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Fill out the rest of the metadata test functionality and add publish batch/condition tests (#49)
* Add first few acceptance tests that run against the actual datastore service Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * Add tests for batch publishing Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * More work on metadata tests. Add batch publish tests. Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * Add to the metadata test. Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * Update metadata test to use all greenboxes and extensions. Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * Add tests for Publish Condition Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * Rename test file for consistency Signed-off-by: Michael Johansen <michael.johansen@emerson.com> * Address review feedback. Signed-off-by: Michael Johansen <michael.johansen@emerson.com> --------- Signed-off-by: Michael Johansen <michael.johansen@emerson.com>
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‎CONTRIBUTING.md‎

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@@ -40,6 +40,9 @@ can also install the Measurement Data Services installer on a test machine. If t
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is not running, these tests will fail. To run the acceptance tests, run this command:
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`poetry run pytest tests\acceptance`
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> Warning! Running the acceptance tests will publish data and metadata to the default global
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> MDS data store location in the Documents folder.
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# Publishing on PyPI
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‎tests/acceptance/schemas/extensions.toml‎

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id = "https://acceptancetests.com/custommetadata.schema.toml"
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[uut]
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u1 = "*"
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u2 = "*"
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[uut_instance]
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ui1 = "*"
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ui2 = "*"
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[operator]
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o1 = "*"
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o2 = "*"
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[test_station]
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ts1 = "*"
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ts2 = "*"
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[test_description]
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td1 = "*"
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td2 = "*"
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[software_item]
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sw1 = "*"
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sw2 = "*"
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[hardware_item]
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hw1 = "*"
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hw2 = "*"
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[test_result]
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tr1 = "*"
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tr2 = "*"
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[step]
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s1 = "*"
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s2 = "*"
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[test]
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t1 = "*"
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t2 = "*"

‎tests/acceptance/test_publish_condition_and_read_data.py‎

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"""Acceptance tests that publish various condition values then read the data back."""
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from ni.datastore.data import (
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DataStoreClient,
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Step,
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TestResult,
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)
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from nitypes.scalar import Scalar
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from nitypes.vector import Vector
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def test___publish_float_condition___read_data_returns_vector() -> None:
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "float condition")
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published_condition = data_store_client.publish_condition(
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condition_name="python float condition",
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type="Upper Limit",
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value=123.45,
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step_id=step_id,
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)
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# A published float will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert len(vector) == 1
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assert vector[0] == 123.45
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assert vector.units == ""
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def test___publish_integer_condition___read_data_returns_vector() -> None:
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "integer condition")
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published_condition = data_store_client.publish_condition(
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condition_name="python integer condition",
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type="Lower Limit",
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value=123,
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step_id=step_id,
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)
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# A published integer will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert len(vector) == 1
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assert vector[0] == 123
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assert vector.units == ""
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def test___publish_bool_condition___read_data_returns_vector() -> None:
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "bool condition")
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published_condition = data_store_client.publish_condition(
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condition_name="python bool condition",
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type="Flag",
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value=True,
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step_id=step_id,
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)
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# A published bool will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert len(vector) == 1
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assert vector[0] is True
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assert vector.units == ""
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def test___publish_str_condition___read_data_returns_vector() -> None:
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "str condition")
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published_condition = data_store_client.publish_condition(
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condition_name="python str condition",
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type="Environment",
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value="condition value",
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step_id=step_id,
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)
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# A published str will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert len(vector) == 1
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assert vector[0] == "condition value"
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assert vector.units == ""
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def test___publish_scalar_condition___read_data_returns_vector() -> None:
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "scalar condition")
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expected_scalar = Scalar(value=25, units="Volts")
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published_condition = data_store_client.publish_condition(
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condition_name="python scalar condition",
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type="Lower Limit",
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value=expected_scalar,
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step_id=step_id,
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)
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# A published Scalar will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert vector[0] == expected_scalar.value
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assert vector.units == expected_scalar.units
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def _create_step(data_store_client: DataStoreClient, datatype_string: str) -> str:
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test_result_name = f"python publish {datatype_string} acceptance test"
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test_result = TestResult(test_result_name=test_result_name)
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test_result_id = data_store_client.create_test_result(test_result)
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# Publish the waveform data
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step = Step(step_name=f"Initial step: {datatype_string}", test_result_id=test_result_id)
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step_id = data_store_client.create_step(step)
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return step_id

‎tests/acceptance/test_publish_condition_batch_and_read_data.py‎

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"""Acceptance tests that publish various batch condition values then read the data back."""
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from ni.datastore.data import (
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DataStoreClient,
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Step,
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TestResult,
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)
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from nitypes.vector import Vector
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def test___publish_batch_float_condition___read_data_returns_vector() -> None:
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expected_value = [1.0, 2.0, 3.0]
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "float condition batch")
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published_condition = data_store_client.publish_condition_batch(
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condition_name="python float condition batch",
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type="Upper Limits",
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values=expected_value,
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step_id=step_id,
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)
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# A batch published float will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert vector._values == expected_value
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assert vector.units == ""
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def test___publish_batch_integer_condition___read_data_returns_vector() -> None:
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expected_value = [5, 6, 7, 8]
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "integer condition batch")
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published_condition = data_store_client.publish_condition_batch(
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condition_name="python integer condition batch",
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type="Lower Limits",
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values=expected_value,
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step_id=step_id,
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)
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# A batch published integer will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert vector._values == expected_value
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assert vector.units == ""
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def test___publish_batch_bool_condition___read_data_returns_vector() -> None:
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expected_value = [True, False, True]
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "bool condition batch")
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published_condition = data_store_client.publish_condition_batch(
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condition_name="python bool condition batch",
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type="Flags",
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values=expected_value,
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step_id=step_id,
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)
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# A batch published bool will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert vector._values == expected_value
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assert vector.units == ""
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def test___publish_batch_str_condition___read_data_returns_vector() -> None:
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expected_value = ["one", "two", "three"]
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "str condition batch")
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published_condition = data_store_client.publish_condition_batch(
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condition_name="python str condition batch",
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type="Environments",
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values=expected_value,
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step_id=step_id,
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)
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# A published str will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert vector._values == expected_value
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assert vector.units == ""
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def test___publish_batch_vector_condition___read_data_returns_vector() -> None:
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with DataStoreClient() as data_store_client:
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step_id = _create_step(data_store_client, "scalar condition batch")
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expected_vector = Vector(values=[25, 50, 75], units="Amps")
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published_condition = data_store_client.publish_condition_batch(
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condition_name="python vector condition batch",
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type="Upper Limit",
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values=expected_vector,
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step_id=step_id,
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)
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# A batch published Vector will be read back as a Vector.
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vector = data_store_client.read_data(published_condition, expected_type=Vector)
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assert vector == expected_vector
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def _create_step(data_store_client: DataStoreClient, datatype_string: str) -> str:
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test_result_name = f"python publish {datatype_string} acceptance test"
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test_result = TestResult(test_result_name=test_result_name)
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test_result_id = data_store_client.create_test_result(test_result)
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# Publish the waveform data
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step = Step(step_name=f"Initial step: {datatype_string}", test_result_id=test_result_id)
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step_id = data_store_client.create_step(step)
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return step_id

‎tests/acceptance/test_publish_measurement_and_read_data.py‎

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import numpy as np
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from ni.datastore.data import (
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DataStoreClient,
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PublishedMeasurement,
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Step,
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TestResult,
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)
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step_id=step_id,
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)
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found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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# A published integer will be read back as a Vector.
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vector = data_store_client.read_data(found_measurement, expected_type=Vector)
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vector = data_store_client.read_data(published_measurement, expected_type=Vector)
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assert vector[0] == 123.45
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assert vector.units == ""
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step_id=step_id,
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)
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found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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# A published Scalar will be read back as a Vector.
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vector = data_store_client.read_data(found_measurement, expected_type=Vector)
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vector = data_store_client.read_data(published_measurement, expected_type=Vector)
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assert vector[0] == expected_scalar.value
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assert vector.units == expected_scalar.units
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step_id=step_id,
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)
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found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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xydata = data_store_client.read_data(found_measurement, expected_type=XYData)
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xydata = data_store_client.read_data(published_measurement, expected_type=XYData)
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assert xydata == expected_xydata
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step_id=step_id,
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)
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found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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spectrum = data_store_client.read_data(found_measurement, expected_type=Spectrum)
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spectrum = data_store_client.read_data(published_measurement, expected_type=Spectrum)
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assert spectrum == expected_spectrum
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step_id=step_id,
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)
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found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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waveform = data_store_client.read_data(found_measurement, expected_type=AnalogWaveform)
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waveform = data_store_client.read_data(published_measurement, expected_type=AnalogWaveform)
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assert waveform == expected_waveform
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step_id=step_id,
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)
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132-
found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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waveform = data_store_client.read_data(found_measurement, expected_type=DigitalWaveform)
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waveform = data_store_client.read_data(published_measurement, expected_type=DigitalWaveform)
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assert waveform == expected_waveform
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step_id=step_id,
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)
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found_measurement = _get_first_measurement(
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data_store_client, published_measurement.published_measurement_id
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)
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waveform = data_store_client.read_data(found_measurement, expected_type=ComplexWaveform)
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waveform = data_store_client.read_data(published_measurement, expected_type=ComplexWaveform)
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assert waveform == expected_waveform
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step = Step(step_name=f"Initial step: {datatype_string}", test_result_id=test_result_id)
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step_id = data_store_client.create_step(step)
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return step_id
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def _get_first_measurement(
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data_store_client: DataStoreClient, published_measurement_id: str
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) -> PublishedMeasurement:
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# Query for the measurement id and read/validate data
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published_measurements = data_store_client.query_measurements(
172-
odata_query=f"$filter=id eq {published_measurement_id}"
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)
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found_measurement = next(iter(published_measurements), None)
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assert found_measurement is not None
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return found_measurement

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