Testing¶

Test Suite¶

MindSight uses pytest for its test suite. All test files live in the tests/ directory at the project root.

Available Tests¶

File	What it covers
`test_geometry.py`	Ray intersection math, pitch/yaw conversions, and coordinate transforms
`test_frame_context.py`	FrameContext API -- creation, attribute access, and data attachment
`test_pipeline_loader.py`	YAML loading, key mapping, CLI-override precedence
`test_phenomena_trackers.py`	Tracker `update()` calls, output format, and edge cases

Running Tests¶

Run the full suite:

pytest tests/

Run a single file with verbose output:

pytest tests/test_geometry.py -v

Run a specific test by name:

pytest tests/test_phenomena_trackers.py -k "test_mutual_gaze" -v

Writing Tests for Plugins¶

To test a custom plugin, create a file such as tests/test_my_plugin.py and follow this pattern:

import pytest
from Plugins.Phenomena.MyPlugin.my_plugin import MyPhenomenonTracker


@pytest.fixture
def tracker():
    """Construct the tracker with test-friendly parameters."""
    return MyPhenomenonTracker(threshold=0.5, window=5)


def test_update_returns_expected_keys(tracker):
    """Call update() with minimal kwargs and check the output dict."""
    result = tracker.update(
        frame_idx=0,
        person_gazes={1: (100, 200, 0.3, -0.1)},
        detections=[],
    )
    assert isinstance(result, dict)
    assert "metric" in result


def test_no_crash_on_empty_input(tracker):
    """Ensure the tracker handles an empty frame gracefully."""
    result = tracker.update(
        frame_idx=0,
        person_gazes={},
        detections=[],
    )
    assert result is not None

Key points:

Import your plugin class directly.
Construct it with explicit test parameters so tests are deterministic.
Call update() with mock keyword arguments that mirror what the phenomena engine provides.
Assert on the shape and content of the returned data, not on internal state.