chore: initialize git repo, add matplotlib dep, extend config

- Add .gitignore for Python/data/models
- Add matplotlib>=3.8.0 for eval plots
- Add PretrainConfig, FinetuneConfig, BalabitAdapterConfig, EvalConfig dataclasses

tests/test_scroll_generator.py

@@ -0,0 +1,50 @@
+"""Tests for scroll generator."""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import torch
+import pytest
+
+from ai_mouse.scroll.generator import generate_scroll
+from ai_mouse.scroll.models import ScrollCVAE
+
+
+@pytest.fixture
+def scroll_model_dir(tmp_path):
+    model = ScrollCVAE(seq_len=32)
+    torch.save(model.state_dict(), tmp_path / "scroll_model.pt")
+    config = {"seq_len": 32, "epochs": 100}
+    (tmp_path / "scroll_config.json").write_text(json.dumps(config))
+    return tmp_path
+
+
+class TestGenerateScroll:
+    def test_returns_list_of_dicts(self, scroll_model_dir):
+        result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
+        assert isinstance(result, list)
+        assert len(result) > 0
+        assert all("deltaY" in e and "t" in e and "deltaMode" in e for e in result)
+
+    def test_timestamps_monotonic(self, scroll_model_dir):
+        result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
+        times = [e["t"] for e in result]
+        for i in range(1, len(times)):
+            assert times[i] >= times[i - 1]
+
+    def test_total_scroll_approximately_matches_distance(self, scroll_model_dir):
+        result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
+        total = sum(e["deltaY"] for e in result)
+        # Should be within 30% of target distance (2000px)
+        assert abs(total - 2000) < 2000 * 0.4
+
+    def test_deltaY_are_integers(self, scroll_model_dir):
+        result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
+        assert all(isinstance(e["deltaY"], int) for e in result)
+
+    def test_direction_up(self, scroll_model_dir):
+        result = generate_scroll(3000, 1000, mode="target", model_dir=str(scroll_model_dir))
+        total = sum(e["deltaY"] for e in result)
+        # Negative total for scrolling up
+        assert total < 0