Measurement Plug-In Code Generator for Python
Project description
Measurement Plug-In SDK Generator for Python
Introduction
Measurement Plug-In SDK Generator for Python (ni-measurement-plugin-sdk-generator
) is a Python package containing tools for generating reusable measurement plug-ins and clients.
ni-measurement-plugin-generator
is a command for generating measurement plug-ins using gRPC services.ni-measurement-plugin-client-generator
is a command for generating plug-in clients to interact with the measurement plug-ins.
Dependencies
- Python >= 3.8 (3.9 recommended)
- mako >= 1.2.1, < 2.x
- click >= 8.1.3
Developing Measurements: Quick Start
For installation and usage, see Measurement Plug-In SDK Service for Python - Developing Measurements: Quick Start section.
Generating Measurement Clients: Quick Start
This section provides instructions to generate custom measurement clients in Python using Measurement Plug-In SDK for Python.
Installation
Make sure the system has the recommended Python version installed. Install Measurement Plug-In SDK for Python using pip.
REM Activate the required virtual environment if any.
pip install ni-measurement-plugin-sdk
Check if you have installed the expected version of Measurement Plug-In SDK for Python installed by running the below command:
pip show ni-measurement-plugin-sdk
Generating a Minimal Python Measurement Client
Run the ni-measurement-plugin-client-generator
tool.
-
To create measurement clients for specific measurements, use this command with optional arguments:
ni-measurement-plugin-client-generator --measurement-service-class "ni.examples.SampleMeasurement_Python" [--module-name "sample_measurement_client"] [--class-name "SampleMeasurementClient"] [--directory-out <new_path_for_created_files>]
-
--measurement-service-class
specifies the measurement service class for which the client is being generated. -
Optional arguments:
-
--module-name
and--class-name
define the module and class names of the generated client. If not specified, they are derived from the measurement service class name. -
--directory-out
specifies the output directory for the generated files. If not specified, files are placed in the current directory.
-
Note: When generating multiple measurement clients,
--module-name
and--class-name
are ignored and derived from the service class of each measurement. So, ensure that the measurement service class name adheres to proper naming conventions. -
-
To create measurement clients for all registered measurements, use this command:
ni-measurement-plugin-client-generator --all [--directory-out <new_path_for_created_files>]
-
To interactively create measurement clients for any registered measurements, use this command:
ni-measurement-plugin-client-generator --interactive
The generated client includes four APIs: measure
, stream_measure
, register_pin_map
, and cancel
. The usage of these APIs is discussed in the "Steps to Run/Debug the Measurement Client" section.
Note:
- The Measurement Plug-In Client is compatible with all datatypes supported by the Measurement Plug-In.
- The Double XY datatype is not supported for measurement configurations (inputs).
- For Enum datatypes, the generated enum class names will be the measurement parameter name suffixed with 'Enum'. For instance, if the measurement parameter name is 'Enum In', the generated enum in the client will be `EnumInEnum'.
- Ring control in LabVIEW measurements will be represented as numeric datatypes in the generated client.
Steps to Run/Debug the Measurement Client
-
Make sure the required measurement service is running before interacting with it via the client.
-
Use the client APIs from the "Generating a Minimal Python Measurement Client" section.
-
For non-streaming measurements, use the
measure
method.from sample_measurement_client import SampleMeasurementClient client = SampleMeasurementClient() outputs = client.measure() print(outputs)
-
For streaming measurements, use the
stream_measure
method.from sample_measurement_client import SampleMeasurementClient client = SampleMeasurementClient() outputs_itr = client.stream_measure() for index, outputs in enumerate(outputs_itr): print(f"outputs[{index}] = {outputs}")
-
If a measurement requires a pin map, it can be registered using the
register_pin_map
method. By default,sites
is set to [0].from sample_measurement_client import SampleMeasurementClient client = SampleMeasurementClient() client.register_pin_map(pin_map_path) outputs = client.measure() print(outputs)
- Alternatively, when calling a measurement service from another measurement, you can pass the first measurement's pin map context to the second measurement's pin map context through the
pin_map_context
property. Sites can also be provided through thesites
property.
from sample_measurement_client import SampleMeasurementClient client = SampleMeasurementClient() client.pin_map_context = available_pin_map_context client.sites = [0, 1] outputs = client.measure() print(outputs)
- Alternatively, when calling a measurement service from another measurement, you can pass the first measurement's pin map context to the second measurement's pin map context through the
-
Cancel an ongoing
measure
orstream_measure
call using thecancel
method.from concurrent.futures import ThreadPoolExecutor from sample_measurement_client import SampleMeasurementClient client = SampleMeasurementClient() with ThreadPoolExecutor() as executor: future = executor.submit(client.measure) client.cancel() outputs = future.result() # Raises grpc.RpcException with status code "CANCELLED"
-
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