Testing (Experimental)

Experimental

The testing functionality in this module is experimental. It does not support all data types and port types, and only works with Table._to_pandas(). Expect potential API changes.

class knime.extension.testing.TestingConfigurationContext

This is a mock implementation of a configuration context for testing. In unit tests, create an instance of the TestConfigurationContext and pass it to your node when you test configure

Examples

>>> import knime.extension as knext
... import knime.extension.testing as ktest
...
... # set up test input schema
... schema = knext.Schema.from_columns(
...     [
...         knext.Column(knext.int64(), "Integers"),
...         knext.Column(knext.double(), "Doubles"),
...     ]
... )
...
... # assume TestNode is your Python node with one input and one output table
... node = TestNode()
... node.column = "Test" # configure parameter "column" to the value "Test"
...
... # create testing configuration context
... config_context = ktest.TestingConfigurationContext()
...
... # configure node, passing the testing configuration context
... output_schema = node.configure(config_context, input_schema)
...
... # TODO: check that the output schema matches your expectations

method __init__() → None

property flow_variables → Dict[str, Any]

The flow variables coming in from KNIME as a dictionary with string keys.

Notes

The dictionary can be edited and supports flow variables of the following types:

• bool
• list[bool]
• float
• list[float]
• int
• list[int]
• str
• list[str]

method get_credential_names()

method get_credentials(identifier: str)

method set_warning(message: str) → None

class knime.extension.testing.TestingExecutionContext

This is a mock implementation of an execution context for testing. In unit tests, create an instance of the TestExecutionContext and pass it to your node when testing execute.

Examples

>>> import knime.extension as knext
... import knime.extension.testing as ktest
...
... # set up test input
... input_df = pd.DataFrame({"Test": [1, 2, 3, 4]})
... input = knext.Table.from_pandas(input_df)
...
... # assume TestNode is your Python node with one input and one output table
... node = TestNode()
... node.column = "Test" # configure parameter "column" to the value "Test"
...
... # create testing execution context
... exec_context = ktest.TestingExecutionContext()
...
... # execute node, passing the testing execution context
... output = node.execute(exec_context, input)
... output_df = output.to_pandas()
...
... # TODO: check that the output DataFrame matches your expectations

method __init__() → None

property flow_variables → Dict[str, Any]

The flow variables coming in from KNIME as a dictionary with string keys.

Notes

The dictionary can be edited and supports flow variables of the following types:

• bool
• list[bool]
• float
• list[float]
• int
• list[int]
• str
• list[str]

method get_credential_names()

method get_credentials(identifier: str)

method get_knime_home_dir() → str

method get_workflow_data_area_dir() → str

method get_workflow_temp_dir() → str

method is_canceled() → bool

method set_progress(progress: float, message: str = None)

method set_warning(message: str) → None

function knime.extension.testing.register_extension(plugin_xml: str, value_factory_to_type_name_map: dict = None)

Registers the Python extension types found in the given plugin.xml file and puts all modules with extension types on the Pythonpath.

Parameters

• plugin_xml : str — Provide the path to a plugin.xml that registers Python types at the extension point.
• value_factory_to_type_name_map : dict — An optional mapping from value factory to readable type names e.g. org.knime.chem.types.SmilesAdapterCellValueFactory -> Smiles