Catalog

The Catalog class enables access to the UP42 catalog functionality (data archive search & ordering).

Use catalog:

catalog = up42.initialize_catalog()

Source code in up42/catalog.py

class Catalog(CatalogBase, VizTools):
    """
    The Catalog class enables access to the UP42 catalog functionality (data archive search & ordering).

    Use catalog:
    ```python
    catalog = up42.initialize_catalog()
    ```
    """

    def __init__(self, auth: Auth):
        self.auth = auth
        self.quicklooks = None
        self.type = "ARCHIVE"
        self.data_products: Union[None, dict] = None

    def __repr__(self):
        return f"Catalog(auth={self.auth})"

    def estimate_order(self, order_parameters: Union[dict, None], **kwargs) -> int:
        """
        Estimate the cost of an order.

        Args:
            order_parameters: A dictionary like {dataProduct: ..., "params": {"id": ..., "aoi": ...}}

        Returns:
            int: An estimated cost for the order in UP42 credits.

        Warning "Deprecated order parameters"
            The use of the 'scene' and 'geometry' parameters for the data estimation is deprecated. Please use the new
            order_parameters parameter as described above.
        """
        if "scene" in kwargs or "geometry" in kwargs:
            # Deprecated, to be removed, use order_parameters.
            message = (
                "The use of the 'scene' and 'geometry' parameters for the data estimation is deprecated. "
                "Please use the new 'order_parameters' parameter."
            )
            warnings.warn(message, DeprecationWarning, stacklevel=2)
        elif order_parameters is None:
            raise ValueError("Please provide the 'order_parameters' parameter!")
        return Order.estimate(self.auth, order_parameters)  # type: ignore

    @deprecation("construct_search_parameters", "0.25.0")
    def construct_parameters(self, **kwargs):  # pragma: no cover
        """Deprecated, see construct_search_parameters"""
        return self.construct_search_parameters(**kwargs)

    @staticmethod
    def construct_search_parameters(
        geometry: Union[FeatureCollection, Feature, dict, list, GeoDataFrame, Polygon],
        collections: List[str],
        start_date: str = "2020-01-01",
        end_date: str = "2020-01-30",
        usage_type: List[str] = None,
        limit: int = 10,
        max_cloudcover: Optional[int] = None,
        sortby: str = "acquisitionDate",
        ascending: bool = True,
    ) -> dict:
        """
        Helps constructing the parameters dictionary required for the search.

        Args:
            geometry: The search geometry, default a Polygon. One of FeatureCollection, Feature,
                dict (geojson geometry), list (bounds coordinates), GeoDataFrame, shapely.Polygon, shapely.Point.
                All assume EPSG 4326!
            collections: The satellite sensor collections to search for, e.g. ["phr"] or ["phr", "spot"].
                Also see catalog.get_collections().
            start_date: Query period starting day, format "2020-01-01".
            end_date: Query period ending day, format "2020-01-01".
            usage_type: Optional. Filter for imagery that can be purchased & downloaded or also
                processed. ["DATA"] (can only be downloaded), ["ANALYTICS"] (can be downloaded
                or used directly with a processing algorithm), ["DATA", "ANALYTICS"]
                (can be any combination). The filter is inclusive, using ["DATA"] can
                also result in results with ["DATA", "ANALYTICS"].
            limit: The maximum number of search results to return (1-max.500).
            max_cloudcover: Optional. Maximum cloudcover % - e.g. 100 will return all scenes,
                8.4 will return all scenes with 8.4 or less cloudcover.
            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.
        """
        time_period = (
            f"{format_time(start_date)}/{format_time(end_date, set_end_of_day=True)}"
        )
        aoi_fc = any_vector_to_fc(
            vector=geometry,
        )
        aoi_geometry = fc_to_query_geometry(fc=aoi_fc, geometry_operation="intersects")
        sort_order = "asc" if ascending else "desc"

        query_filters: Dict[Any, Any] = {}
        if max_cloudcover is not None:
            query_filters["cloudCoverage"] = {"lte": max_cloudcover}  # type: ignore

        if usage_type is not None:
            if usage_type == ["DATA"]:
                query_filters["up42:usageType"] = {"in": ["DATA"]}
            elif usage_type == ["ANALYTICS"]:
                query_filters["up42:usageType"] = {"in": ["ANALYTICS"]}
            elif usage_type == ["DATA", "ANALYTICS"]:
                query_filters["up42:usageType"] = {"in": ["DATA", "ANALYTICS"]}
            else:
                raise ValueError("Select correct `usage_type`")

        search_parameters = {
            "datetime": time_period,
            "intersects": aoi_geometry,
            "limit": limit,
            "collections": collections,
            "query": query_filters,
            "sortby": [{"field": f"properties.{sortby}", "direction": sort_order}],
        }

        return search_parameters

    def search(
        self, search_parameters: dict, as_dataframe: bool = True
    ) -> Union[GeoDataFrame, dict]:
        """
        Searches the catalog for the the search parameters and returns the metadata of
        the matching scenes.

        Args:
            search_parameters: The catalog search parameters, see example.
            as_dataframe: return type, GeoDataFrame if True (default), FeatureCollection if False.

        Returns:
            The search results as a GeoDataFrame, optionally as json dict.

        Example:
            ```python
                search_parameters={
                    "datetime": "2019-01-01T00:00:00Z/2019-01-15T23:59:59Z",
                    "collections": ["phr"],
                    "intersects": {
                        "type": "Polygon",
                        "coordinates": [[[13.32113746,52.73971768],[13.15981158,52.2092959],
                        [13.62204483,52.15632025],[13.78859517,52.68655119],[13.32113746,
                        52.73971768]]]},
                    "limit": 10,
                    "sortby": [{"field" : "properties.acquisitionDate", "direction" : "asc"}]
                    }
            ```
        """
        logger.info(f"Searching catalog with search_parameters: {search_parameters}")

        # The API request would fail with a limit above 500, thus 500 is forced in the initial
        # request but additional results are handled below via pagination.
        try:
            max_limit = search_parameters["limit"]
        except KeyError:
            logger.info("No `limit` parameter in search_parameters, using default 500.")
            max_limit = 500

        if max_limit > 500:
            search_parameters["limit"] = 500

        # UP42 API can query multiple collections of the same host at once.
        if self.data_products is None:
            self.data_products = self.get_data_products(basic=True)  # type: ignore
        hosts = [
            v["host"]
            for v in self.data_products.values()  # type: ignore
            if v["collection"] in search_parameters["collections"]
        ]
        if not hosts:
            raise ValueError(
                f"Selected collections {search_parameters['collections']} are not valid. See "
                f"catalog.get_collections."
            )
        if len(set(hosts)) > 1:
            raise ValueError(
                "Only collections with the same host can be searched at the same time. Please adjust the "
                "collections in the search_parameters!"
            )
        host = hosts[0]

        url = f"{self.auth._endpoint()}/catalog/hosts/{host}/stac/search"
        response_json: dict = self.auth._request("POST", url, search_parameters)
        features = response_json["features"]

        # Search results with more than 500 items are given as 50-per-page additional pages.
        while len(features) < max_limit:
            pagination_exhausted = len(response_json["links"]) == 1
            if pagination_exhausted:
                break
            next_page_url = response_json["links"][1]["href"]
            response_json = self.auth._request("POST", next_page_url, search_parameters)
            features += response_json["features"]

        features = features[:max_limit]
        if not features:
            df = GeoDataFrame(columns=["geometry"], geometry="geometry")
        else:
            df = GeoDataFrame.from_features(
                FeatureCollection(features=features), crs="EPSG:4326"
            )

        logger.info(f"{df.shape[0]} results returned.")
        if as_dataframe:
            return df
        else:
            return df.__geo_interface__

    def construct_order_parameters(
        self,
        data_product_id: str,
        image_id: str,
        aoi: Union[
            dict,
            Feature,
            FeatureCollection,
            list,
            GeoDataFrame,
            Polygon,
        ] = None,
    ):
        """
        Helps constructing the parameters dictionary required for the catalog order. Some datasets have
        additional parameters that are added to the output dictionary with value None. The potential values to
        select from are given in the logs, for more detail on the parameter use `catalog.get_data_product_schema()`.

        Args:
            data_product_id: Id of the desired UP42 data product, see `catalog.get_data_products`
            image_id: The id of the desired image (from search results)
            aoi: The geometry of the order, one of dict, Feature, FeatureCollection,
                list, GeoDataFrame, Polygon. Optional for "full-image products".
        Returns:
            The order parameters dictionary.

        Example:
            ```python
            order_parameters = catalog.construct_order_parameters(
                data_product_id='647780db-5a06-4b61-b525-577a8b68bb54',
                image_id='6434e7af-2d41-4ded-a789-fb1b2447ac92',
                aoi={'type': 'Polygon',
                'coordinates': (((13.375966, 52.515068),
                  (13.375966, 52.516639),
                  (13.378314, 52.516639),
                  (13.378314, 52.515068),
                  (13.375966, 52.515068)),)})
            ```
        """
        order_parameters = {
            "dataProduct": data_product_id,
            "params": {"id": image_id},
        }
        logger.info(
            "See `catalog.get_data_product_schema(data_product_id)` for more detail on the parameter options."
        )
        schema = self.get_data_product_schema(data_product_id)
        order_parameters = autocomplete_order_parameters(order_parameters, schema)

        # Some catalog orders, e.g. Capella don't require aoi (full image order)
        # Handled on API level, don't manipulate in SDK, providers might accept geometries in the future.
        if aoi is not None:
            aoi = any_vector_to_fc(vector=aoi)
            aoi = fc_to_query_geometry(fc=aoi, geometry_operation="intersects")
            order_parameters["params"]["aoi"] = aoi  # type: ignore

        return order_parameters

    def download_quicklooks(
        self,
        image_ids: List[str],
        collection: str,
        output_directory: Union[str, Path, None] = None,
    ) -> List[str]:
        """
        Gets the quicklooks of scenes from a single sensor. After download, can
        be plotted via catalog.map_quicklooks() or catalog.plot_quicklooks().

        Args:
            image_ids: List of provider image_ids e.g. ["6dffb8be-c2ab-46e3-9c1c-6958a54e4527"].
                Access the search results id column via `list(search_results.id)`.
            collection: The data collection corresponding to the image ids.
            output_directory: The file output directory, defaults to the current working
                directory.

        Returns:
            List of quicklook image output file paths.
        """
        if self.data_products is None:
            self.data_products = self.get_data_products(basic=True)  # type: ignore
        host = [
            v["host"]
            for v in self.data_products.values()  # type: ignore
            if v["collection"] == collection
        ]
        if not host:
            raise ValueError(
                f"Selected collections {collection} is not valid. See catalog.get_collections."
            )
        host = host[0]
        logger.info(f"Downloading quicklooks from provider {host}.")

        if output_directory is None:
            output_directory = Path.cwd() / f"project_{self.auth.project_id}/catalog"
        else:
            output_directory = Path(output_directory)
        output_directory.mkdir(parents=True, exist_ok=True)
        logger.info(f"Download directory: {str(output_directory)}")

        if isinstance(image_ids, str):
            image_ids = [image_ids]

        out_paths: List[str] = []
        for image_id in tqdm(image_ids):
            try:
                url = (
                    f"{self.auth._endpoint()}/catalog/{host}/image/{image_id}/quicklook"
                )
                response = self.auth._request(
                    request_type="GET", url=url, return_text=False
                )
                out_path = output_directory / f"quicklook_{image_id}.jpg"
                out_paths.append(str(out_path))
                with open(out_path, "wb") as dst:
                    for chunk in response:
                        dst.write(chunk)
            except ValueError:
                logger.warning(
                    f"Image with id {image_id} does not have quicklook available. Skipping ..."
                )

        self.quicklooks = out_paths  # pylint: disable=attribute-defined-outside-init
        return out_paths

Functions

construct_order_parameters(data_product_id, image_id, aoi=None)

Helps constructing the parameters dictionary required for the catalog order. Some datasets have additional parameters that are added to the output dictionary with value None. The potential values to select from are given in the logs, for more detail on the parameter use catalog.get_data_product_schema().

Parameters:

Name	Type	Description	Default
data_product_id	str	Id of the desired UP42 data product, see catalog.get_data_products	required
image_id	str	The id of the desired image (from search results)	required
aoi	Union[dict, Feature, FeatureCollection, list, GeoDataFrame, Polygon]	The geometry of the order, one of dict, Feature, FeatureCollection, list, GeoDataFrame, Polygon. Optional for "full-image products".	None

Returns:

Type	Description
	The order parameters dictionary.

Example

order_parameters = catalog.construct_order_parameters(
    data_product_id='647780db-5a06-4b61-b525-577a8b68bb54',
    image_id='6434e7af-2d41-4ded-a789-fb1b2447ac92',
    aoi={'type': 'Polygon',
    'coordinates': (((13.375966, 52.515068),
      (13.375966, 52.516639),
      (13.378314, 52.516639),
      (13.378314, 52.515068),
      (13.375966, 52.515068)),)})

Source code in up42/catalog.py

def construct_order_parameters(
    self,
    data_product_id: str,
    image_id: str,
    aoi: Union[
        dict,
        Feature,
        FeatureCollection,
        list,
        GeoDataFrame,
        Polygon,
    ] = None,
):
    """
    Helps constructing the parameters dictionary required for the catalog order. Some datasets have
    additional parameters that are added to the output dictionary with value None. The potential values to
    select from are given in the logs, for more detail on the parameter use `catalog.get_data_product_schema()`.

    Args:
        data_product_id: Id of the desired UP42 data product, see `catalog.get_data_products`
        image_id: The id of the desired image (from search results)
        aoi: The geometry of the order, one of dict, Feature, FeatureCollection,
            list, GeoDataFrame, Polygon. Optional for "full-image products".
    Returns:
        The order parameters dictionary.

    Example:
        ```python
        order_parameters = catalog.construct_order_parameters(
            data_product_id='647780db-5a06-4b61-b525-577a8b68bb54',
            image_id='6434e7af-2d41-4ded-a789-fb1b2447ac92',
            aoi={'type': 'Polygon',
            'coordinates': (((13.375966, 52.515068),
              (13.375966, 52.516639),
              (13.378314, 52.516639),
              (13.378314, 52.515068),
              (13.375966, 52.515068)),)})
        ```
    """
    order_parameters = {
        "dataProduct": data_product_id,
        "params": {"id": image_id},
    }
    logger.info(
        "See `catalog.get_data_product_schema(data_product_id)` for more detail on the parameter options."
    )
    schema = self.get_data_product_schema(data_product_id)
    order_parameters = autocomplete_order_parameters(order_parameters, schema)

    # Some catalog orders, e.g. Capella don't require aoi (full image order)
    # Handled on API level, don't manipulate in SDK, providers might accept geometries in the future.
    if aoi is not None:
        aoi = any_vector_to_fc(vector=aoi)
        aoi = fc_to_query_geometry(fc=aoi, geometry_operation="intersects")
        order_parameters["params"]["aoi"] = aoi  # type: ignore

    return order_parameters

construct_parameters(**kwargs)

Deprecated, see construct_search_parameters

Source code in up42/catalog.py

@deprecation("construct_search_parameters", "0.25.0")
def construct_parameters(self, **kwargs):  # pragma: no cover
    """Deprecated, see construct_search_parameters"""
    return self.construct_search_parameters(**kwargs)

construct_search_parameters(geometry, collections, start_date='2020-01-01', end_date='2020-01-30', usage_type=None, limit=10, max_cloudcover=None, sortby='acquisitionDate', ascending=True) staticmethod

Helps constructing the parameters dictionary required for the search.

Parameters:

Name	Type	Description	Default
geometry	Union[FeatureCollection, Feature, dict, list, GeoDataFrame, Polygon]	The search geometry, default a Polygon. One of FeatureCollection, Feature, dict (geojson geometry), list (bounds coordinates), GeoDataFrame, shapely.Polygon, shapely.Point. All assume EPSG 4326!	required
collections	List[str]	The satellite sensor collections to search for, e.g. ["phr"] or ["phr", "spot"]. Also see catalog.get_collections().	required
start_date	str	Query period starting day, format "2020-01-01".	'2020-01-01'
end_date	str	Query period ending day, format "2020-01-01".	'2020-01-30'
usage_type	List[str]	Optional. Filter for imagery that can be purchased & downloaded or also processed. ["DATA"] (can only be downloaded), ["ANALYTICS"] (can be downloaded or used directly with a processing algorithm), ["DATA", "ANALYTICS"] (can be any combination). The filter is inclusive, using ["DATA"] can also result in results with ["DATA", "ANALYTICS"].	None
limit	int	The maximum number of search results to return (1-max.500).	10
max_cloudcover	Optional[int]	Optional. Maximum cloudcover % - e.g. 100 will return all scenes, 8.4 will return all scenes with 8.4 or less cloudcover.	None
sortby	str	The property to sort by, "cloudCoverage", "acquisitionDate", "acquisitionIdentifier", "incidenceAngle", "snowCover".	'acquisitionDate'
ascending	bool	Ascending sort order by default, descending if False.	True

Returns:

Type	Description
dict	The constructed parameters dictionary.

Source code in up42/catalog.py

@staticmethod
def construct_search_parameters(
    geometry: Union[FeatureCollection, Feature, dict, list, GeoDataFrame, Polygon],
    collections: List[str],
    start_date: str = "2020-01-01",
    end_date: str = "2020-01-30",
    usage_type: List[str] = None,
    limit: int = 10,
    max_cloudcover: Optional[int] = None,
    sortby: str = "acquisitionDate",
    ascending: bool = True,
) -> dict:
    """
    Helps constructing the parameters dictionary required for the search.

    Args:
        geometry: The search geometry, default a Polygon. One of FeatureCollection, Feature,
            dict (geojson geometry), list (bounds coordinates), GeoDataFrame, shapely.Polygon, shapely.Point.
            All assume EPSG 4326!
        collections: The satellite sensor collections to search for, e.g. ["phr"] or ["phr", "spot"].
            Also see catalog.get_collections().
        start_date: Query period starting day, format "2020-01-01".
        end_date: Query period ending day, format "2020-01-01".
        usage_type: Optional. Filter for imagery that can be purchased & downloaded or also
            processed. ["DATA"] (can only be downloaded), ["ANALYTICS"] (can be downloaded
            or used directly with a processing algorithm), ["DATA", "ANALYTICS"]
            (can be any combination). The filter is inclusive, using ["DATA"] can
            also result in results with ["DATA", "ANALYTICS"].
        limit: The maximum number of search results to return (1-max.500).
        max_cloudcover: Optional. Maximum cloudcover % - e.g. 100 will return all scenes,
            8.4 will return all scenes with 8.4 or less cloudcover.
        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.
    """
    time_period = (
        f"{format_time(start_date)}/{format_time(end_date, set_end_of_day=True)}"
    )
    aoi_fc = any_vector_to_fc(
        vector=geometry,
    )
    aoi_geometry = fc_to_query_geometry(fc=aoi_fc, geometry_operation="intersects")
    sort_order = "asc" if ascending else "desc"

    query_filters: Dict[Any, Any] = {}
    if max_cloudcover is not None:
        query_filters["cloudCoverage"] = {"lte": max_cloudcover}  # type: ignore

    if usage_type is not None:
        if usage_type == ["DATA"]:
            query_filters["up42:usageType"] = {"in": ["DATA"]}
        elif usage_type == ["ANALYTICS"]:
            query_filters["up42:usageType"] = {"in": ["ANALYTICS"]}
        elif usage_type == ["DATA", "ANALYTICS"]:
            query_filters["up42:usageType"] = {"in": ["DATA", "ANALYTICS"]}
        else:
            raise ValueError("Select correct `usage_type`")

    search_parameters = {
        "datetime": time_period,
        "intersects": aoi_geometry,
        "limit": limit,
        "collections": collections,
        "query": query_filters,
        "sortby": [{"field": f"properties.{sortby}", "direction": sort_order}],
    }

    return search_parameters

download_quicklooks(image_ids, collection, output_directory=None)

Gets the quicklooks of scenes from a single sensor. After download, can be plotted via catalog.map_quicklooks() or catalog.plot_quicklooks().

Parameters:

Name	Type	Description	Default
image_ids	List[str]	List of provider image_ids e.g. ["6dffb8be-c2ab-46e3-9c1c-6958a54e4527"]. Access the search results id column via list(search_results.id).	required
collection	str	The data collection corresponding to the image ids.	required
output_directory	Union[str, Path, None]	The file output directory, defaults to the current working directory.	None

Returns:

Type	Description
List[str]	List of quicklook image output file paths.

Source code in up42/catalog.py

def download_quicklooks(
    self,
    image_ids: List[str],
    collection: str,
    output_directory: Union[str, Path, None] = None,
) -> List[str]:
    """
    Gets the quicklooks of scenes from a single sensor. After download, can
    be plotted via catalog.map_quicklooks() or catalog.plot_quicklooks().

    Args:
        image_ids: List of provider image_ids e.g. ["6dffb8be-c2ab-46e3-9c1c-6958a54e4527"].
            Access the search results id column via `list(search_results.id)`.
        collection: The data collection corresponding to the image ids.
        output_directory: The file output directory, defaults to the current working
            directory.

    Returns:
        List of quicklook image output file paths.
    """
    if self.data_products is None:
        self.data_products = self.get_data_products(basic=True)  # type: ignore
    host = [
        v["host"]
        for v in self.data_products.values()  # type: ignore
        if v["collection"] == collection
    ]
    if not host:
        raise ValueError(
            f"Selected collections {collection} is not valid. See catalog.get_collections."
        )
    host = host[0]
    logger.info(f"Downloading quicklooks from provider {host}.")

    if output_directory is None:
        output_directory = Path.cwd() / f"project_{self.auth.project_id}/catalog"
    else:
        output_directory = Path(output_directory)
    output_directory.mkdir(parents=True, exist_ok=True)
    logger.info(f"Download directory: {str(output_directory)}")

    if isinstance(image_ids, str):
        image_ids = [image_ids]

    out_paths: List[str] = []
    for image_id in tqdm(image_ids):
        try:
            url = (
                f"{self.auth._endpoint()}/catalog/{host}/image/{image_id}/quicklook"
            )
            response = self.auth._request(
                request_type="GET", url=url, return_text=False
            )
            out_path = output_directory / f"quicklook_{image_id}.jpg"
            out_paths.append(str(out_path))
            with open(out_path, "wb") as dst:
                for chunk in response:
                    dst.write(chunk)
        except ValueError:
            logger.warning(
                f"Image with id {image_id} does not have quicklook available. Skipping ..."
            )

    self.quicklooks = out_paths  # pylint: disable=attribute-defined-outside-init
    return out_paths

estimate_order(order_parameters, **kwargs)

Estimate the cost of an order.

Parameters:

Name	Type	Description	Default
order_parameters	Union[dict, None]	A dictionary like {dataProduct: ..., "params": {"id": ..., "aoi": ...}}	required

Returns:

Name	Type	Description
int	int	An estimated cost for the order in UP42 credits.

Warning "Deprecated order parameters" The use of the 'scene' and 'geometry' parameters for the data estimation is deprecated. Please use the new order_parameters parameter as described above.

Source code in up42/catalog.py

def estimate_order(self, order_parameters: Union[dict, None], **kwargs) -> int:
    """
    Estimate the cost of an order.

    Args:
        order_parameters: A dictionary like {dataProduct: ..., "params": {"id": ..., "aoi": ...}}

    Returns:
        int: An estimated cost for the order in UP42 credits.

    Warning "Deprecated order parameters"
        The use of the 'scene' and 'geometry' parameters for the data estimation is deprecated. Please use the new
        order_parameters parameter as described above.
    """
    if "scene" in kwargs or "geometry" in kwargs:
        # Deprecated, to be removed, use order_parameters.
        message = (
            "The use of the 'scene' and 'geometry' parameters for the data estimation is deprecated. "
            "Please use the new 'order_parameters' parameter."
        )
        warnings.warn(message, DeprecationWarning, stacklevel=2)
    elif order_parameters is None:
        raise ValueError("Please provide the 'order_parameters' parameter!")
    return Order.estimate(self.auth, order_parameters)  # type: ignore

search(search_parameters, as_dataframe=True)

Searches the catalog for the the search parameters and returns the metadata of the matching scenes.

Parameters:

Name	Type	Description	Default
search_parameters	dict	The catalog search parameters, see example.	required
as_dataframe	bool	return type, GeoDataFrame if True (default), FeatureCollection if False.	True

Returns:

Type	Description
Union[GeoDataFrame, dict]	The search results as a GeoDataFrame, optionally as json dict.

Example

    search_parameters={
        "datetime": "2019-01-01T00:00:00Z/2019-01-15T23:59:59Z",
        "collections": ["phr"],
        "intersects": {
            "type": "Polygon",
            "coordinates": [[[13.32113746,52.73971768],[13.15981158,52.2092959],
            [13.62204483,52.15632025],[13.78859517,52.68655119],[13.32113746,
            52.73971768]]]},
        "limit": 10,
        "sortby": [{"field" : "properties.acquisitionDate", "direction" : "asc"}]
        }

Source code in up42/catalog.py

def search(
    self, search_parameters: dict, as_dataframe: bool = True
) -> Union[GeoDataFrame, dict]:
    """
    Searches the catalog for the the search parameters and returns the metadata of
    the matching scenes.

    Args:
        search_parameters: The catalog search parameters, see example.
        as_dataframe: return type, GeoDataFrame if True (default), FeatureCollection if False.

    Returns:
        The search results as a GeoDataFrame, optionally as json dict.

    Example:
        ```python
            search_parameters={
                "datetime": "2019-01-01T00:00:00Z/2019-01-15T23:59:59Z",
                "collections": ["phr"],
                "intersects": {
                    "type": "Polygon",
                    "coordinates": [[[13.32113746,52.73971768],[13.15981158,52.2092959],
                    [13.62204483,52.15632025],[13.78859517,52.68655119],[13.32113746,
                    52.73971768]]]},
                "limit": 10,
                "sortby": [{"field" : "properties.acquisitionDate", "direction" : "asc"}]
                }
        ```
    """
    logger.info(f"Searching catalog with search_parameters: {search_parameters}")

    # The API request would fail with a limit above 500, thus 500 is forced in the initial
    # request but additional results are handled below via pagination.
    try:
        max_limit = search_parameters["limit"]
    except KeyError:
        logger.info("No `limit` parameter in search_parameters, using default 500.")
        max_limit = 500

    if max_limit > 500:
        search_parameters["limit"] = 500

    # UP42 API can query multiple collections of the same host at once.
    if self.data_products is None:
        self.data_products = self.get_data_products(basic=True)  # type: ignore
    hosts = [
        v["host"]
        for v in self.data_products.values()  # type: ignore
        if v["collection"] in search_parameters["collections"]
    ]
    if not hosts:
        raise ValueError(
            f"Selected collections {search_parameters['collections']} are not valid. See "
            f"catalog.get_collections."
        )
    if len(set(hosts)) > 1:
        raise ValueError(
            "Only collections with the same host can be searched at the same time. Please adjust the "
            "collections in the search_parameters!"
        )
    host = hosts[0]

    url = f"{self.auth._endpoint()}/catalog/hosts/{host}/stac/search"
    response_json: dict = self.auth._request("POST", url, search_parameters)
    features = response_json["features"]

    # Search results with more than 500 items are given as 50-per-page additional pages.
    while len(features) < max_limit:
        pagination_exhausted = len(response_json["links"]) == 1
        if pagination_exhausted:
            break
        next_page_url = response_json["links"][1]["href"]
        response_json = self.auth._request("POST", next_page_url, search_parameters)
        features += response_json["features"]

    features = features[:max_limit]
    if not features:
        df = GeoDataFrame(columns=["geometry"], geometry="geometry")
    else:
        df = GeoDataFrame.from_features(
            FeatureCollection(features=features), crs="EPSG:4326"
        )

    logger.info(f"{df.shape[0]} results returned.")
    if as_dataframe:
        return df
    else:
        return df.__geo_interface__