LTA’s Unit Tests

Unit tests for LTA.

Tests for the parser

Tests for lta.parser.

tests.unit.test_parser.test_correct_usage(capsys: _pytest.capture.CaptureFixture) → None: It has the correct usage (ie. structure).

tests.unit.test_parser.test_default_func() → None: It has the correct default function.

Tests for the run command function

Tests for the simple command function.

tests.unit.test_commands_run.test_prints_text(capsys: _pytest.capture.CaptureFixture) → None: It prints the text.

Tests for the pipeline dataclass

Unit tests for lta.helpers.pipeline.

Testing datahandling can be a challenge, particularly for tools designed to handle compler, “omics” data. At the moment, only the basic error handling is tested. If you can provide any contributions towards better unit tests for data science, please reach out!

tests.unit.test_helpers_pipeline.test_raise_NotDir(mocker: pytest_mock.plugin.MockerFixture, caplog: _pytest.capture.CaptureFixture) → None: It handles IsADirectoryErrors.

tests.unit.test_helpers_pipeline.test_raise_NotFound(caplog: _pytest.capture.CaptureFixture) → None: It handles FileNotFoundErrors.

tests.unit.test_helpers_pipeline.test_raise_Runtime(mocker: pytest_mock.plugin.MockerFixture, caplog: _pytest.capture.CaptureFixture) → None: It fails if a file is empty.

Tests for the FloatRange Custom Type

Unit tests for the lta.helpers.custom_types module.

tests.unit.test_helpers_custom_types.test_FloatRange_attributes() → None: It has a start and end value.

tests.unit.test_helpers_custom_types.test_FloatRange_contains() → None: It checks the range contains a value.

tests.unit.test_helpers_custom_types.test_FloatRange_iter() → None: It is iterable.

tests.unit.test_helpers_custom_types.test_FloatRange_repr(capsys: _pytest.capture.CaptureFixture) → None: It correctly prints the repr.