azure-digitaltwins-core 1.0.0b1

Microsoft Azure Azure DigitalTwins Core Client Library for Python

Azure Azure Digital Twins Core client library for Python

This package contains an SDK for Azure Digital Twins API to provide access to the Azure Digital Twins service for managing twins, models, relationships, etc.

This package contains an isomorphic SDK for Azure Digital Twins API to provide access to the Azure Digital Twins service for managing twins, models, relationships, etc.

Getting started

Introduction

Azure Digital Twins is a developer platform for next-generation IoT solutions that lets you create, run, and manage digital representations of your business environment, securely and efficiently in the cloud. With Azure Digital Twins, creating live operational state representations is quick and cost-effective, and digital representations stay current with real-time data from IoT and other data sources. If you are new to Azure Digital Twins and would like to learn more about the platform, please make sure you check out the Azure Digital Twins official documentation page.

For an introduction on how to program against the Azure Digital Twins service, visit the coding tutorial page for an easy step-by-step guide. Visit this tutorial to learn how to interact with an Azure Digital Twin instance using a command-line client application. Finally, for a quick guide on how to build an end-to-end Azure Digital Twins solution that is driven by live data from your environment, make sure you check out this helpful guide.

The guides mentioned above can help you get started with key elements of Azure Digital Twins, such as creating Azure Digital Twins instances, models, twin graphs, etc. Use this samples guide below to familiarize yourself with the various APIs that help you program against Azure Digital Twins.

How to Install

Install [azure-digitaltwins-core][pypi_package_keys] and azure-identity with pip:

pip install azure-digitaltiwns-core azure-identity

azure-identity is used for Azure Active Directory authentication as demonstrated below.

How to use

Authentication, permission

To create a new digital twins client, you need the endpoint to an Azure Digital Twin instance and credentials. For the samples below, the AZURE_URL, AZURE_TENANT_ID, AZURE_CLIENT_ID, and AZURE_CLIENT_SECRET environment variables have to be set. The client requires an instance of TokenCredential or ServiceClientCredentials. In this samples, we illustrate how to use one derived class: DefaultAzureCredentials.

Note: In order to access the data plane for the Digital Twins service, the entity must be given permissions. To do this, use the Azure CLI command: az dt rbac assign-role --assignee '<user-email | application-id>' --role owner -n '<your-digital-twins-instance>'

DefaultAzureCredential supports different authentication mechanisms and determines the appropriate credential type based of the environment it is executing in. It attempts to use multiple credential types in an order until it finds a working credential.

Sample code

# DefaultAzureCredential supports different authentication mechanisms and determines the appropriate credential type based of the environment it is executing in.
# It attempts to use multiple credential types in an order until it finds a working credential.

# - AZURE_URL: The tenant ID in Azure Active Directory
url = os.getenv("AZURE_URL")

# DefaultAzureCredential expects the following three environment variables:
# - AZURE_TENANT_ID: The tenant ID in Azure Active Directory
# - AZURE_CLIENT_ID: The application (client) ID registered in the AAD tenant
# - AZURE_CLIENT_SECRET: The client secret for the registered application
credential = DefaultAzureCredential()
serviceClient = DigitalTwinsClient(url, credential)

Key concepts

Azure Digital Twins is an Azure IoT service that creates comprehensive models of the physical environment. It can create spatial intelligence graphs to model the relationships and interactions between people, spaces, and devices. You can learn more about Azure Digital Twins by visiting Azure Digital Twins Documentation.

Examples

You can explore the digital twins APIs (using the client library) using the samples project.

The samples project demonstrates the following:

• Instantiate the client
• Create, get, and decommission models
• Create, query, and delete a digital twin
• Get and update components for a digital twin
• Create, get, and delete relationships between digital twins
• Create, get, and delete event routes for digital twin
• Publish telemetry messages to a digital twin and digital twin component

Create, list, decommission, and delete models

Create models

Let's create models using the code below. You need to pass an array containing list of models.

temporary_component = {
    "@id": component_id,
    "@type": "Interface",
    "@context": "dtmi:dtdl:context2",
    "displayName": "Component1",
    "contents": [
    {
        "@type": "Property",
        "name": "ComponentProp1",
        "schema": "string"
    },
    {
        "@type": "Telemetry",
        "name": "ComponentTelemetry1",
        "schema": "integer"
    }
    ]
}

temporary_model = {
    "@id": model_id,
    "@type": "Interface",
    "@context": "dtmi:dtdl:context2",
    "displayName": "TempModel",
    "contents": [
    {
        "@type": "Property",
        "name": "Prop1",
        "schema": "string"
    },
    {
        "@type": "Component",
        "name": "Component1",
        "schema": component_id
    },
    {
        "@type": "Telemetry",
        "name": "Telemetry1",
        "schema": "integer"
    }
    ]
}

new_models = [temporary_component, temporary_model]
models = service_client.create_models(new_models)
print('Created Models:')
print(models)

List models

Using list_models to retrieve all created models

listed_models = service_client.list_models(model_id)
for model in listed_models:
    print(model + '\n')

Get model

Use get_model with model's unique identifier to get a specific model.

# Get a model
get_model = service_client.get_model(model_id)
print('Get Model:')
print(get_model)

Decommission model

To decommision a model, pass in a model Id for the model you want to decommision.

# Decommission a model
service_client.decommission_model(model_id)

Delete model

To delete a model, pass in a model Id for the model you want to delete.

# Delete a model
service_client.delete_model(model_id)

Create and delete digital twins

Create digital twins

For Creating Twin you will need to provide Id of a digital Twin such as my_twin and the application/json digital twin based on the model created earlier. You can look at sample application/json here.

digital_twin_id = 'digitalTwin-' + str(uuid.uuid4())
with open(r"dtdl\digital_twins_\buildingTwin.json") as f:
    dtdl_digital_twins_building_twin = json.load(f)

created_twin = service_client.upsert_digital_twin(digital_twin_id, dtdl_digital_twins_building_twin)
print('Created Digital Twin:')
print(created_twin)

Get a digital twin

Getting a digital twin is extremely easy.

get_twin = service_client.get_digital_twin(digital_twin_id)
print('Get Digital Twin:')
print(get_twin)

Query digital twins

Query the Azure Digital Twins instance for digital twins using the Azure Digital Twins Query Store lanaguage. Query calls support paging. Here's an example of how to query for digital twins and how to iterate over the results.

query_expression = 'SELECT * FROM digitaltwins'
query_result = service_client.query_twins(query_expression)
print('DigitalTwins:')
for twin in query_result:
    print("    -: {}".format(twin["$dtId"]))

Delete digital twins

Delete a digital twin simply by providing Id of a digital twin as below.

service_client.delete_digital_twin(digital_twin_id)

Get and update digital twin components

Update digital twin components

To update a component or in other words to replace, remove and/or add a component property or subproperty within Digital Twin, you would need Id of a digital twin, component name and application/json-patch+json operations to be performed on the specified digital twin's component. Here is the sample code on how to do it.

component_path = "Component1"
options = {
    "patchDocument": {
    "ComponentProp1": "value2"
    }
}
service_client.update_component(digital_twin_id, component_path, options)

Get digital twin components

Get a component by providing name of a component and Id of digital twin to which it belongs.

get_component = service_client.get_component(digital_twin_id, component_path)
print('Get Component:')
print(get_component)

Create and list digital twin relationships

Create digital twin relationships

upsert_relationship creates a relationship on a digital twin provided with Id of a digital twin, name of relationship such as "contains", Id of an relationship such as "FloorContainsRoom" and an application/json relationship to be created. Must contain property with key "$targetId" to specify the target of the relationship. Sample payloads for relationships can be found here.

with open(r"dtdl\relationships\hospitalRelationships.json") as f:
    dtdl_relationships = json.load(f)
for relationship in dtdl_relationships:
    service_client.upsert_relationship(
        relationship["$sourceId"],
        relationship["$relationshipId"],
        relationship
    )

List digital twin relationships

list_relationships and list_incoming_relationships lists all the relationships and all incoming relationships respectively of a digital twin.

relationships = service_client.list_relationships(digital_twint_id)
for relationship in relationships:
    print(relationship + '\n')

incoming_relationships = service_client.list_incoming_relationships(digital_twin_id)
for incoming_relationship in incoming_relationships:
    print(incoming_relationship + '\n')

Create, list, and delete event routes of digital twins

Create event routes

To create an event route, provide an Id of an event route such as "myEventRouteId" and event route data containing the endpoint and optional filter like the example shown below.

event_route_id = 'eventRoute-' + str(uuid.uuid4())
event_filter = "$eventType = 'DigitalTwinTelemetryMessages' or $eventType = 'DigitalTwinLifecycleNotification'"
service_client.upsert_event_route(
    event_route_id,
    event_hub_endpoint_name,
    **{"filter": event_filter}
)

For more information on the event route filter language, see the "how to manage routes" filter events documentation.

List event routes

List a specific event route given event route Id or all event routes setting options with list_event_routes.

event_routes = service_client.list_event_routes()
for event_route in event_routes:
    print(event_route + '\n')

Delete event routes

Delete an event route given event route Id.

service_client.delete_event_route(event_route_id)

Publish telemetry messages for a digital twin

To publish a telemetry message for a digital twin, you need to provide the digital twin Id, along with the payload on which telemetry that needs the update.

digita_twin_id = "<DIGITAL TWIN ID>"
telemetry_payload = '{"Telemetry1": 5}'
service_client.publish_telemetry(
    digita_twin_id,
    telemetry_payload
)

You can also publish a telemetry message for a specific component in a digital twin. In addition to the digital twin Id and payload, you need to specify the target component Id.

digita_twin_id = "<DIGITAL TWIN ID>"
component_path = "<COMPONENT_PATH>"
telemetry_payload = '{"Telemetry1": 5}'
service_client.publish_component_telemetry(
    digita_twin_id,
    component_path,
    telemetry_payload
)

Troubleshooting

Logging

This library uses the standard logging library for logging. Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.

Detailed DEBUG level logging, including request/response bodies and unredacted headers, can be enabled on a client with the logging_enable keyword argument:

Client level logging

import sys
import logging

# Create logger
logger = logging.getLogger('azure')
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler(stream=sys.stdout)
logger.addHandler(handler)

# Create service client and enable logging for all operations
service_client = DigitalTwinsClient(url, credential, logging_enable=True)

Per-operation level logging

import sys
import logging

# Create logger
logger = logging.getLogger('azure')
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler(stream=sys.stdout)
logger.addHandler(handler)

# Get model with logging enabled
model = service_client.get_model(model_id, logging_enable=True)

Optional Configuration

Optional keyword arguments can be passed in at the client and per-operation level. The azure-core reference documentation describes available configurations for retries, logging, transport protocols, and more.

Next steps

Provide Feedback

If you encounter bugs or have suggestions, please open an issue.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Release History

1.0.0b1 (2020-10-31)

• Initial Release