The Vector Service provides access to vector products and the features contained therein from Descartes Labs.

Vector

The Vector API can be found as Python package descarteslabs.vector.

class Table(table_parameters: Union[dict, str])

A class for creating and interacting with vector products.

add(feature_collection: Union[dict, descarteslabs.vector.vector.FeatureCollection]) → descarteslabs.vector.vector.FeatureCollection

Add a feature collection to this table.

Parameters:feature_collection (Union[dict, geojson.FeatureCollection, FeatureCollection]) – Collection of features to add to this table. Returns:feature_collection – Added features. Note that this will differ from the input in that the this will have feature IDs. Return type:FeatureCollection

static create(product_id, *args, **kwargs) → descarteslabs.vector.vector.Table

Create a vector product.

Parameters:

• product_id (str) – ID of the vector product.
• name (str) – Name of the vector product.
• description (str, optional) – Description of the vector product.
• tags (list of str, optional) – A list of tags to associate with the vector product.
• readers (list of str, optional) – A list of vector product readers. Can take the form “user:{namespace}”, “group:{group}”, “org:{org}”, or “email:{email}”.
• writers (list of str, optional) – A list of vector product writers. Can take the form “user:{namespace}”, “group:{group}”, “org:{org}”, or “email:{email}”.
• owners (list of str, optional) – A list of vector product owners. Can take the form “user:{namespace}”, “group:{group}”, “org:{org}”, or “email:{email}”.

Returns:

table – Table instace representing new product

Return type:

Table

delete()

Delete this vector product.

This function will disable all subsequent non-static method calls.

delete_feature(feature_id: str)

Delete a feature in a vector product.

Parameters:feature_id (str) – ID of the feature.

description()

Return the description of the table.

Returns:description – Table Description Return type:str

features(aoi: Union[descarteslabs.core.common.geo.geocontext.GeoContext, dict, shapely.geometry.base.BaseGeometry, None] = None, filter: Optional[descarteslabs.core.common.property_filtering.filtering.Properties] = None) → descarteslabs.vector.vector.FeatureSearch

Return a filterable FeatureSearch object.

Parameters:

• aoi (Optional[Union[dl.geo.GeoContext, dict, shapely.geometry.base.BaseGeometry]]) – Optional AOI object on which to filter features.
• filter (Optional[Properties]) – Optional property filte.

Returns:

fs – Filteratble object

Return type:

FeatureSearch

static get(product_id: str) → descarteslabs.vector.vector.Table

Get a Table instance associated with a product id. Raise an exception if this product_id doesn’t exit.

Parameters:product_id (str) – ID of product Returns:table – Table instance for the product ID. Return type:Table

get_feature(feature_id: str) → dict

Get a specific feature from this Table instance.

Parameters:

• feature_id (str) – Feature ID for which we would like the feature
• Retruns –
• ------- –
• dict – A GeoJSON Feature.

static list(tags: Optional[List[str]] = None) → List[descarteslabs.vector.vector.Table]

List available vector products.

Parameters:tags (list of str) – Optional list of tags a table must have to be returned. Returns:products – List of table instances. Return type:list of Table

name()

Return the name of the table.

Returns:name – Table name Return type:str

tid()

Return the ID of the table.

Returns:tid – Table ID Return type:str

update(*args, **kwargs)

Update this vector product.

Parameters:

• name (str) – New name of the vector product.
• description (str, optional) – New Description of the vector product.
• tags (list of str, optional) – New list of tags to associate with the vector product.
• readers (list of str, optional) – New list of vector product readers. Can take the form “user:{namespace}”, “group:{group}”, “org:{org}”, or “email:{email}”.
• writers (list of str, optional) – New list of vector product writers. Can take the form “user:{namespace}”, “group:{group}”, “org:{org}”, or “email:{email}”.
• owners (list of str, optional) – New list of vector product owners. Can take the form “user:{namespace}”, “group:{group}”, “org:{org}”, or “email:{email}”.

update_feature(feature_id: str, feature: dict) → dict

Update a feature in a vector product.

Parameters:

• feature_id (str) – ID of the feature.
• feature (dict) – The GeoJSON Feature to replace the feature with.

Returns:

A GeoJSON feature.

Return type:

dict

visualize(name: str, map: ipyleaflet.leaflet.Map, property_filter: Optional[descarteslabs.core.common.property_filtering.filtering.Properties] = None, include_properties: Optional[List[str]] = None, vector_tile_layer_styles: Optional[dict] = None) → ipyleaflet.leaflet.TileLayer

Visualize this Table as an ipyleaflet vector tile layer.

Parameters:

• name (str) – Name to give to the ipyleaflet vector tile layer.
• map (ipyleaflet.leaflet.Map) – Map to which to add the layer
• property_filter (Properties, optional) – Property filter to apply to the vector tiles.
• include_properties (list of str, optional) – Properties to include in the vector tiles. These can be used for styling.
• vector_tile_layer_styles (dict, optional) – Vector tile styles to apply. See https://ipyleaflet.readthedocs.io/en/latest/layers/vector_tile.html for more details.

Returns:

Vector tile layer that can be added to an ipyleaflet map.

Return type:

ipyleaflet.VectorTileLayer

class FeatureCollection(feature_collection: Union[dict, geojson.feature.FeatureCollection], parent_table: descarteslabs.vector.vector.Table)

A class for creating and interacting with collections of features.

features() → List[descarteslabs.vector.vector.Feature]

Return the feature list.

Returns:features – Contained Features Return type:list of Feature

features_list() → List[dict]

Return the feature list as GeoJSON features.

Returns:features – Contained GeoJSON features Return type:list of dict

filter(filter_func: Callable[[descarteslabs.vector.vector.Feature], bool])

Create a new FeatureCollection by filtering this one.

Note this filtering is performed on data that have already been pulled from the server. Where possible filtering should be performed with a FeatureSearch instance.

Parameters:filter_func (Callable[[Feature], bool]) – Preciate for selecting features. Returns:filtered – New FeatureCollection instance derived from filtering this one. Return type:FeatureCollection

get_feature(feature_id: str)

Get a specific feature from this FeatureCollection instance.

This call requires that a feature collection was generated with feature-IDs. This occurs, e.g., when the FeatureCollection instance is generated with Table.query.

Parameters:feature_id (str) – Feature ID for which we would like the feature Returns:A GeoJSON Feature. Return type:dict

class Feature(parameters: dict, parent_table: descarteslabs.vector.vector.Table)

delete()

Delete this feature.

geometry() → dict

Get geometry.

Returns:geometry – Feature geometry Return type:dict

properties() → dict

Get properties.

Returns:properties – Feature properties Return type:dict

set_properties(properties: dict)

Set properties for this Feature.

Parameters:properties (dict) – New properties for this feature

update()

Update this feature.

uuid() → dict

Get UUID.

Returns:uuid – Feature UUID Return type:uuid

class FeatureSearch(parent_table: str, aoi: Union[descarteslabs.core.common.geo.geocontext.GeoContext, dict, shapely.geometry.base.BaseGeometry, None] = None, filter: Optional[descarteslabs.core.common.property_filtering.filtering.Properties] = None)

A class for searching and filtering through vector features

collect() → descarteslabs.vector.vector.FeatureCollection

Return a FeatureCollection with the selected items.

Returns:fc – Selected features as a FeatureCollection Return type:FeatureCollection

filter(filter: descarteslabs.core.common.property_filtering.filtering.Properties) → descarteslabs.vector.vector.FeatureSearch

Create a new FeatureSearch instance that downselects to features that are selected by the filter.

Parameters:filter (descarteslabs.common.Properties) – AOI used to filter features by intersection Returns:feature_search – New FeatureSearch instance that downselects to features in the AOI. Return type:FeatureSearch

intersects(aoi: Union[descarteslabs.core.common.geo.geocontext.GeoContext, dict, shapely.geometry.base.BaseGeometry]) → descarteslabs.vector.vector.FeatureSearch

Create a new FeatureSearch instance that downselects to features that intersect the given AOI.

Parameters:aoi (descarteslabs.geo.GeoContext) – AOI used to filter features by intersection Returns:feature_search – New FeatureSearch instance that downselects to features in the AOI. Return type:FeatureSearch