def _order_payload(
geometry: Union[Dict, Feature, FeatureCollection, List, GeoDataFrame,
Point, Polygon, ],
scene: Series,
) -> Tuple[str, Dict]:
"""
Helper that constructs necessary parameters for `Order.place` and `Order.estimate`.
Args:
geometry (Union[ Dict, Feature, FeatureCollection, List, GeoDataFrame, Point, Polygon, ]): The intended
output AOI of the order.
scene (GeoSeries): A single item/row of the result of `Catalog.search`. For instance, search_results.loc[0]
for the first scene of a catalog search result.
Returns:
str, Dict: A tuple including a provider name and order parameters.
"""
if not isinstance(scene, Series):
raise ValueError(
"`scene` parameter must be a GeoSeries, or a single item/row of a GeoDataFrame. "
"For instance, search_results.loc[0] returns a GeoSeries.")
aoi_fc = any_vector_to_fc(vector=geometry, )
aoi_geometry = fc_to_query_geometry(
fc=aoi_fc,
geometry_operation="intersects",
squash_multiple_features="union",
)
data_provider_name = scene.providerName
order_params = {"id": scene.id, "aoi": aoi_geometry}
return data_provider_name, order_params
def construct_parameters(
self,
geometry: Union[dict, Feature, FeatureCollection, geojson_Polygon,
list, GeoDataFrame, Polygon, Point, ] = None,
geometry_operation: Optional[str] = None,
handle_multiple_features: str = "union",
start_date: Optional[str] = None,
end_date: Optional[str] = None,
limit: Optional[int] = None,
scene_ids: Optional[list] = None,
assets: Optional[List[Asset]] = None,
) -> dict:
"""
Constructs workflow input parameters with a specified aoi, the default input parameters, and
optionally limit and order-ids. Further parameter editing needs to be done manually
via dict.update({key:value}).
Args:
geometry: One of Dict, FeatureCollection, Feature, List,
GeoDataFrame, shapely.geometry.Polygon, shapely.geometry.Point. All
assume EPSG 4326.
geometry_operation: Desired operation, One of "bbox", "intersects", "contains".
limit: Maximum number of expected results.
start_date: Query period starting day, format "2020-01-01".
end_date: Query period ending day, format "2020-01-01".
scene_ids: List of scene_ids, if given ignores all other parameters except geometry.
assets: Optional, can be used to incorporate existing assets in Storage (result
of Orders for instance) into new workflows.
Returns:
Dictionary of constructed input parameters.
"""
input_parameters = self._get_default_parameters()
try:
data_block_name = list(input_parameters.keys())[0]
except IndexError as e:
raise ValueError("The Workflow has no workflow tasks.") from e
if assets is not None:
# Needs to be handled in this function(not run_job) as it is only
# relevant for the data block.
asset_ids = [
asset.asset_id for asset in assets if asset.source == "BLOCK"
]
if not asset_ids:
raise ValueError(
"None of the assets are usable in a workflow since the source is not `BLOCK`."
)
input_parameters[data_block_name] = {"asset_ids": asset_ids}
else:
if limit is not None:
input_parameters[data_block_name]["limit"] = limit
if scene_ids is not None:
if not isinstance(scene_ids, list):
scene_ids = [scene_ids]
input_parameters[data_block_name]["ids"] = scene_ids
input_parameters[data_block_name]["limit"] = len(scene_ids)
input_parameters[data_block_name].pop("time")
elif start_date is not None and end_date is not None:
time = f"{start_date}T00:00:00Z/{end_date}T23:59:59Z"
input_parameters[data_block_name]["time"] = time
if geometry is not None:
aoi_fc = any_vector_to_fc(vector=geometry, )
aoi_feature = fc_to_query_geometry(
fc=aoi_fc,
geometry_operation=geometry_operation, # type: ignore
squash_multiple_features=handle_multiple_features,
)
input_parameters[data_block_name][
geometry_operation] = aoi_feature
return input_parameters
def construct_parameters(
geometry: Union[Dict, Feature, FeatureCollection, List, GeoDataFrame,
Point, Polygon, ],
start_date: str = "2020-01-01",
end_date: str = "2020-01-30",
sensors: List[str] = [
"pleiades",
"spot",
"sentinel1",
"sentinel2",
"sentinel3",
"sentinel5p",
],
limit: int = 10,
max_cloudcover: float = 100,
sortby: str = "acquisitionDate",
ascending: bool = True,
) -> Dict:
"""
Follows STAC principles and property names.
Args:
geometry: The search geometry, one of Dict, Feature, FeatureCollection,
List, GeoDataFrame, Point, Polygon.
start_date: Query period starting day, format "2020-01-01".
end_date: Query period ending day, format "2020-01-01".
sensors: The satellite sensors to search for, one or multiple of
["pleiades", "spot", "sentinel1", "sentinel2", "sentinel3", "sentinel5p"]
limit: The maximum number of search results to return (1-max.500).
max_cloudcover: Maximum cloudcover % - e.g. 100 will return all scenes,
8.4 will return all scenes with 8.4 or less cloudcover.
Ignored for sensors that have no cloudcover (e.g. sentinel1).
sortby: The property to sort by, "cloudCoverage", "acquisitionDate",
"acquisitionIdentifier", "incidenceAngle", "snowCover".
ascending: Ascending sort order by default, descending if False.
Returns:
The constructed parameters dictionary.
"""
datetime = f"{start_date}T00:00:00Z/{end_date}T23:59:59Z"
block_filters: List[str] = []
for sensor in sensors:
if sensor not in list(supported_sensors.keys()):
raise ValueError(
f"Currently only these sensors are supported: "
f"{list(supported_sensors.keys())}")
block_filters.extend(supported_sensors[sensor]["blocks"])
aoi_fc = any_vector_to_fc(vector=geometry, )
aoi_geometry = fc_to_query_geometry(
fc=aoi_fc,
geometry_operation="intersects",
squash_multiple_features="union",
)
sort_order = "asc" if ascending else "desc"
query_filters = {"dataBlock": {"in": block_filters}}
if sensors != ["sentinel1"]:
query_filters["cloudCoverage"] = {
"lte": max_cloudcover
} # type: ignore
search_parameters = {
"datetime": datetime,
"intersects": aoi_geometry,
"limit": limit,
"query": query_filters,
"sortby": [{
"field": f"properties.{sortby}",
"direction": sort_order
}],
}
return search_parameters
def construct_parameters(
self,
geometry: Union[
Dict,
Feature,
FeatureCollection,
geojson_Polygon,
List,
GeoDataFrame,
Polygon,
Point,
] = None,
geometry_operation: str = None,
handle_multiple_features: str = "union",
start_date: str = None,
end_date: str = None,
limit: int = None,
scene_ids: List = None,
order_ids: List[str] = None,
) -> Dict:
"""
Constructs workflow input parameters with a specified aoi, the default input parameters, and
optionally limit and order-ids. Further parameter editing needs to be done manually
via dict.update({key:value}).
Args:
geometry: One of Dict, FeatureCollection, Feature, List,
GeoDataFrame, shapely.geometry.Polygon, shapely.geometry.Point. All
assume EPSG 4326.
geometry_operation: Desired operation, One of "bbox", "intersects", "contains".
limit: Maximum number of expected results.
start_date: Query period starting day, format "2020-01-01".
end_date: Query period ending day, format "2020-01-01".
scene_ids: List of scene_ids, if given ignores all other parameters except geometry.
order_ids: Optional, can be used to incorporate existing bought imagery on UP42
into new workflows.
Returns:
Dictionary of constructed input parameters.
"""
input_parameters = self._get_default_parameters()
data_block_name = list(input_parameters.keys())[0]
if order_ids is not None:
# Needs to be handled in this function(not run_job) as it is only
# relevant for the data block.
input_parameters[data_block_name] = {"order_ids": order_ids}
else:
if limit is not None:
input_parameters[data_block_name]["limit"] = limit
if scene_ids is not None:
if not isinstance(scene_ids, list):
scene_ids = [scene_ids]
input_parameters[data_block_name]["ids"] = scene_ids
input_parameters[data_block_name]["limit"] = len(scene_ids)
input_parameters[data_block_name].pop("time")
elif start_date is not None and end_date is not None:
time = f"{start_date}T00:00:00Z/{end_date}T23:59:59Z"
input_parameters[data_block_name]["time"] = time
if geometry is not None:
aoi_fc = any_vector_to_fc(
vector=geometry,
)
aoi_feature = fc_to_query_geometry(
fc=aoi_fc,
geometry_operation=geometry_operation, # type: ignore
squash_multiple_features=handle_multiple_features,
)
input_parameters[data_block_name][geometry_operation] = aoi_feature
return input_parameters
