Package: datatrails-archivist
DataTrails Python ClientThe standard DataTrails Python Client.Please note that the canonical API for DataTrails is always the REST API
documented athttps://docs.datatrails.aiSupportThis package currently is tested against Python versions 3.8,3.9,3.10,3.11 and 3.12.The current default version is 3.8 - this means that this package will not
use any features specific to versions 3.9 and later.After End of Life of a particular Python version, support is offered on a best effort
basis. We may ask you to update your Python version to help solve the problem,
if it cannot be reasonably resolved in your current version.InstallationUse standard python pip utility:python3-mpipinstalldatatrails-archivistIf your version of python3 is too old an error of this type or similar will be emitted:NoteERROR: Could not find a version that satisfies the requirement datatrails-archivist (from versions: none)
ERROR: No matching distribution found for datatrails-archivistExampleYou can then use the examples code to create assets (see examples directory):"""Create an asset in DataTrails with User Token.

The module contains two functions: main and create_asset. Main function parses in
a url to the Archivist and credentials, which is a user authorization.
The main function would initialize an archivist connection using the url and
the credentials, called "arch", then call arch.assets.create() and the asset will be created.
"""fromosimportgetenvfromarchivist.archivistimportArchivistfromarchivist.proof_mechanismimportProofMechanismdefcreate_asset(arch):"""Create an asset using Archivist Connection.

    Args:
        arch: archivist connection.

    Returns:
        newasset: a new asset created.

    """attrs={"arc_display_name":"display_name",# Asset's display name in the user interface"arc_description":"display_description",# Asset's description in the user interface"arc_display_type":"display_type",# Arc_display_type is a free text field# allowing the creator of# an asset to specify the asset# type or class. Be careful when setting this:# assets are grouped by type and# sharing policies can be# configured to share assets based on# their arc_display_type.# So a mistake here can result in asset data being# under- or over-shared."some_custom_attribute":"value"# You can add any custom value as long as# it does not start with arc_}## Select the mechanism used to prove evidence for the asset.  If the selected proof# mechanism is not enabled for your tenant then an error will occur.# If unspecified then SIMPLE_HASH is used.props={"proof_mechanism":ProofMechanism.SIMPLE_HASH.name,}# The first argument are the properties of the asset# The second argument are the attributes of the asset# The third argument is wait for confirmation:#   If @confirm@ is True then this function will not#   return until the asset is confirmed on the blockchain and ready#   to accept events (or an error occurs)#returnarch.assets.create(props=props,attrs=attrs,confirm=True)# alternatively if some work can be done whilst the asset is confirmed then this call can be# replaced by a two-step alternative:# asset = arch.assets.create(props=props, attrs=attrs, confirm=False)# ... do something else here# and then wait for confirmation# self.arch.assets.wait_for_confirmation(asset['identity']))defmain():"""Main function of create asset.

    Parse in user input of url and client id/secrets and use them to
    create an example archivist connection and create an asset.

    """# client id and client secret is obtained from the appidp endpoint - see the# application registrations example code in examples/applications_registration.py## client id is an environment variable. client_secret is stored in a file in a# directory that has 0700 permissions. The location of this file is set in# the client_secret_filename environment variable.client_id=getenv("DATATRAILS_APPREG_CLIENT")client_secret_file=getenv("DATATRAILS_APPREG_SECRET_FILENAME")withopen(client_secret_file,mode="r",encoding="utf-8")astokenfile:client_secret=tokenfile.read().strip()# Initialize connection to Archivist. max_time is the time to wait for confirmation# of an asset or event creation - the default is 1200 seconds but one can optionally# specify a different value here particularly when creating assets on SIMPLE_HASH# as confirmation times are much shorter in this case.witharch=Archivist("https://app.datatrails.ai",(client_id,client_secret),max_time=300,)asarch:# Create a new assetasset=create_asset(arch)print("Asset",asset)if__name__=="__main__":main()NotebooksSome jupyter notebooks are available to exercise the examples code.
These examples can be downloaded from python.datatrails.ai and run in a local install
of jupyter notebook such as jupyterLabDesktop.Please consulthttps://python.datatrails.ai/notebooks.htmlfor details.File Story RunnerYou can run scenarios - a sequence of steps - from a python dictionary or from a yaml
or json file.PythonfromloggingimportgetLoggerfrompyaml_envimportparse_configfromsysimportexitassys_exitfromsysimportstdoutassys_stdoutfromarchivistimportaboutfromarchivist.archivistimportArchivistfromarchivist.parserimportcommon_parser,endpointLOGGER=getLogger(__name__)defrun(arch:Archivist,args):LOGGER.info("Using version%sof datatrails-archivist",about.__version__)LOGGER.info("Namespace%s",args.namespace)withopen(args.yamlfile,"r",encoding="utf-8")asy:arch.runner(parse_config(data=y)sys_exit(0)defmain():parser=common_parser("Executes the archivist runner from a yaml file")parser.add_argument("yamlfile",help="the yaml file describing the steps to conduct")args=parser.parse_args()arch=endpoint(args)run(arch,args)parser.print_help(sys_stdout)sys_exit(1)Command LineThis functionality is also available with the CLI toolarchivist_runner, which is bundled with version v0.10 onwards of thedatatrails-archivist.You can verify the installation by running the following:archivist_runner-hWhich will show you the available options when usingarchivist_runner.To use thearchivist_runnercommand you will need the following:A Client ID and Client Secret by creating anApp RegistrationThe YAML file with the operations you wish to runThe URL of your DataTrails instance, this is typicallyhttps://app.datatrails.aiExample usage:archivist_runner\-uhttps://app.datatrails.ai\--client-id<your-client-id>\--client-secret<your-client-secret>\functests/test_resources/richness_story.yamlExample Yaml SnippetThis is an example of creating an asset and creating an event for that asset. The
yaml file consists of a list of steps.Each step consists of control parameters (specified in the ‘step’ dictionary) and
the yaml representation of the request body for an asset or event.The confirm: field is a control variable for the PythonSDK that ensures that the
asset or event is confirmed before returning.NoteThe name of the asset is important. The value of the name is carried forward for
every operation - in this case the name of the asset is ‘radiation bag 1’.Arguments to the archivist are usually strings - in this example radioactive is
‘true’ which archivist will treat as a boolean.---# Demonstration of applying a Richness compliance policy to an asset that undergoes# events that may or may not make the asset compliant or non-compliant.## The operation field is a string that represents the method bound to an endpoint and# the args and kwargs correspond to the arguments to such a method.## NB the assets and events endpoints require all values to be strings. Other values may# be of the correct type such as confirm which is a boolean.#steps:# note the values to the assets.create method are string representations of boolean# and numbers-step:action:ASSETS_CREATEdescription:Create an empty radiation bag with id 1.asset_label:radiation bag 1behaviours:-RecordEvidenceattributes:arc_display_name:radiation bag 1radioactive:"true"radiation_level:"0"weight:"0"confirm:true# setup the radiation bags to have a varing amount of radiactive waste# note the values to the events.create method are string representations of boolean# and numbers-step:action:EVENTS_CREATEdescription:Create Event adding 3 rads of radiation to bag 1, increasing its weight by 1kg.asset_label:radiation bag 1operation:Recordbehaviour:RecordEvidenceevent_attributes:arc_description:add waste to bagarc_evidence:see attached conformance reportconformance_report:blobs/e2a1d16c-03cd-45a1-8cd0-690831df1273asset_attributes:radiation_level:"3"weight:"1"confirm:trueLoggingFollows the Django model as described here:https://docs.djangoproject.com/en/3.2/topics/logging/The base logger for this package is rooted at “archivist” with subloggers for each endpoint:Notearchivist.archivistsublogger for archivist submodulearchivist.assetssublogger for assets submoduleand for other endpoints.Logging is configured by either defining a root logger with suitable handlers, formatters etc. or
by using dictionary configuration as described here:https://docs.python.org/3/library/logging.config.html#logging-config-dictschemaA recommended minimum configuration would be:importlogginglogging.dictConfig({"version":1,"disable_existing_loggers":False,"handlers":{"console":{"class":"logging.StreamHandler",},},"root":{"handlers":["console"],"level":"INFO",},})For convenience this has been encapsulated in a convenience functionset_loggerwhich should be called before anything else:fromarchivist.loggerimportset_loggerfromarchivist.archivistimportArchivistset_logger("DEBUG")client_id=getenv("DATATRAILS_APPREG_CLIENT")client_secret_file=getenv("DATATRAILS_APPREG_SECRET_FILENAME")withopen(client_secret_file,mode="r",encoding="utf-8")astokenfile:client_secret=tokenfile.read().strip()arch=Archivist("https://app.datatrails.ai",(client_id,client_secret),max_time=300,)DevelopmentFor instructions on contributing to the DataTrails SDK see DEVELOPMENT.md.