower_left = result["spatialFootprint"]["coordinates"][0][0]
r_upper_right = result["spatialFootprint"]["coordinates"][0][2]
summary_match = result_summary_pattern.match(
result["summary"]).groupdict()
result["geometry"] = geometry.box(r_lower_left[0],
r_lower_left[1],
r_upper_right[0],
r_upper_right[1])
# Same method as in base.py, Search.__init__()
# Prepare the metadata mapping
# Do a shallow copy, the structure is flat enough for this to be sufficient
metas = DEFAULT_METADATA_MAPPING.copy()
# Update the defaults with the mapping value. This will add any new key
# added by the provider mapping that is not in the default metadata.
# A deepcopy is done to prevent self.config.metadata_mapping from being modified when metas[metadata]
# is a list and is modified
metas.update(copy.deepcopy(self.config.metadata_mapping))
metas = mtd_cfg_as_jsonpath(metas)
result["productType"] = usgs_dataset
product_properties = properties_from_json(result, metas)
if getattr(self.config, "product_location_scheme",
"https") == "file":
product_properties["downloadLink"] = dl_url_pattern.format(
base_url="file://")
else:
product_properties["downloadLink"] = dl_url_pattern.format(
base_url=self.config.google_base_url.rstrip("/"),
entity=result["entityId"],
**summary_match)
final.append(
EOProduct(
productType=product_type,
provider=self.provider,
properties=product_properties,
geometry=footprint,
))
except USGSError as e:
logger.debug(
"Product type %s does not exist on catalogue %s",
usgs_dataset,
usgs_catalog_node,
)
logger.debug("Skipping error: %s", e)
api.logout()
return final, len(final)
def query(self,
product_type=None,
items_per_page=None,
page=None,
count=True,
**kwargs):
"""Search for data on USGS catalogues
.. versionchanged::
2.2.0
* Based on usgs library v0.3.0 which now uses M2M API. The library
is used for both search & download
.. versionchanged::
1.0
* ``product_type`` is no longer mandatory
"""
product_type = kwargs.get("productType")
if product_type is None:
return [], 0
try:
api.login(
self.config.credentials["username"],
self.config.credentials["password"],
save=True,
)
except USGSError:
raise AuthenticationError(
"Please check your USGS credentials.") from None
product_type_def_params = self.config.products.get(
product_type, self.config.products[GENERIC_PRODUCT_TYPE])
usgs_dataset = format_dict_items(product_type_def_params,
**kwargs)["dataset"]
start_date = kwargs.pop("startTimeFromAscendingNode", None)
end_date = kwargs.pop("completionTimeFromAscendingNode", None)
geom = kwargs.pop("geometry", None)
footprint = {}
if hasattr(geom, "bounds"):
(
footprint["lonmin"],
footprint["latmin"],
footprint["lonmax"],
footprint["latmax"],
) = geom.bounds
else:
footprint = geom
final = []
if footprint and len(footprint.keys()) == 4: # a rectangle (or bbox)
lower_left = {
"longitude": footprint["lonmin"],
"latitude": footprint["latmin"],
}
upper_right = {
"longitude": footprint["lonmax"],
"latitude": footprint["latmax"],
}
else:
lower_left, upper_right = None, None
try:
results = api.scene_search(
usgs_dataset,
start_date=start_date,
end_date=end_date,
ll=lower_left,
ur=upper_right,
max_results=items_per_page,
starting_number=(1 + (page - 1) * items_per_page),
)
# Same method as in base.py, Search.__init__()
# Prepare the metadata mapping
# Do a shallow copy, the structure is flat enough for this to be sufficient
metas = DEFAULT_METADATA_MAPPING.copy()
# Update the defaults with the mapping value. This will add any new key
# added by the provider mapping that is not in the default metadata.
# A deepcopy is done to prevent self.config.metadata_mapping from being modified when metas[metadata]
# is a list and is modified
metas.update(copy.deepcopy(self.config.metadata_mapping))
metas = mtd_cfg_as_jsonpath(metas)
for result in results["data"]["results"]:
result["productType"] = usgs_dataset
product_properties = properties_from_json(result, metas)
final.append(
EOProduct(
productType=product_type,
provider=self.provider,
properties=product_properties,
geometry=footprint,
))
except USGSError as e:
logger.warning(
"Product type %s does not exist on USGS EE catalog",
usgs_dataset,
)
logger.warning("Skipping error: %s", e)
api.logout()
if final:
# parse total_results
path_parsed = parse(
self.config.pagination["total_items_nb_key_path"])
total_results = path_parsed.find(results["data"])[0].value
else:
total_results = 0
return final, total_results