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Priyadarshini Piramanayagam
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Merge remote-tracking branch 'upstream/master' into users/priya/results-dataframe-utility
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‎CHANGELOG.md‎

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<!--next-version-placeholder-->
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## v2.3.0 (2025-03-07)
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### Feature
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* Add steps APIs to the testmonitor client ([#96](https://github.com/ni/nisystemlink-clients-python/issues/96)) ([`85a676d`](https://github.com/ni/nisystemlink-clients-python/commit/85a676d000b68233b03339a943834d1054fbf2ab))
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## v2.2.0 (2025-03-07)
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### Feature
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* Export products as dataframe ([#102](https://github.com/ni/nisystemlink-clients-python/issues/102)) ([`e5a41c0`](https://github.com/ni/nisystemlink-clients-python/commit/e5a41c0839cbd2412d613e78ac698f7000c32e99))
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## v2.1.0 (2025-03-06)
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### Feature

‎docs/api_reference/testmonitor.rst‎

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.. automethod:: update_results
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.. automethod:: delete_result
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.. automethod:: delete_results
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.. automethod:: create_steps
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.. automethod:: query_steps
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.. automethod:: query_step_values
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.. automethod:: delete_steps
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.. automethod:: delete_step
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.. automethod:: update_steps
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.. automethod:: get_steps
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.. automethod:: get_step
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.. automodule:: nisystemlink.clients.testmonitor.models
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:members:

‎docs/getting_started.rst‎

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~~~~~~~~
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The :class:`.TestMonitorClient` class is the primary entry point of the Test Monitor API
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used to interact with test results (Results).
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used to interact with test results (Results) and test steps (Steps).
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When constructing a :class:`.TestMonitorClient`, you can pass an
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:class:`.HttpConfiguration` (like one retrieved from the
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With a :class:`.TestMonitorClient` object, you can:
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* Create, update, query, and delete results
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* Create, update, query, and delete steps
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Examples
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~~~~~~~~
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Create, query, update, and delete some results
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.. literalinclude:: ../examples/result/results.py
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.. literalinclude:: ../examples/testmonitor/results.py
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:language: python
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:linenos:
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Create, update, query, and delete steps
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.. literalinclude:: ../examples/testmonitor/steps.py
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:language: python
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:linenos:

‎examples/testmonitor/steps.py‎

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from nisystemlink.clients.testmonitor import TestMonitorClient
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from nisystemlink.clients.testmonitor.models import (
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CreateResultRequest,
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CreateStepRequest,
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NamedValue,
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QueryStepsRequest,
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QueryStepValuesRequest,
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StepField,
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StepIdResultIdPair,
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UpdateStepRequest,
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)
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from nisystemlink.clients.testmonitor.models._status import Status
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from nisystemlink.clients.testmonitor.models._step_data import Measurement, StepData
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def create_test_result():
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"""Create example result on your server."""
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new_results = [
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CreateResultRequest(
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part_number="Example 123 AA",
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program_name="Example Name",
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host_name="Example Host",
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status=Status.PASSED(),
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keywords=["original keyword"],
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properties={"original property key": "yes"},
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)
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]
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create_response = client.create_results(new_results)
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return create_response
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# Server configuration is not required when used with Systemlink Client or run throught Jupyter on SLE
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server_configuration = None
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# # Example of setting up the server configuration to point to your instance of SystemLink Enterprise
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# server_configuration = HttpConfiguration(
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# server_uri="https://yourserver.yourcompany.com",
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# api_key="YourAPIKeyGeneratedFromSystemLink",
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# )
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client = TestMonitorClient(configuration=server_configuration)
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# create a result to attach the steps to
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create_response = create_test_result()
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# Create the step requests
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result_id = create_response.results[0].result_id
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step_requests = [
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CreateStepRequest(
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step_id="step1",
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name="step1",
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result_id=result_id,
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inputs=[
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NamedValue(name="Temperature", value="35"),
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NamedValue(name="Voltage", value="5"),
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],
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),
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CreateStepRequest(
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step_id="step2",
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name="step2",
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result_id=result_id,
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),
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]
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# Create the steps
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create_response = client.create_steps(steps=step_requests)
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created_steps = create_response.steps
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print(create_response)
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# You can query steps based on any field using DynamicLinq syntax.
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# These are just some representative examples.
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# Query based on result id
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query_response = client.query_steps(
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QueryStepsRequest(filter=f'resultId == "{result_id}"')
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)
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queried_steps = query_response.steps
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# query step name using query step values
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query_values_response = client.query_step_values(
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QueryStepValuesRequest(
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filter=f'resultId == "{result_id}"',
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field=StepField.NAME,
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)
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)
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# update the data of the step
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# extra properties of the measurements will be converted to string if not already a string
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update_response = client.update_steps(
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steps=[
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UpdateStepRequest(
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step_id=step.step_id,
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result_id=step.result_id,
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data=StepData(
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text="My output string",
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parameters=[
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Measurement(
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name="Temperature",
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status="Passed",
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measurement="35",
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lowLimit="30",
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highLimit="40",
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units="C",
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comparisonType="Numeric",
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spec_id="spec1",
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spec_info={
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"specKey": 10
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}, # will be converted to string as '{"specKey": 10}'
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)
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],
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),
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)
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for step in created_steps
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]
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)
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# delete all steps at once
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delete_response = client.delete_steps(
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steps=[
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StepIdResultIdPair(step_id=step.step_id, result_id=step.result_id)
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for step in queried_steps
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]
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)
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create_response = client.create_steps(steps=step_requests)
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created_steps = create_response.steps
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# delete steps one by one
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for step in created_steps:
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if step.step_id and step.result_id:
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client.delete_step(result_id=step.result_id, step_id=step.step_id)

‎nisystemlink/clients/product/utilities/__init__.py‎

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from ._dataframe_utilities import convert_products_to_dataframe
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from ._file_utilities import get_products_linked_to_file
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# flake8: noqa

‎nisystemlink/clients/product/utilities/_dataframe_utilities.py‎

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from typing import List
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import pandas as pd
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from nisystemlink.clients.product.models import Product
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from pandas import DataFrame
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def convert_products_to_dataframe(products: List[Product]) -> DataFrame:
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"""Converts a list of products into a normalized dataframe.
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Args:
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products (List[Product]): A list of products
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Returns:
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DataFrame:
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- A Pandas DataFrame containing the product data. The DataFrame would consist of all the
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fields in the input products.
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- A new column would be created for unique properties across all products. The property
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columns would be named in the format `properties.property_name`.
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"""
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products_dict_representation = [
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product.dict(exclude_none=True) for product in products
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]
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normalized_products_dataframe = pd.json_normalize(
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products_dict_representation, sep="."
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)
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return normalized_products_dataframe

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