"""exporter.py の単体テスト. 仕様 (SPEC_01_アーキテクチャ設計.md): - export_results: 単一結果を CSV 出力 - export_summary: バッチ結果を集約 CSV 出力 - ensure_output_dirs: output/figures/ と output/results/ を作成 テスト方針 (GUIDE_08_テスト方針.md): - pytest を使用 - tmp_path フィクスチャで一時ディレクトリを利用 """ import csv from pathlib import Path import numpy as np import pytest from src.export.exporter import ( SPATIAL_FIELDNAMES, SUMMARY_FIELDNAMES, calc_batch_statistics, ensure_output_dirs, export_batch_statistics, export_results, export_spatial_summary, export_summary, ) # --------------------------------------------------------------------------- # ensure_output_dirs テスト # --------------------------------------------------------------------------- class TestEnsureOutputDirs: """ensure_output_dirs 関数のテスト群.""" def test_creates_figures_directory(self, tmp_path: Path) -> None: """正常: figures/ サブディレクトリが作成されること.""" ensure_output_dirs(str(tmp_path)) assert (tmp_path / "figures").is_dir() def test_creates_results_directory(self, tmp_path: Path) -> None: """正常: results/ サブディレクトリが作成されること.""" ensure_output_dirs(str(tmp_path)) assert (tmp_path / "results").is_dir() def test_idempotent_when_dirs_already_exist(self, tmp_path: Path) -> None: """正常: 既にディレクトリが存在する場合でもエラーにならないこと.""" ensure_output_dirs(str(tmp_path)) # 2 回目の呼び出しでも例外が発生しないこと ensure_output_dirs(str(tmp_path)) assert (tmp_path / "figures").is_dir() assert (tmp_path / "results").is_dir() def test_creates_nested_base_directory(self, tmp_path: Path) -> None: """正常: base_path が存在しない場合でも自動作成されること.""" nested_base = tmp_path / "new_output" ensure_output_dirs(str(nested_base)) assert (nested_base / "figures").is_dir() assert (nested_base / "results").is_dir() # --------------------------------------------------------------------------- # export_results テスト # --------------------------------------------------------------------------- class TestExportResults: """export_results 関数のテスト群.""" @pytest.fixture def sample_results(self) -> dict: """テスト用の均一性指標辞書を返す fixture.""" return { "cov": 0.05, "std": 10.0, "max_min_ratio": 1.2, "mean": 200.0, "max": 220.0, "min": 180.0, } def test_csv_file_is_created(self, sample_results: dict, tmp_path: Path) -> None: """正常: CSV ファイルが生成されること.""" output_path = str(tmp_path / "results.csv") export_results(sample_results, output_path) assert Path(output_path).exists() def test_csv_has_correct_header(self, sample_results: dict, tmp_path: Path) -> None: """正常: CSV の 1 行目にヘッダーが含まれること.""" output_path = str(tmp_path / "results.csv") export_results(sample_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) fieldnames = reader.fieldnames assert set(fieldnames) == set(sample_results.keys()) def test_csv_has_correct_values(self, sample_results: dict, tmp_path: Path) -> None: """正常: CSV の値が正しく書き込まれていること.""" output_path = str(tmp_path / "results.csv") export_results(sample_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) assert len(rows) == 1 row = rows[0] assert float(row["cov"]) == pytest.approx(0.05) assert float(row["std"]) == pytest.approx(10.0) assert float(row["mean"]) == pytest.approx(200.0) assert float(row["max"]) == pytest.approx(220.0) assert float(row["min"]) == pytest.approx(180.0) def test_creates_parent_directory_automatically( self, sample_results: dict, tmp_path: Path ) -> None: """正常: 出力ディレクトリが存在しない場合に自動作成されること.""" nested_path = str(tmp_path / "results" / "uniformity.csv") export_results(sample_results, nested_path) assert Path(nested_path).exists() # --------------------------------------------------------------------------- # export_summary テスト # --------------------------------------------------------------------------- class TestExportSummary: """export_summary 関数のテスト群.""" @pytest.fixture def sample_all_results(self) -> list[dict]: """テスト用の複数画像の均一性指標リストを返す fixture.""" return [ { "image_name": "image_001", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, }, { "image_name": "image_002", "mean": 150.0, "std": 20.0, "cov": 0.133, "max_min_ratio": 1.5, "max": 200.0, "min": 100.0, }, ] def test_csv_file_is_created( self, sample_all_results: list[dict], tmp_path: Path ) -> None: """正常: CSV ファイルが生成されること.""" output_path = str(tmp_path / "summary.csv") export_summary(sample_all_results, output_path) assert Path(output_path).exists() def test_csv_has_correct_summary_fieldnames( self, sample_all_results: list[dict], tmp_path: Path ) -> None: """正常: CSV のヘッダーが SUMMARY_FIELDNAMES と一致すること.""" output_path = str(tmp_path / "summary.csv") export_summary(sample_all_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) fieldnames = reader.fieldnames assert list(fieldnames) == SUMMARY_FIELDNAMES def test_csv_has_correct_row_count( self, sample_all_results: list[dict], tmp_path: Path ) -> None: """正常: CSV の行数が入力リストのサイズと一致すること.""" output_path = str(tmp_path / "summary.csv") export_summary(sample_all_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) assert len(rows) == len(sample_all_results) def test_csv_first_row_values_are_correct( self, sample_all_results: list[dict], tmp_path: Path ) -> None: """正常: CSV の 1 行目の値が正しいこと.""" output_path = str(tmp_path / "summary.csv") export_summary(sample_all_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) first = rows[0] assert first["image_name"] == "image_001" assert float(first["mean"]) == pytest.approx(200.0) assert float(first["std"]) == pytest.approx(10.0) def test_csv_second_row_values_are_correct( self, sample_all_results: list[dict], tmp_path: Path ) -> None: """正常: CSV の 2 行目の値が正しいこと.""" output_path = str(tmp_path / "summary.csv") export_summary(sample_all_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) second = rows[1] assert second["image_name"] == "image_002" assert float(second["mean"]) == pytest.approx(150.0) def test_summary_fieldnames_constant_has_correct_keys(self) -> None: """正常: SUMMARY_FIELDNAMES 定数が期待されるキーを持つこと.""" expected = ["image_name", "mean", "std", "cov", "max_min_ratio", "max", "min"] assert SUMMARY_FIELDNAMES == expected def test_creates_parent_directory_automatically( self, sample_all_results: list[dict], tmp_path: Path ) -> None: """正常: 出力ディレクトリが存在しない場合に自動作成されること.""" nested_path = str(tmp_path / "results" / "summary.csv") export_summary(sample_all_results, nested_path) assert Path(nested_path).exists() def test_empty_list_creates_header_only_csv(self, tmp_path: Path) -> None: """エッジケース: 空リストを渡した場合にヘッダーのみの CSV が生成されること.""" output_path = str(tmp_path / "empty_summary.csv") export_summary([], output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) assert len(rows) == 0 assert list(reader.fieldnames) == SUMMARY_FIELDNAMES # --------------------------------------------------------------------------- # export_spatial_summary テスト # --------------------------------------------------------------------------- class TestExportSpatialSummary: """export_spatial_summary 関数のテスト群.""" @pytest.fixture def sample_spatial_results(self) -> list[dict]: """テスト用の空間解析結果リストを返す fixture.""" return [ { "image_name": "image_001", "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.29, "gradient_magnitude": 22.2, }, { "image_name": "image_002", "center_mean": 200.0, "middle_mean": 190.0, "periphery_mean": 170.0, "center_periphery_ratio": 1.18, "gradient_magnitude": 15.0, }, ] def test_csv_file_is_created( self, sample_spatial_results: list[dict], tmp_path: Path ) -> None: """正常: CSV ファイルが生成されること.""" output_path = str(tmp_path / "spatial_summary.csv") export_spatial_summary(sample_spatial_results, output_path) assert Path(output_path).exists() def test_csv_has_correct_spatial_fieldnames( self, sample_spatial_results: list[dict], tmp_path: Path ) -> None: """正常: CSV のヘッダーが SPATIAL_FIELDNAMES と一致すること.""" output_path = str(tmp_path / "spatial_summary.csv") export_spatial_summary(sample_spatial_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) fieldnames = reader.fieldnames assert list(fieldnames) == SPATIAL_FIELDNAMES def test_csv_has_correct_row_count( self, sample_spatial_results: list[dict], tmp_path: Path ) -> None: """正常: CSV の行数が入力リストのサイズと一致すること.""" output_path = str(tmp_path / "spatial_summary.csv") export_spatial_summary(sample_spatial_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) assert len(rows) == len(sample_spatial_results) def test_csv_first_row_values_are_correct( self, sample_spatial_results: list[dict], tmp_path: Path ) -> None: """正常: CSV の 1 行目の値が正しいこと.""" output_path = str(tmp_path / "spatial_summary.csv") export_spatial_summary(sample_spatial_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) first = rows[0] assert first["image_name"] == "image_001" assert float(first["center_mean"]) == pytest.approx(180.0) assert float(first["periphery_mean"]) == pytest.approx(140.0) assert float(first["center_periphery_ratio"]) == pytest.approx(1.29) def test_csv_second_row_values_are_correct( self, sample_spatial_results: list[dict], tmp_path: Path ) -> None: """正常: CSV の 2 行目の値が正しいこと.""" output_path = str(tmp_path / "spatial_summary.csv") export_spatial_summary(sample_spatial_results, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) second = rows[1] assert second["image_name"] == "image_002" assert float(second["gradient_magnitude"]) == pytest.approx(15.0) def test_spatial_fieldnames_constant_has_correct_keys(self) -> None: """正常: SPATIAL_FIELDNAMES 定数が期待されるキーを持つこと.""" expected = [ "image_name", "center_mean", "middle_mean", "periphery_mean", "center_periphery_ratio", "gradient_magnitude", ] assert SPATIAL_FIELDNAMES == expected def test_creates_parent_directory_automatically( self, sample_spatial_results: list[dict], tmp_path: Path ) -> None: """正常: 出力ディレクトリが存在しない場合に自動作成されること.""" nested_path = str(tmp_path / "results" / "spatial_summary.csv") export_spatial_summary(sample_spatial_results, nested_path) assert Path(nested_path).exists() def test_empty_list_creates_header_only_csv(self, tmp_path: Path) -> None: """エッジケース: 空リストを渡した場合にヘッダーのみの CSV が生成されること.""" output_path = str(tmp_path / "empty_spatial.csv") export_spatial_summary([], output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) assert len(rows) == 0 assert list(reader.fieldnames) == SPATIAL_FIELDNAMES def test_extra_keys_in_dict_are_ignored(self, tmp_path: Path) -> None: """正常: 余分なキーを含む辞書を渡しても CSV には SPATIAL_FIELDNAMES のみ出力されること.""" results_with_extra = [ { "image_name": "image_001", "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.29, "gradient_magnitude": 22.2, "extra_key": "should_be_ignored", } ] output_path = str(tmp_path / "spatial_summary.csv") export_spatial_summary(results_with_extra, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) fieldnames = reader.fieldnames assert list(fieldnames) == SPATIAL_FIELDNAMES # --------------------------------------------------------------------------- # calc_batch_statistics テスト # --------------------------------------------------------------------------- class TestCalcBatchStatistics: """calc_batch_statistics 関数のテスト群. 仕様 (SPEC_02 バッチ解析 / 実装サマリー): - 入力: 均一性指標と空間解析結果を含む辞書リスト - 出力: {"uniformity": {...}, "spatial": {...}} の形式 - uniformity 対象指標: mean, std, cov, max_min_ratio - spatial 対象指標: center_mean, middle_mean, periphery_mean, center_periphery_ratio, gradient_magnitude - numpy 母集団標準偏差（ddof=0）を使用 - cv = std / mean """ @pytest.fixture def two_image_results(self) -> list[dict]: """テスト用の 2 画像分の解析結果リストを返す fixture.""" return [ { "image_name": "image_001", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.286, "gradient_magnitude": 22.2, }, { "image_name": "image_002", "mean": 210.0, "std": 20.0, "cov": 0.095, "max_min_ratio": 1.4, "max": 240.0, "min": 160.0, "center_mean": 200.0, "middle_mean": 185.0, "periphery_mean": 160.0, "center_periphery_ratio": 1.25, "gradient_magnitude": 20.0, }, ] def test_returns_dict_with_uniformity_and_spatial_keys( self, two_image_results: list[dict] ) -> None: """正常: 戻り値が "uniformity" と "spatial" キーを持つ辞書であること.""" result = calc_batch_statistics(two_image_results) assert isinstance(result, dict) assert "uniformity" in result assert "spatial" in result def test_uniformity_contains_exactly_four_metrics( self, two_image_results: list[dict] ) -> None: """正常: uniformity 部に mean, std, cov, max_min_ratio の 4 指標のみが含まれること.""" result = calc_batch_statistics(two_image_results) expected_metrics = {"mean", "std", "cov", "max_min_ratio"} assert expected_metrics == set(result["uniformity"].keys()) def test_spatial_contains_exactly_five_metrics( self, two_image_results: list[dict] ) -> None: """正常: spatial 部に 5 指標のみが含まれること.""" result = calc_batch_statistics(two_image_results) expected_metrics = { "center_mean", "middle_mean", "periphery_mean", "center_periphery_ratio", "gradient_magnitude", } assert expected_metrics == set(result["spatial"].keys()) def test_each_metric_has_five_stat_subkeys( self, two_image_results: list[dict] ) -> None: """正常: 各指標が mean, std, min, max, cv の 5 つのサブキーを持つこと.""" result = calc_batch_statistics(two_image_results) expected_subkeys = {"mean", "std", "min", "max", "cv"} for category in ("uniformity", "spatial"): for metric_name, metric_values in result[category].items(): assert expected_subkeys == set(metric_values.keys()), ( f"{category}.{metric_name} のキーが期待と異なる" ) def test_uniformity_mean_mean_is_arithmetic_mean( self, two_image_results: list[dict] ) -> None: """正常: uniformity.mean.mean が算術平均と一致すること. (200.0 + 210.0) / 2 = 205.0 """ result = calc_batch_statistics(two_image_results) assert result["uniformity"]["mean"]["mean"] == pytest.approx(205.0) def test_uniformity_mean_std_uses_population_std_ddof0( self, two_image_results: list[dict] ) -> None: """正常: uniformity.mean.std が母集団標準偏差（ddof=0）で算出されること. values = [200.0, 210.0] std(ddof=0) = sqrt(((200-205)^2 + (210-205)^2) / 2) = 5.0 """ result = calc_batch_statistics(two_image_results) assert result["uniformity"]["mean"]["std"] == pytest.approx(5.0) def test_uniformity_mean_std_differs_from_sample_std( self, two_image_results: list[dict] ) -> None: """正常: uniformity.mean.std がサンプル標準偏差（ddof=1）と異なること. ddof=0 (母集団): 5.0 ddof=1 (標本): 7.07... (2 画像の場合) """ result = calc_batch_statistics(two_image_results) sample_std = float(np.std([200.0, 210.0], ddof=1)) # ddof=0 の結果はサンプル標準偏差と一致しないはず assert result["uniformity"]["mean"]["std"] != pytest.approx(sample_std) def test_uniformity_mean_min_is_minimum_value( self, two_image_results: list[dict] ) -> None: """正常: uniformity.mean.min が最小値と一致すること.""" result = calc_batch_statistics(two_image_results) assert result["uniformity"]["mean"]["min"] == pytest.approx(200.0) def test_uniformity_mean_max_is_maximum_value( self, two_image_results: list[dict] ) -> None: """正常: uniformity.mean.max が最大値と一致すること.""" result = calc_batch_statistics(two_image_results) assert result["uniformity"]["mean"]["max"] == pytest.approx(210.0) def test_uniformity_mean_cv_is_std_divided_by_mean( self, two_image_results: list[dict] ) -> None: """正常: uniformity.mean.cv が std / mean と一致すること. std=5.0, mean=205.0 -> cv = 5.0 / 205.0 """ result = calc_batch_statistics(two_image_results) expected_cv = 5.0 / 205.0 assert result["uniformity"]["mean"]["cv"] == pytest.approx(expected_cv) def test_uniformity_cov_mean_is_correct( self, two_image_results: list[dict] ) -> None: """正常: uniformity.cov.mean が (0.05 + 0.095) / 2 と一致すること.""" result = calc_batch_statistics(two_image_results) expected = (0.05 + 0.095) / 2 assert result["uniformity"]["cov"]["mean"] == pytest.approx(expected) def test_uniformity_max_min_ratio_min_is_correct( self, two_image_results: list[dict] ) -> None: """正常: uniformity.max_min_ratio.min が 1.2 と一致すること.""" result = calc_batch_statistics(two_image_results) assert result["uniformity"]["max_min_ratio"]["min"] == pytest.approx(1.2) def test_uniformity_max_min_ratio_max_is_correct( self, two_image_results: list[dict] ) -> None: """正常: uniformity.max_min_ratio.max が 1.4 と一致すること.""" result = calc_batch_statistics(two_image_results) assert result["uniformity"]["max_min_ratio"]["max"] == pytest.approx(1.4) def test_spatial_center_mean_mean_is_correct( self, two_image_results: list[dict] ) -> None: """正常: spatial.center_mean.mean が (180.0 + 200.0) / 2 = 190.0 と一致すること.""" result = calc_batch_statistics(two_image_results) assert result["spatial"]["center_mean"]["mean"] == pytest.approx(190.0) def test_spatial_gradient_magnitude_std_is_correct( self, two_image_results: list[dict] ) -> None: """正常: spatial.gradient_magnitude.std が母集団標準偏差と一致すること.""" result = calc_batch_statistics(two_image_results) expected_std = float(np.std([22.2, 20.0], ddof=0)) assert result["spatial"]["gradient_magnitude"]["std"] == pytest.approx(expected_std) def test_spatial_gradient_magnitude_cv_is_std_over_mean( self, two_image_results: list[dict] ) -> None: """正常: spatial.gradient_magnitude.cv が std / mean と一致すること.""" result = calc_batch_statistics(two_image_results) values = [22.2, 20.0] expected_mean = float(np.mean(values)) expected_std = float(np.std(values, ddof=0)) expected_cv = expected_std / expected_mean assert result["spatial"]["gradient_magnitude"]["cv"] == pytest.approx(expected_cv) def test_all_stat_values_are_float_type(self, two_image_results: list[dict]) -> None: """正常: 全ての統計値が float 型であること.""" result = calc_batch_statistics(two_image_results) for category in ("uniformity", "spatial"): for metric_name, metric_values in result[category].items(): for stat_name, stat_value in metric_values.items(): assert isinstance(stat_value, float), ( f"{category}.{metric_name}.{stat_name} が float でない: {type(stat_value)}" ) def test_single_image_std_is_zero(self) -> None: """エッジケース: 画像が 1 枚の場合 std は 0.0 となること（母集団標準偏差 ddof=0）.""" single = [ { "image_name": "image_001", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.286, "gradient_magnitude": 22.2, } ] result = calc_batch_statistics(single) assert result["uniformity"]["mean"]["std"] == pytest.approx(0.0) def test_single_image_cv_is_zero_when_std_is_zero(self) -> None: """エッジケース: 画像が 1 枚の場合 cv は 0.0 となること（std=0 の場合）.""" single = [ { "image_name": "image_001", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.286, "gradient_magnitude": 22.2, } ] result = calc_batch_statistics(single) assert result["uniformity"]["mean"]["cv"] == pytest.approx(0.0) def test_single_image_min_equals_max(self) -> None: """エッジケース: 画像が 1 枚の場合 min と max は同一値となること.""" single = [ { "image_name": "image_001", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.286, "gradient_magnitude": 22.2, } ] result = calc_batch_statistics(single) assert result["uniformity"]["mean"]["min"] == pytest.approx(200.0) assert result["uniformity"]["mean"]["max"] == pytest.approx(200.0) def test_three_images_population_std_is_correct(self) -> None: """正常: 3 画像での std が母集団標準偏差（ddof=0）で正しく算出されること. values = [100, 200, 300] mean = 200, std(ddof=0) = sqrt(((100-200)^2 + (200-200)^2 + (300-200)^2) / 3) = sqrt(20000/3) = sqrt(6666.67) ≈ 81.65 """ three_images = [ { "image_name": f"image_00{i}", "mean": m, "std": 5.0, "cov": 0.02, "max_min_ratio": 1.1, "max": m + 10, "min": m - 10, "center_mean": m, "middle_mean": m - 5.0, "periphery_mean": m - 10.0, "center_periphery_ratio": 1.1, "gradient_magnitude": 5.0, } for i, m in enumerate([100.0, 200.0, 300.0]) ] result = calc_batch_statistics(three_images) expected_std = float(np.std([100.0, 200.0, 300.0], ddof=0)) assert result["uniformity"]["mean"]["std"] == pytest.approx(expected_std) def test_uniformly_same_values_cv_is_zero(self) -> None: """エッジケース: 全画像で指標値が同一の場合 cv は 0.0 となること.""" identical = [ { "image_name": f"image_00{i}", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.286, "gradient_magnitude": 22.2, } for i in range(3) ] result = calc_batch_statistics(identical) assert result["uniformity"]["mean"]["cv"] == pytest.approx(0.0) assert result["spatial"]["center_mean"]["cv"] == pytest.approx(0.0) # --------------------------------------------------------------------------- # export_batch_statistics テスト # --------------------------------------------------------------------------- class TestExportBatchStatistics: """export_batch_statistics 関数のテスト群. 仕様 (実装サマリー): - 行: 各指標名，列: statistic, mean, std, min, max, cv - uniformity と spatial の全指標を 1 ファイルにまとめる """ @pytest.fixture def sample_stats(self) -> dict: """テスト用の集計統計辞書を返す fixture.""" return { "uniformity": { "mean": {"mean": 205.0, "std": 5.0, "min": 200.0, "max": 210.0, "cv": 0.0244}, "std": {"mean": 15.0, "std": 5.0, "min": 10.0, "max": 20.0, "cv": 0.333}, "cov": {"mean": 0.0725, "std": 0.0225, "min": 0.05, "max": 0.095, "cv": 0.310}, "max_min_ratio": {"mean": 1.3, "std": 0.1, "min": 1.2, "max": 1.4, "cv": 0.077}, }, "spatial": { "center_mean": {"mean": 190.0, "std": 10.0, "min": 180.0, "max": 200.0, "cv": 0.053}, "middle_mean": {"mean": 175.0, "std": 10.0, "min": 165.0, "max": 185.0, "cv": 0.057}, "periphery_mean": {"mean": 150.0, "std": 10.0, "min": 140.0, "max": 160.0, "cv": 0.067}, "center_periphery_ratio": { "mean": 1.268, "std": 0.018, "min": 1.25, "max": 1.286, "cv": 0.014 }, "gradient_magnitude": {"mean": 21.1, "std": 1.1, "min": 20.0, "max": 22.2, "cv": 0.052}, }, } def test_csv_file_is_created(self, sample_stats: dict, tmp_path: Path) -> None: """正常: CSV ファイルが生成されること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) assert Path(output_path).exists() def test_csv_has_correct_header(self, sample_stats: dict, tmp_path: Path) -> None: """正常: CSV のヘッダーが statistic, mean, std, min, max, cv であること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) fieldnames = reader.fieldnames assert list(fieldnames) == ["statistic", "mean", "std", "min", "max", "cv"] def test_csv_row_count_equals_uniformity_plus_spatial( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: CSV の行数が uniformity 指標数 + spatial 指標数と一致すること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) expected_count = len(sample_stats["uniformity"]) + len(sample_stats["spatial"]) assert len(rows) == expected_count def test_csv_statistic_column_contains_all_metric_names( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: statistic 列に全指標名（uniformity + spatial）が含まれること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) statistic_names = [row["statistic"] for row in reader] all_expected_metrics = list(sample_stats["uniformity"].keys()) + list( sample_stats["spatial"].keys() ) assert set(statistic_names) == set(all_expected_metrics) def test_uniformity_mean_row_values_are_correct( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: uniformity の mean 指標行の全列値が正しく書き込まれていること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = {row["statistic"]: row for row in reader} mean_row = rows["mean"] assert float(mean_row["mean"]) == pytest.approx(205.0) assert float(mean_row["std"]) == pytest.approx(5.0) assert float(mean_row["min"]) == pytest.approx(200.0) assert float(mean_row["max"]) == pytest.approx(210.0) assert float(mean_row["cv"]) == pytest.approx(0.0244) def test_spatial_center_mean_row_values_are_correct( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: spatial の center_mean 指標行の全列値が正しく書き込まれていること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = {row["statistic"]: row for row in reader} cm_row = rows["center_mean"] assert float(cm_row["mean"]) == pytest.approx(190.0) assert float(cm_row["std"]) == pytest.approx(10.0) assert float(cm_row["min"]) == pytest.approx(180.0) assert float(cm_row["max"]) == pytest.approx(200.0) assert float(cm_row["cv"]) == pytest.approx(0.053) def test_spatial_gradient_magnitude_row_values_are_correct( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: spatial の gradient_magnitude 指標行の全列値が正しく書き込まれていること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = {row["statistic"]: row for row in reader} grad_row = rows["gradient_magnitude"] assert float(grad_row["mean"]) == pytest.approx(21.1) assert float(grad_row["std"]) == pytest.approx(1.1) assert float(grad_row["min"]) == pytest.approx(20.0) assert float(grad_row["max"]) == pytest.approx(22.2) assert float(grad_row["cv"]) == pytest.approx(0.052) def test_creates_parent_directory_automatically( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: 出力ディレクトリが存在しない場合に自動作成されること.""" nested_path = str(tmp_path / "results" / "summary_statistics.csv") export_batch_statistics(sample_stats, nested_path) assert Path(nested_path).exists() def test_uniformity_metrics_appear_before_spatial_metrics( self, sample_stats: dict, tmp_path: Path ) -> None: """正常: CSV の先頭 4 行が uniformity 指標，後続 5 行が spatial 指標であること.""" output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(sample_stats, output_path) with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) uniformity_count = len(sample_stats["uniformity"]) uniformity_names = set(sample_stats["uniformity"].keys()) for row in rows[:uniformity_count]: assert row["statistic"] in uniformity_names, ( f"先頭 {uniformity_count} 行に uniformity 以外の指標が含まれている: {row['statistic']}" ) spatial_names = set(sample_stats["spatial"].keys()) for row in rows[uniformity_count:]: assert row["statistic"] in spatial_names, ( f"後続行に spatial 以外の指標が含まれている: {row['statistic']}" ) def test_integration_calc_then_export(self, tmp_path: Path) -> None: """統合: calc_batch_statistics の結果を export_batch_statistics で出力できること.""" all_results = [ { "image_name": "image_001", "mean": 200.0, "std": 10.0, "cov": 0.05, "max_min_ratio": 1.2, "max": 220.0, "min": 180.0, "center_mean": 180.0, "middle_mean": 165.0, "periphery_mean": 140.0, "center_periphery_ratio": 1.286, "gradient_magnitude": 22.2, }, { "image_name": "image_002", "mean": 210.0, "std": 20.0, "cov": 0.095, "max_min_ratio": 1.4, "max": 240.0, "min": 160.0, "center_mean": 200.0, "middle_mean": 185.0, "periphery_mean": 160.0, "center_periphery_ratio": 1.25, "gradient_magnitude": 20.0, }, ] stats = calc_batch_statistics(all_results) output_path = str(tmp_path / "summary_statistics.csv") export_batch_statistics(stats, output_path) assert Path(output_path).exists() with open(output_path, encoding="utf-8") as f: reader = csv.DictReader(f) rows = list(reader) assert len(rows) == 9 # uniformity 4 + spatial 5 statistic_names = {row["statistic"] for row in rows} assert "mean" in statistic_names assert "cov" in statistic_names assert "center_mean" in statistic_names assert "gradient_magnitude" in statistic_names