Add pytest and CI baseline

.gitattributes

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+* text=auto eol=crlf
+
+# Preserve CRLF bytes for files covered by the repository's line-ending review policy.
+.gitattributes -text
+.github/workflows/*.yml -text
+pytest.ini -text
+requirements-test.txt -text
+tests/*.py -text

.github/workflows/tests.yml

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+name: Tests
+
+on:
+    pull_request:
+    push:
+        branches:
+            - master
+    workflow_dispatch:
+
+permissions:
+    contents: read
+
+jobs:
+    pytest:
+        name: pytest (Python ${{ matrix.python-version }})
+        runs-on: ubuntu-latest
+        timeout-minutes: 10
+        strategy:
+            fail-fast: false
+            matrix:
+                python-version:
+                    - "3.10"
+                    - "3.13"
+        steps:
+            - name: Check out repository
+              uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683
+            - name: Set up Python
+              uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065
+              with:
+                  cache: pip
+                  cache-dependency-path: requirements-test.txt
+                  python-version: ${{ matrix.python-version }}
+            - name: Install test dependencies
+              run: python -m pip install --requirement requirements-test.txt
+            - name: Run tests
+              run: python -m pytest

.gitignore

@@ -5,6 +5,9 @@ desktop.ini
 .idea/
 .env
 .vscode
+.pytest_cache/
+.coverage
+htmlcov/
 
 # internal-only math assets (keep local, do not commit)
 src/assets_master_script/general_assets_internal.csv

pytest.ini

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+[pytest]
+addopts = -ra
+testpaths = tests

requirements-test.txt

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+numpy
+pandas
+pytest
+scipy

tests/test_create_input.py

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+from unittest.mock import Mock
+
+import pandas as pd
+import pytest
+
+from src import create_input
+
+
+ACR_COLUMNS = [
+    "rating_clips",
+    "math",
+    "pair_a",
+    "pair_b",
+    "trapping_clips",
+    "trapping_ans",
+]
+
+
+@pytest.fixture(autouse=True)
+def clear_condition_cache():
+    """
+    Clear the filename-to-condition cache around each test.
+
+    :return: None.
+    """
+    create_input.file_to_condition_map.clear()
+    yield
+    create_input.file_to_condition_map.clear()
+
+
+def test_conv_filename_to_condition_returns_sorted_groups():
+    """
+    Verify that named condition groups are returned in sorted order.
+
+    :return: None.
+    """
+    pattern = r"(?P<noise>[^_]+)_(?P<level>\d+)\.wav"
+
+    result = create_input.conv_filename_to_condition("white_10.wav", pattern)
+
+    assert list(result.items()) == [("level", "10"), ("noise", "white")]
+
+
+def test_conv_filename_to_condition_marks_nonmatching_filename():
+    """
+    Verify that a filename that does not match is marked as unknown.
+
+    :return: None.
+    """
+    pattern = r"(?P<noise>[^_]+)_(?P<level>\d+)\.wav"
+
+    result = create_input.conv_filename_to_condition("unexpected.wav", pattern)
+
+    assert result == {"Unknown": "NoCondition"}
+
+
+def test_validate_inputs_accepts_minimal_acr_columns():
+    """
+    Verify that the required ACR input columns pass validation.
+
+    :return: None.
+    """
+    cfg = {"number_of_gold_clips_per_session": "0"}
+    data = pd.DataFrame(columns=ACR_COLUMNS)
+
+    create_input.validate_inputs(cfg, data, "acr")
+
+
+def test_validate_inputs_rejects_missing_required_column():
+    """
+    Verify that validation identifies a missing required ACR column.
+
+    :return: None.
+    """
+    cfg = {"number_of_gold_clips_per_session": "0"}
+    data = pd.DataFrame(columns=[column for column in ACR_COLUMNS if column != "pair_b"])
+
+    with pytest.raises(AssertionError, match="pair_b"):
+        create_input.validate_inputs(cfg, data, "acr")
+
+
+def test_validate_inputs_requires_gold_columns_when_enabled():
+    """
+    Verify that enabling gold clips requires their input columns.
+
+    :return: None.
+    """
+    cfg = {"number_of_gold_clips_per_session": "1"}
+    data = pd.DataFrame(columns=ACR_COLUMNS)
+
+    with pytest.raises(AssertionError, match="gold_clips"):
+        create_input.validate_inputs(cfg, data, "acr")
+
+
+def test_create_input_for_mturk_dispatches_acr(monkeypatch):
+    """
+    Verify that ACR-compatible methods use the ACR input generator.
+
+    :param monkeypatch: Pytest fixture used to replace the input generator.
+    :return: None.
+    """
+    cfg = object()
+    data = object()
+    output_path = "output.csv"
+    create_acr = Mock(return_value=3)
+    monkeypatch.setattr(create_input, "create_input_for_acr", create_acr)
+
+    result = create_input.create_input_for_mturk(cfg, data, "p835", output_path)
+
+    assert result == 3
+    create_acr.assert_called_once_with(cfg, data, output_path, "p835")
+
+
+def test_create_input_for_mturk_dispatches_comparison_method(monkeypatch):
+    """
+    Verify that comparison methods use the DCR/CCR input generator.
+
+    :param monkeypatch: Pytest fixture used to replace the input generator.
+    :return: None.
+    """
+    cfg = object()
+    data = object()
+    output_path = "output.csv"
+    create_comparison = Mock(return_value=2)
+    monkeypatch.setattr(create_input, "create_input_for_dcrccr", create_comparison)
+
+    result = create_input.create_input_for_mturk(cfg, data, "ccr", output_path)
+
+    assert result == 2
+    create_comparison.assert_called_once_with(cfg, data, output_path)

tests/test_result_parser.py

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+from src.result_parser import outliers_modified_z_score, outliers_z_score
+
+
+def test_outliers_modified_z_score_removes_clear_outlier():
+    """
+    Verify that the modified z-score filter removes a clear outlier.
+
+    :return: None.
+    """
+    votes = [1, 2, 2, 2, 3, 100]
+
+    assert outliers_modified_z_score(votes) == [1, 2, 2, 2, 3]
+
+
+def test_outliers_z_score_preserves_constant_votes():
+    """
+    Verify that the z-score filter preserves votes with no variance.
+
+    :return: None.
+    """
+    votes = [3, 3, 3]
+
+    assert outliers_z_score(votes) == votes
+
+
+def test_outliers_z_score_uses_threshold():
+    """
+    Verify that the z-score cutoff distinguishes values around the threshold.
+
+    :return: None.
+    """
+    below_threshold = [10] * 10 + [1000]
+    above_threshold = [10] * 11 + [1000]
+
+    assert outliers_z_score(below_threshold) == below_threshold
+    assert outliers_z_score(above_threshold) == [10] * 11