API Reference

Catalog

class Catalog(**kwargs)
get(catID, includeRelationships=False)

Retrieves the strip footprint WKT string given a cat ID.

Parameters:
  • catID (str) – The source catalog ID from the platform catalog.
  • includeRelationships (bool) – whether to include graph links to related objects. Default False.
Returns:

A dict object identical to the json representation of the catalog record

Return type:

record (dict)

get_address_coords(address)

Use the google geocoder to get latitude and longitude for an address string

Parameters:address – any address string
Returns:A tuple of (lat,lng)
get_data_location(catalog_id)

Find and return the S3 data location given a catalog_id.

Parameters:catalog_id – The catalog ID
Returns:A string containing the s3 location of the data associated with a catalog ID. Returns None if the catalog ID is not found, or if there is no data yet associated with it.
get_most_recent_images(results, types=[], sensors=[], N=1)

Return the most recent image

Parameters:
  • results – a catalog resultset, as returned from a search
  • types – array of types you want. optional.
  • sensors – array of sensornames. optional.
  • N – number of recent images to return. defaults to 1.
Returns:

single catalog item, or none if not found

get_strip_footprint_wkt(catID)

Retrieves the strip footprint WKT string given a cat ID.

Parameters:catID (str) – The source catalog ID from the platform catalog.
Returns:A POLYGON of coordinates.
Return type:footprint (str)
get_strip_metadata(catID)

Retrieves the strip catalog metadata given a cat ID.

Parameters:catID (str) – The source catalog ID from the platform catalog.
Returns:A metadata dictionary .

TODO: have this return a class object with interesting information exposed.

Return type:metadata (dict)
search(searchAreaWkt=None, filters=None, startDate=None, endDate=None, types=None)

Perform a catalog search

Parameters:
  • searchAreaWkt – WKT Polygon of area to search. Optional.
  • filters – Array of filters. Optional. Example:
  • [ – “(sensorPlatformName = ‘WORLDVIEW01’ OR sensorPlatformName =’QUICKBIRD02’)”, “cloudCover < 10”, “offNadirAngle < 10”
  • ]
  • startDate – string. Optional. Example: “2004-01-01T00:00:00.000Z”
  • endDate – string. Optional. Example: “2004-01-01T00:00:00.000Z”
  • types – Array of types to search for. Optional. Example (and default): [“Acquisition”]
Returns:

catalog search resultset

search_address(address, filters=None, startDate=None, endDate=None, types=None)

Perform a catalog search over an address string

Parameters:
  • address – any address string
  • filters – Array of filters. Optional. Example:
  • [ – “(sensorPlatformName = ‘WORLDVIEW01’ OR sensorPlatformName =’QUICKBIRD02’)”, “cloudCover < 10”, “offNadirAngle < 10”
  • ]
  • startDate – string. Optional. Example: “2004-01-01T00:00:00.000Z”
  • endDate – string. Optional. Example: “2004-01-01T00:00:00.000Z”
  • types – Array of types to search for. Optional. Example (and default): [“Acquisition”]
Returns:

catalog search resultset

search_point(lat, lng, filters=None, startDate=None, endDate=None, types=None, type=None)

Perform a catalog search over a specific point, specified by lat,lng

Parameters:
  • lat – latitude
  • lng – longitude
  • filters – Array of filters. Optional. Example:
  • [ – “(sensorPlatformName = ‘WORLDVIEW01’ OR sensorPlatformName =’QUICKBIRD02’)”, “cloudCover < 10”, “offNadirAngle < 10”
  • ]
  • startDate – string. Optional. Example: “2004-01-01T00:00:00.000Z”
  • endDate – string. Optional. Example: “2004-01-01T00:00:00.000Z”
  • types – Array of types to search for. Optional. Example (and default): [“Acquisition”]
Returns:

catalog search resultset

CatalogImage

CatalogImage is a wrapper class that returns the appropriate image class for the catalog ID. This description applies to all of the other RDA-based classes.

CatalogImages are also Dask arrays and support all of their properties and methods. The most commonly used ones are listed here. Some methods are overridden to preserve geospatial information but other work identically.

class CatalogImage

Creates an image instance matching the type of the Catalog ID.

Parameters:
  • catalogID (str) – The source catalog ID from the platform catalog.
  • proj (str) – Optional EPSG projection string for the image in the form of “EPSG:4326”
  • dtype (str) – The dtype for the returned image (only valid for Worldview). One of: “int8”, “int16”, “uint16”, “int32”, “float32”, “float64”
  • band_type (str) – The product spec / band type for the image returned (band_type=’MS’|’Pan’)
  • pansharpen (bool) – Whether or not to return a pansharpened image (defaults to False)
  • acomp (bool) – Perform atmospheric compensation on the image (defaults to False, i.e. Top of Atmosphere value)
  • gsd (float) – The Ground Sample Distance (GSD) of the image. Must be defined in the same projected units as the image projection.
  • dra (bool) – Perform Dynamic Range Adjustment (DRA) on the image. DRA will override the dtype and return int8 data.
affine

list – The image affine transformation

bounds

list – Spatial bounds of the image

metadata

dict – image metadata

ntiles

int – the number of tiles composing the image

nbytes

int – size of the image in bytes

proj

str – The image projection as EPSG string

Returns:An image instance - one of IdahoImage, WV02, WV03_VNIR, LandsatImage, IkonosImage
Return type:image (ndarray)
aoi(**kwargs)

Subsets the Image by the given bounds

Parameters:
  • bbox (list) – optional. A bounding box array [minx, miny, maxx, maxy]
  • wkt (str) – optional. A WKT geometry string
  • geojson (str) – optional. A GeoJSON geometry dictionary
Returns:

an image instance of the same type

Return type:

image

geotiff(**kwargs)

Creates a geotiff on the filesystem

Parameters:
  • path (str) – optional, path to write the geotiff file to, default is ./output.tif
  • proj (str) – optional, EPSG string of projection to reproject to
  • spec (str) – optional, if set to ‘rgb’, write out color-balanced 8-bit RGB tif
  • bands (list) – optional, list of bands to export. If spec=’rgb’ will default to RGB bands, otherwise will export all bands
Returns:

path the geotiff was written to

Return type:

str

iterwindows(count=64, window_shape=(256, 256))

Iterate over random windows of an image

Parameters:
  • count (int) – the number of the windows to generate. Defaults to 64, if None will continue to iterate over random windows until stopped.
  • window_shape (tuple) – The desired shape of each image as (height, width) in pixels.
Yields:

image – an image of the given shape and same type.

map_blocks(*args, **kwargs)

Queue a deferred function to run on each block of image

This is identical to Dask’s map_block functinos, but returns a GeoDaskImage to preserve the geospatial information.

Args: see dask.Array.map_blocks

Returns:a dask array with the function queued up to run when the image is read
Return type:GeoDaskImage
ndvi(**kwargs)

Calculates Normalized Difference Vegetation Index using NIR and Red of an image.

Returns: numpy array with ndvi values

ndwi()

Calculates Normalized Difference Water Index using Coastal and NIR2 bands for WV02, WV03. For Landsat8 and sentinel2 calculated by using Green and NIR bands.

Returns: numpy array of ndwi values

plot(spec='rgb', **kwargs)

Plot the image with MatplotLib

Plot sizing includes default borders and spacing. If the image is shown in Jupyter the outside whitespace will be automatically cropped to save size, resulting in a smaller sized image than expected.

Histogram options:
  • ‘equalize’: performs histogram equalization on the image.
  • ‘minmax’: stretch the pixel range to the minimum and maximum input pixel values. Equivalent to stretch=[0,100].
  • ‘match’: match the histogram to the Maps API imagery. Pass the additional keyword blm_source=’browse’ to match to the Browse Service (image thumbnail) instead.
  • ‘ignore’: Skip dynamic range adjustment, in the event the image is already correctly balanced and the values are in the correct range.

Gamma values greater than 1 will brighten the image midtones, values less than 1 will darken the midtones.

Plots generated with the histogram options of ‘match’ and ‘equalize’ can be combined with the stretch and gamma options. The stretch and gamma adjustments will be applied after the histogram adjustments.

Parameters:
  • w (float or int) – width of plot in inches at 72 dpi, default is 10
  • h (float or int) – height of plot in inches at 72 dpi, default is 10
  • title (str) – Title to use on the plot
  • fontsize (int) – Size of title font, default is 22. Size is measured in points.
  • bands (list) – bands to use for plotting, such as bands=[4,2,1]. Defaults to the image’s natural RGB bands. This option is useful for generating pseudocolor images when passed a list of three bands. If only a single band is provided, a colormapped plot will be generated instead.
  • cmap (str) – MatPlotLib colormap name to use for single band images. Default is colormap=’Grey_R’.
  • histogram (str) – either ‘equalize’, ‘minmax’, ‘match’, or ignore
  • stretch (list) – stretch the histogram between two percentile values, default is [2,98]
  • gamma (float) – adjust image gamma, default is 1.0
preview(**kwargs)

Show a slippy map preview of the image. Requires iPython.

Parameters:
  • image (image) – image object to display
  • zoom (int) – zoom level to intialize the map, default is 16
  • center (list) – center coordinates to initialize the map, defaults to center of image
  • bands (list) – bands of image to display, defaults to the image’s default RGB bands
pxbounds(geom, clip=False)

Returns the bounds of a geometry object in pixel coordinates

Parameters:
  • geom – Shapely geometry object or GeoJSON as Python dictionary or WKT string
  • clip (bool) – Clip the bounds to the min/max extent of the image
Returns:

bounds in pixels [min x, min y, max x, max y] clipped to image bounds

Return type:

list

randwindow(window_shape)

Get a random window of a given shape from within an image

Parameters:window_shape (tuple) – The desired shape of the returned image as (height, width) in pixels.
Returns:a new image object of the specified shape and same type
Return type:image
read(bands=None, **kwargs)

Reads data from a dask array and returns the computed ndarray matching the given bands

Parameters:bands (list) – band indices to read from the image. Returns bands in the order specified in the list of bands.
Returns:a numpy array of image data
Return type:ndarray
rgb(**kwargs)

Convert the image to a 3 band RGB for plotting

This method shares the same arguments as plot(). It will perform visual adjustment on the image and prepare the data for plotting in MatplotLib. Values are converted to an appropriate precision and the axis order is changed to put the band axis last.

warp(dem=None, proj='EPSG:4326', **kwargs)

Delayed warp across an entire AOI or Image

Creates a new dask image by deferring calls to the warp_geometry on chunks

Parameters:
  • dem (ndarray) – optional. A DEM for warping to specific elevation planes
  • proj (str) – optional. An EPSG proj string to project the image data into (“EPSG:32612”)
Returns:

a warped image as deferred image array

Return type:

daskarray

window_at(geom, window_shape)

Return a subsetted window of a given size, centered on a geometry object

Useful for generating training sets from vector training data Will throw a ValueError if the window is not within the image bounds

Parameters:
  • geom (shapely,geometry) – Geometry to center the image on
  • window_shape (tuple) – The desired shape of the image as (height, width) in pixels.
Returns:

image object of same type

Return type:

image

window_cover(window_shape, pad=True)

Iterate over a grid of windows of a specified shape covering an image.

The image is divided into a grid of tiles of size window_shape. Each iteration returns the next window.

Parameters:
  • window_shape (tuple) – The desired shape of each image as (height, width) in pixels.
  • pad – (bool): Whether or not to pad edge cells. If False, cells that do not have the desired shape will not be returned. Defaults to True.
Yields:

image – image object of same type.

classmethod acomp_available(cat_id)

Checks to see if a CatalogID can be atmos. compensated or not.

Parameters:catalogID (str) – The catalog ID from the platform catalog.
Returns:Whether or not the image can be acomp’d
Return type:available (bool)
classmethod is_ordered(cat_id)

Checks to see if a CatalogID has been ordered or not.

Parameters:catalogID (str) – The catalog ID from the platform catalog.
Returns:Whether or not the image has been ordered
Return type:ordered (bool)

Base Image classes

IdahoImage

class IdahoImage

Image based on IDAHO virtual tiles

Like a CatalogImage, but takes an IDAHO ID when initialized. Band_type and pansharpen arguments are not supported because IDAHO multispectral and panchromatic images are stored separately.

Parameters:(str) – IDAHO ID

Example

>>> img = IdahoImage('87a5b5a7-5438-44bf-926a-c8c7bc153713')

LandsatImage

class LandsatImage

Dask based access to landsat image backed by rda Graphs.

TmsImage

class TmsImage

An image built from the DigitalGlobe Maps API TMS tiles

These are global mosiacs of imagery that can be an effective source for training Machine Learning algorithms or whenever high-resolution is needed. Since the Maps API is static, or changes less frequently, these images are best suited when there are no temporal requirements on an analysis.

Instead of an ID the zoom level to use can be specified (default is 22). Changing the zoom level will change the resolution of the image. Note that different image sources are used at different zoom levels.

Supports the basic methods shared by Catalog Images such as plot() and geotiff().

Parameters:
  • zoom (int) – (optional) Zoom level to use as the source if the image, default is 22
  • bbox (list) – (optional) Bounding box of AOI, if aoi() method is not used.
  • proj (str) – (optional) EPSG projection string to reproject to, native SRS is EPSG:3857

Example

>>> img = TmsImage(zoom=13, bbox=[-109.84, 43.19, -109.59, 43.34], proj='EPSG:4326')

DemImage

class DemImage

Image class for Digital Elevation Model (DEM) data from the NED/SRTM dataset.

This class has no Catalog IDs and is created by passing an AOI. It shares most of the same methods as CatalogImage objects.

Parameters:
  • aoi (list) – list of coordinate in BBOX format
  • proj (str) – (optional) EPSG string of projection reproject to. Native projection is “EPSG:4326” (WGS84)

Example

>>> dem = DemImage(aoi=[5.279, 60.358, 5.402, 60.419])

S3Image

Idaho

class Idaho(**kwargs)
create_leaflet_viewer(idaho_image_results, filename)

Create a leaflet viewer html file for viewing idaho images.

Parameters:
  • idaho_image_results (dict) – IDAHO image result set as returned from the catalog.
  • filename (str) – Where to save output html file.
describe_images(idaho_image_results)

Describe the result set of a catalog search for IDAHO images.

Parameters:idaho_image_results (dict) – Result set of catalog search.
Returns:
The full catalog-search response for IDAHO images
corresponding to the given catID.
Return type:results (json)
get_chip(coordinates, catid, chip_type='PAN', chip_format='TIF', filename='chip.tif')

Downloads a native resolution, orthorectified chip in tif format from a user-specified catalog id.

Parameters:
  • coordinates (list) – Rectangle coordinates in order West, South, East, North. West and East are longitudes, North and South are latitudes. The maximum chip size is (2048 pix)x(2048 pix)
  • catid (str) – The image catalog id.
  • chip_type (str) – ‘PAN’ (panchromatic), ‘MS’ (multispectral), ‘PS’ (pansharpened). ‘MS’ is 4 or 8 bands depending on sensor.
  • chip_format (str) – ‘TIF’ or ‘PNG’
  • filename (str) – Where to save chip.
Returns:

True if chip is successfully downloaded; else False.

get_images_by_catid(catid)

Retrieves the IDAHO image records associated with a given catid. :param catid: The source catalog ID from the platform catalog. :type catid: str

Returns:
The full catalog-search response for IDAHO images
within the catID.
Return type:results (json)
get_images_by_catid_and_aoi(catid, aoi_wkt)

Retrieves the IDAHO image records associated with a given catid. :param catid: The source catalog ID from the platform catalog. :type catid: str :param aoi_wkt: The well known text of the area of interest. :type aoi_wkt: str

Returns:
The full catalog-search response for IDAHO images
within the catID.
Return type:results (json)
get_tms_layers(catid, bands='4, 2, 1', gamma=1.3, highcutoff=0.98, lowcutoff=0.02, brightness=1.0, contrast=1.0)

Get list of urls and bounding boxes corrsponding to idaho images for a given catalog id.

Parameters:
  • catid (str) – Catalog id
  • bands (str) – Bands to display, separated by commas (0-7).
  • gamma (float) – gamma coefficient. This is for on-the-fly pansharpening.
  • highcutoff (float) – High cut off coefficient (0.0 to 1.0). This is for on-the-fly pansharpening.
  • lowcutoff (float) – Low cut off coefficient (0.0 to 1.0). This is for on-the-fly pansharpening.
  • brightness (float) – Brightness coefficient (0.0 to 1.0). This is for on-the-fly pansharpening.
  • contrast (float) – Contrast coefficient (0.0 to 1.0). This is for on-the-fly pansharpening.
Returns:

TMS urls. bboxes (list of tuples): Each tuple is (W, S, E, N) where (W,S,E,N) are the bounds of the corresponding idaho part.

Return type:

urls (list)

Ordering

class Ordering(**kwargs)
heartbeat()

Check the heartbeat of the ordering API

Args: None

Returns: True or False

order(image_catalog_ids, batch_size=100, callback=None)

Orders images from GBDX.

Parameters:
  • image_catalog_ids (str or list) – A single catalog id or a list of catalog ids.
  • batch_size (int) – The image_catalog_ids will be split into batches of batch_size. The ordering API max batch size is 100, if batch_size is greater than 100 it will be truncated.
  • callback (str) – A url to call when ordering is completed.
Returns:

If one batch, returns a string. If more

than one batch, returns a list of order ids, one for each batch.

Return type:

order_ids (str or list)

status(order_id)

Checks imagery order status. There can be more than one image per order and this function returns the status of all images within the order.

Parameters:order_id (str) – The id of the order placed.
Returns:List of dictionaries, one per image. Each dictionary consists of the keys ‘acquisition_id’, ‘location’ and ‘state’.

S3

class S3(**kwargs)
delete(location)

Delete content in bucket/prefix/location. Location can be a directory or a file (e.g., my_dir or my_dir/my_image.tif) If location is a directory, all files in the directory are deleted. If it is a file, then that file is deleted.

Parameters:location (str) – S3 location within prefix. Can be a directory or a file (e.g., my_dir or my_dir/my_image.tif).
download(location, local_dir='.')

Download content from bucket/prefix/location. Location can be a directory or a file (e.g., my_dir or my_dir/my_image.tif) If location is a directory, all files in the directory are downloaded. If it is a file, then that file is downloaded.

Parameters:
  • location (str) – S3 location within prefix.
  • local_dir (str) – Local directory where file(s) will be stored. Default is here.
upload(local_file, s3_path='')

Upload files to your DG S3 bucket/prefix.

Parameters:
  • local_file (str) – a path to a local file to upload
  • s3_path – a key (location) on s3 to upload the file to

Task

class Task(_Task__task_type, **kwargs)
set(**kwargs)

Set input values on task

Parameters:arbitrary_keys – values for the keys
Returns:None

Task Registry

class TaskRegistry(**kwargs)
delete(task_name)

Deletes a GBDX task.

Parameters:task_name (str) – Task name.
Returns:Response (str).
get_definition(task_name)

Gets definition of a registered GBDX task.

Parameters:task_name (str) – Task name.
Returns:Dictionary representing the task definition.
list()

Lists available and visible GBDX tasks.

Returns:List of tasks
register(task_json=None, json_filename=None)

Registers a new GBDX task.

Parameters:
  • task_json (dict) – Dictionary representing task definition.
  • json_filename (str) – A full path of a file with json representing the task definition.
  • one out of task_json and json_filename should be provided. (Only) –
Returns:

Response (str).

update(task_name, task_json)

Updates a GBDX task.

Parameters:
  • task_name (str) – Task name.
  • task_json (dict) – Dictionary representing updated task definition.
Returns:

Dictionary representing the updated task definition.

Vectors

class Vectors(**kwargs)
aggregate_query(searchAreaWkt, agg_def, query=None, start_date=None, end_date=None, count=10, index='vector-gbdx-alpha-catalog-v2-*')

Aggregates results of a query into buckets defined by the ‘agg_def’ parameter. The aggregations are represented by dicts containing a ‘name’ key and a ‘terms’ key holding a list of the aggregation buckets. Each bucket element is a dict containing a ‘term’ key containing the term used for this bucket, a ‘count’ key containing the count of items that match this bucket, and an ‘aggregations’ key containing any child aggregations.

Parameters:
  • searchAreaWkt (str) – wkt representation of the geometry
  • agg_def (str or AggregationDef) – the aggregation definitions
  • query (str) – a valid Elasticsearch query string to constrain the items going into the aggregation
  • start_date (str) – either an ISO-8601 date string or a ‘now’ expression (e.g. “now-6d” or just “now”)
  • end_date (str) – either an ISO-8601 date string or a ‘now’ expression (e.g. “now-6d” or just “now”)
  • count (int) – the number of buckets to include in the aggregations (the top N will be returned)
  • index (str) – the index (or alias or wildcard index expression) to run aggregations against, set to None for the entire set of vector indexes
Returns:

A (usually single-element) list of dict objects containing the aggregation results.

Return type:

results (list)

create(vectors)

Create a vectors in the vector service.

Parameters:vectors – A single geojson vector or a list of geojson vectors. Item_type and ingest_source are required.
Returns:IDs of the vectors created
Return type:(list)

Example

>>> vectors.create(
...     {
...         "type": "Feature",
...         "geometry": {
...             "type": "Point",
...             "coordinates": [1.0,1.0]
...         },
...         "properties": {
...             "text" : "item text",
...             "name" : "item name",
...             "item_type" : "type",
...             "ingest_source" : "source",
...             "attributes" : {
...                 "latitude" : 1,
...                 "institute_founded" : "2015-07-17",
...                 "mascot" : "moth"
...             }
...         }
...     }
... )
create_from_wkt(wkt, item_type, ingest_source, **attributes)

Create a single vector in the vector service

Parameters:
  • wkt (str) – wkt representation of the geometry
  • item_type (str) – item_type of the vector
  • ingest_source (str) – source of the vector
  • attributes – a set of key-value pairs of attributes
Returns:

string identifier of the vector created

Return type:

id (str)

get(ID, index='vector-web-s')

Retrieves a vector. Not usually necessary because searching is the best way to find & get stuff.

Parameters:
  • ID (str) – ID of the vector object
  • index (str) – Optional. Index the object lives in. defaults to ‘vector-web-s’
Returns:

A dict object identical to the json representation of the catalog record

Return type:

record (dict)

map(features=None, query=None, style={}, bbox=[-180, -90, 180, 90], zoom=10, api_key=None)

Renders a mapbox gl map from a vector service query

query(searchAreaWkt, query, count=100, ttl='5m', index='vector-gbdx-alpha-catalog-v2-*')

Perform a vector services query using the QUERY API (https://gbdxdocs.digitalglobe.com/docs/vs-query-list-vector-items-returns-default-fields)

Parameters:
  • searchAreaWkt – WKT Polygon of area to search
  • query – Elastic Search query
  • count – Maximum number of results to return
  • ttl – Amount of time for each temporary vector page to exist
Returns:

List of vector results

query_iteratively(searchAreaWkt, query, count=100, ttl='5m', index='vector-gbdx-alpha-catalog-v2-*')

Perform a vector services query using the QUERY API (https://gbdxdocs.digitalglobe.com/docs/vs-query-list-vector-items-returns-default-fields)

Parameters:
  • searchAreaWkt – WKT Polygon of area to search
  • query – Elastic Search query
  • count – Maximum number of results to return
  • ttl – Amount of time for each temporary vector page to exist
Returns:

generator of vector results

tilemap(query, style={}, bbox=[-180, -90, 180, 90], zoom=16, api_key=None, index='vector-user-provided', name='GBDX_Task_Output')

Renders a mapbox gl map from a vector service query

Workflows

class Workflow(tasks, **kwargs)
cancel()

Cancel a running workflow.

Parameters:None
Returns:None
execute()

Execute the workflow.

Parameters:None
Returns:Workflow_id
generate_workflow_description()

Generate workflow json for launching the workflow against the gbdx api

Parameters:None
Returns:json string
list_workflow_outputs()

Get a list of outputs from the workflow that are saved to S3. To get resolved locations call workflow status. :param None:

Returns:list
savedata(output, location=None)

Save output data from any task in this workflow to S3

Parameters:
  • output – Reference task output (e.g. task.outputs.output1).
  • location (optional) – Subfolder under which the output will be saved. It will be placed under the account directory in gbd-customer-data bucket: s3://gbd-customer-data/{account_id}/{location} Leave blank to save to: workflow_output/{workflow_id}/{task_name}/{port_name}
Returns:

None

stderr

Get stderr from all the tasks of a workflow.

Returns:tasks with their stderr
Return type:(list)

Example

>>> workflow.stderr
[
    {
        "id": "4488895771403082552",
        "taskType": "AOP_Strip_Processor",
        "name": "Task1",
        "stderr": "............"
    }
]
stdout

Get stdout from all the tasks of a workflow.

Returns:tasks with their stdout
Return type:(list)

Example

>>> workflow.stdout
[
    {
        "id": "4488895771403082552",
        "taskType": "AOP_Strip_Processor",
        "name": "Task1",
        "stdout": "............"
    }
]
task_ids

Get the task IDs of a running workflow

Parameters:None
Returns:List of task IDs