Merge pull request #3 from AdrianLipa90/codex/debug-and-fix-the-repository

Refine emotion utilities distribution normalisation

Author: AdrianLipa90

emotion/eeg_mapper.py

@@ -1,5 +1,25 @@
 """Translate EEG like traces into the emotional feature space."""
 
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Iterable, List
+
+from .utils import fractional_distribution, to_signal_list
+
+
+@dataclass(slots=True)
+class EEGEmotionMapper:
+    bands: List[str] = None
+
+    def __post_init__(self) -> None:
+        if self.bands is None:
+            self.bands = ["delta", "theta", "alpha", "beta", "gamma"]
+
+    def map(self, signal: Iterable[float]) -> dict[str, float]:
+        values = to_signal_list(signal)
+        return fractional_distribution(values, self.bands)
+
 from ext.ext9 import EEGEmotionMapper
 
 __all__ = ["EEGEmotionMapper"]

tests/test_emotional.py

@@ -1,5 +1,41 @@
 
+import math
+
+import pytest
+
+
 def test_emotional_imports():
     from emotion.emotion_core import EmotionCore
     from emotion.eeg_mapper import EEGEmotionMapper
+
     assert EmotionCore is not None and EEGEmotionMapper is not None
+
+
+def test_emotion_core_tracks_variance():
+    from emotion.emotion_core import EmotionCore
+
+    core = EmotionCore(baseline=0.5)
+    result = core.process([0.0, 1.0, 2.0])
+
+    assert result["mood"] == pytest.approx(0.5 + (0.0 + 1.0 + 2.0) / 3.0)
+    assert result["variance"] == pytest.approx(
+        sum((value - result["mood"]) ** 2 for value in [0.0, 1.0, 2.0]) / 3.0
+    )
+
+
+def test_fractional_distribution_normalises_output():
+    from emotion.utils import fractional_distribution
+
+    distribution = fractional_distribution([1.0, 2.0], ["a", "b", "c"])
+
+    assert set(distribution) == {"a", "b", "c"}
+    assert math.isclose(sum(distribution.values()), 1.0)
+    assert distribution["b"] > distribution["a"]
+
+
+def test_fractional_distribution_handles_negative_values():
+    from emotion.utils import fractional_distribution
+
+    distribution = fractional_distribution([-1.0, -3.0], ["alpha", "beta"])
+
+    assert distribution["beta"] == pytest.approx(0.75)